The growing demand of DG (diesel electric generators) has led to air pollution and green house ga... more The growing demand of DG (diesel electric generators) has led to air pollution and green house gas emissions, especially CO 2 (Carbon-dioxide). Hence, it is necessary to predict the level of CO 2 released from the DG, to ensure the minimum level of emission. Forecasting the CO/CO 2 ratio, flue gas temperature (T F) and gross efficiency (h), ensures the effective and smooth operations of DGs. Keeping this in view, in this paper, ANN (artificial neural network) models are proposed for the prediction of CO 2, CO/CO 2 ratio, T F and (h) of DG. The training and testing data required to develop the ANN were obtained through a number of experiments in 3 phase, 415 V, DG of different capacities operated at different loads, speed and torques. Three different capacities of DGs such as 180, 250, and 380 kVA have been investigated. Back propagation algorithm was used for training the ANN. The application of the newly developed models shows better results in terms of accuracy and percentage error. The coefficient of multiple determination values are found to be above 0.99 for all the models. It is evident that the ANN models are reliable tools for the prediction of the performance and exhaust emissions of DGs.
DNA microarray technology can monitor the expression levels of thousands of genes simultaneously ... more DNA microarray technology can monitor the expression levels of thousands of genes simultaneously during important biological processes and across collections of related samples. Knowledge gained through microarray data analysis is increasingly important as they are useful for phenotype classification of diseases. This paper presents an effective method for gene classification using Support Vector Machine (SVM). SVM is a supervised learning algorithm capable of solving complex classification problems. Mutual information (MI) between the genes and the class label is used for identifying the informative genes. The selected genes are utilized for training the SVM classifier and the testing ability is evaluated using Leave-one-Out Cross Validation (LOOCV) method. The performance of the proposed approach is evaluated using two cancer microarray datasets. From the simulation study it is observed that the proposed approach reduces the dimension of the input features by identifying the most informative gene subset and improve classification accuracy when compared to other approaches.
This paper presents the modelling and simulation of Dynamic Voltage Restorer (DVR)
for mitigation... more This paper presents the modelling and simulation of Dynamic Voltage Restorer (DVR) for mitigation of voltage sags and swells which are the major problems and issues on non linear loads.The Dynamic Voltage Restore (DVR) has become popular as a cost effective solutions for the protection of sensitive loads from voltage sags and swells.. The control of compensation voltages in DVR based on a-b-c to d-q-0 algorithm is discussed. The proposed control technique is cost effective and simple to design. Computer simulations are carried out in a suitable test system to investigate the effectiveness of control technique by using MATLAB/SIMULINK software.
Mammography is the most effective procedure
for the early detection of breast diseases. Mammogram... more Mammography is the most effective procedure for the early detection of breast diseases. Mammogram analysis refers the processing of mammograms with the goal of finding abnormality presented in the mammogram. In this paper, various steps involved in the recognition of breast tumours include image segmentation, feature extraction and classification. Back propagation Neural Network based identification along with wavelet based adaptive windowing technique can be done for detecting tumours in digital mammograms. The algorithm is validated with mammograms in Mammographic Image Analysis Society Mini Mammographic database which shows that the proposed technique is capable of detecting tumours of very different sizes.
Mammography is the most effective procedure for
the early detection of breast diseases. Mammogram... more Mammography is the most effective procedure for the early detection of breast diseases. Mammogram analysis refers the processing of mammograms with the goal of finding abnormality presented in the mammogram. In this paper, the tumour can be detected by using wavelet based adaptive windowing technique. Coarse segmentation is the first step which can be done by using wavelet based histogram thresholding where, the thereshold value is chosen by performing 1-D wavelet based analysis of PDFs of wavelet transformed images at different channels. Fine segmentation can be done by partitioning the image into fixed number of large and small windows. By calculating the mean, maximum and minimum pixel values for the windows a threshold value has been obtained. Depending upon the threshold values the suspicious areas have been segmented. Intensity adjustment is applied as a preprocessing step to improve the quality of an image before applying the proposed technique. The algorithm is validated with mammograms in Mammographic Image Analysis Society Mini Mammographic database which shows that the proposed technique is capable of detecting lesions of very different sizes.
Photovoltaic inverters are important solar energy application. This paper presents a novel
Fuzzy ... more Photovoltaic inverters are important solar energy application. This paper presents a novel Fuzzy Adaptive Hysteresis Current Controller to control the inverter, used in the non-linear timevariant solar photovoltaic cell. The proposed controller has the advantages of both fuzzy as well as adaptive controller. It is capable of reducing the total harmonic distortion and to provide acceptable switching frequency. The mathematical model of Photovoltaic array is developed using the Newton’s method using the parameter obtained from a commercial photovoltaic data sheet under variable weather conditions, in which the effect of irradiance and temperature are considered. The modeled Photovoltaic array is interfaced with DC-DC boost converter, AC-DC inverter and load. A DC-DC boost converter is used to step up the input DC voltage of the Photovoltaic array while the DC-AC single-phase inverter converts the input DC comes from boost converter into AC. The performance of the proposed controller of inverter is evaluated through MATLAB-Simulation. Unlike standard adaptive controller designs, this adaptive fuzzy controller does not require an explicit mathematical model of the system. The results obtained with the proposed algorithm are compared with those obtained when using conventional fixed hysteresis current controller for single-phase photovoltaic inverter in terms of THD and switching frequency
This paper presents a fuzzy logic based three phase four wire four-leg shunt active power filter ... more This paper presents a fuzzy logic based three phase four wire four-leg shunt active power filter to suppress harmonic currents. Modified instantaneous p-q theory is adopted for calculating the compensating current. Fuzzy-adaptive hysteresis band technique is applied for the current control to derive the switching signals for the voltage source inverter. A fuzzy logic controller is developed to control the voltage of the DC capacitor. Computer simulations are carried out on a sample power system to demonstrate the suitability of the pro-posed control strategy, for harmonic reduction under three different conditions namely, ideal, unbalance, unbalance and distorted source voltage conditions. The proposed control strategy is found to be effective to reduce the harmonics and compensate reactive power and neutral current and balance load currents under ideal and non-ideal source voltage conditions.
Knowledge gained through classification of microarray gene expression data is increasingly import... more Knowledge gained through classification of microarray gene expression data is increasingly important as they are useful for phenotype classification of diseases. Different from black box methods, fuzzy expert system can produce interpretable classifier with knowledge expressed in terms of if-then rules and membership function. This paper proposes a novel Genetic Swarm Algorithm (GSA) for obtaining near optimal rule set and membership function tuning. Advanced and problem specific genetic operators are proposed to improve the convergence of GSA and classification accuracy. The performance of the proposed approach is evaluated using six gene expression data sets. From the simulation study it is found that the proposed approach generated a compact fuzzy system with high classification accuracy for all the data sets when compared with other approaches.
A targeted energy analysis was carried out in a textile industry focusing on lighting
system. It ... more A targeted energy analysis was carried out in a textile industry focusing on lighting system. It is observed from the study that the industry can replace the existing TL-D 36 W/54 fluorescent tube lights with energy efficient TL-5 28 W/865 fluorescent tube lights with electronic ballast. This has resulted in energy saving of 206388 kWh per annum. The corresponding CO2 mitigation is estimated at 167.174 t CO2/annum. An experiment was conducted to identify the exact light intensity of the proposed lighting system. The result shows that the light intensity has reduced by 11.1 % which is insignificant compared to energy saving and CO2 mitigation. The investment for the new lighting fixtures is estimated at INR 0.468 million with cost saving of INR 1.65 million which has return on investment of just 3.3 months
The electric power supplied by a photovoltaic power
generation systems depends on the solar irrad... more The electric power supplied by a photovoltaic power generation systems depends on the solar irradiation and temperature. The PV system can supply the maximum power to the load at a particular operating point which is generally called as maximum power point (MPP), at which the entire PV system operates with maximum efficiency and produces its maximum power. Hence, a Maximum power point tracking (MPPT) methods are used to maximize the PV array output power by tracking continuously the maximum power point. The proposed MPPT controller is designed for 10kW solar PV system installed at Cape Institute of Technology. This paper presents the fuzzy logic based MPPT algorithm. A fuzzy logic based MPPT control technique is implemented to generate the optimal voltage from the photovoltaic system by modulating the duty cycle applied to the buck boost dc-dc converter. The proposed algorithm gives a good maximum power operation of the PV system. Simulation results obtained are presented and compared with the conventional P&O controller. Simulation results show the effectiveness of the proposed technique.
This paper describes the modelling and control of a pH neutralization process using a Local Linea... more This paper describes the modelling and control of a pH neutralization process using a Local Linear Model Tree (LOLIMOT) and an adaptive neuro–fuzzy inference system (ANFIS). The Direct and Inverse model building using LOLIMOT and ANFIS structures is described and compared. The direct and inverse models of the pH system are identified based on experimental data for the LOLIMOT and ANFIS structures. The identified models are implemented in the experimental pH system with IMC structure using a GUI developed in the MATLAB -SIMULINK platform. The main aim is to illustrate the online modelling and control of the experimental setup. The results of real-time control of an experimental pH process using the Internal Model Control (IMC) strategy are also presented.
Accuracy maximization and Complexity minimization are the two main goals of a fuzzy expert system... more Accuracy maximization and Complexity minimization are the two main goals of a fuzzy expert system based microarray data classification. Our previous Genetic Swarm Algorithm (GSA) approach has improved the classification accuracy of the fuzzy expert system at the cost of their interpretability. The if-then rules produced by the GSA are lengthy and complex which is difficult for the physician to understand. To address this interpretability-accuracy tradeoff, the rule set is represented using integer numbers and the task of rule generation is treated as a combinatorial optimization task. Ant Colony Optimization (ACO) with local and global pheromone updations are applied to find out the fuzzy partition based on the gene expression values for generating simpler rule set. In order to address the formless and continuous expression values of a gene, this paper employs Artificial Bee Colony (ABC) algorithm to evolve the points of membership function. Mutual Information is used for idenfication of informative genes. The performance of the proposed hybrid Ant Bee Algorithm (ABA) is evaluated using six gene expression data sets. From the simulation study, it is found that the proposed approach generated an accurate fuzzy system with highly interpretable and compact rules for all the data sets when compared with other approaches
Voltage stability is an important issue in the
planning and operation of deregulated power system... more Voltage stability is an important issue in the planning and operation of deregulated power systems. The voltage stability problems is a most challenging one for the system operators in deregulated power systems because of the intense use of transmission line capabilities and poor regulation in market environment. This article addresses the congestion management problem avoiding offline transmission capacity limits related to voltage stability by considering Voltage Security Constrained Optimal Power Flow (VSCOPF) problem in deregulated environment. This article presents the application of Multi Objective Differential Evolution (MODE) algorithm to solve the VSCOPF problem in new competitive power systems. The maximum of L-index of the load buses is taken as the indicator of voltage stability and is incorporated in the Optimal Power Flow (OPF) problem. The proposed method in hybrid power market which also gives solutions to voltage stability problems by considering the generation rescheduling cost and load shedding cost which relieves the congestion problem in deregulated environment. The buses for load shedding are selected based on the minimum eigen value of Jacobian with respect to the load shed. In the proposed approach, real power settings of generators in base case and contingency cases, generator bus voltage magnitudes, real and reactive power demands of selected load buses using sensitivity analysis are taken as the control variables and are represented as the combination of floating point numbers and integers. DE/randSF/1/bin strategy scheme of differential evolution with self-tuned parameter which employs binomial crossover and difference vector based mutation is used for the VSCOPF problem. A fuzzy based mechanism is employed to get the best compromise solution from the pareto front to aid the decision maker. The proposed VSCOPF planning model is implemented on IEEE 30-bus system, IEEE 57 bus practical system and IEEE 118 bus system. The pareto optimal front obtained from MODE is compared with reference pareto front and the best compromise solution for all the cases are obtained from fuzzy decision making strategy. The performance measures of proposed MODE in two test systems are calculated using suitable performance metrices. The simulation results show that the proposed approach provides considerable improvement in the congestion management by generation rescheduling and load shedding while enhancing the voltage stability in deregulated power system.
In solar PV (photovoltaic) system, tracking the module’s MPP (maximum power point) is challenging... more In solar PV (photovoltaic) system, tracking the module’s MPP (maximum power point) is challenging due to varying climatic conditions. Moreover, the tracking algorithm becomes more complicated under the condition of partial shading due to the presence of multiple peaks in the power voltage characteristics.This paper presents a NN (neural network) based modified IC (incremental conductance) algorithm for MPPT (maximum power point tracking) in PV system. The PV system along with the proposed MPPT algorithm was simulated using Matlab/Simulink simscape tool box. The simulated system was evaluated under uniform and non-uniform irradiation conditions and the results are presented. For comparison, P&O (perturb and observe) and Fuzzy based Modified Hill Climbing algorithms were used for MPP tracking, and the results show that the proposed approach is effective in tracking the MPP under partial shading conditions. To validate the simulated system hardware implementation of the proposed algorithm was carried out using FPGA (Field Programmable Gate Array).
Voltage stability has become an important issue in planning and operation of many power systems. ... more Voltage stability has become an important issue in planning and operation of many power systems. This work includes multi-objective evolutionary algorithm techniques such as Genetic Algorithm (GA) and Non-dominated Sorting Genetic Algorithm II (NSGA II) approach for solving Voltage Stability Constrained-Optimal Power Flow (VSC-OPF). Base case generator power output, voltage magnitude of generator buses are taken as the control variables and maximum L-index of load buses is used to specify the voltage stability level of the system. Multi-Objective OPF, formulated as a multi-objective mixed integer nonlinear optimization problem, minimizes fuel cost and minimizes emission of gases, as well as improvement of voltage profile in the system. NSGA-II based OPF-case 1-Two objective-Min Fuel cost and Voltage stability index; case 2-Three objective-Min Fuel cost, Min Emission cost and Voltage stability index. The above method is tested on standard IEEE 30-bus test system and simulation results are done for base case and the two severe contingency cases and also on loaded conditions.
Modeling and controlling of level process is one of the most common problems in the process indus... more Modeling and controlling of level process is one of the most common problems in the process industry. As the level process is nonlinear, Model Reference Adaptive Control (MRAC) strategy is employed in this paper. To design an MRAC with equally good transient and steady state performance is a challenging task. The main objective of this paper is to design an MRAC with very good steady-state and transient performance for a nonlinear process such as the hybrid tank process. A modification to the MRAC scheme is proposed in this study. Real-coded Genetic Algorithm (RGA) is used to tune off-line the controller parameters. Three different versions of MRAC and also a Proportional Integral Derivative (PID) controller are employed, and their performances are compared by using MATLAB. Input– output data of a coupled tank setup of the hybrid tank process are obtained by using Lab VIEW and a system identification procedure is carried out. The accuracy of the resultant model is further improved by parameter tuning using RGA. The simulation results shows that the proposed controller gives better transient performance than the well-designed PID controller or the MRAC does; while giving equally good steady-state performance. It is concluded that the proposed controllers can be used to achieve very good transient and steady state performance during the control of any nonlinear process.
The PID Controllers are commonly used in industries for nearly a century due to its
simplicity, e... more The PID Controllers are commonly used in industries for nearly a century due to its simplicity, efficiency and flexibility. Recently, the control of non-linear processes in the industries have turned the attention towards the intelligent controllers such as, Neural Networks, Fuzzy Logic Controller, Genetic Algorithm tuned Controllers, Adaptive Controller, etc. This paper focuses on the Investigation of Intelligent Controllers for conical tank level process. A conical tank is a highly nonlinear process due to the variation in the area of cross section of the level system with change in shape. In this work, Fuzzy Logic Controller is designed for the control of nonlinear process to ensure the exact level maintenance. The simulation results are obtained by Servo and Regulator operation of the nonlinear conical tank process. For this work, Fuzzy Logic Controller is compared with Conventional PI Controller.
Power system security enhancement is a major concern in the operation of power system. In this pa... more Power system security enhancement is a major concern in the operation of power system. In this paper, the task of security enhancement is formulated as a multi-objective optimization problem with minimization of fuel cost and minimization of FACTS device investment cost as objectives. Generator active power, generator bus voltage magnitude and the reactance of Thyristor Controlled Series Capacitors (TCSC) are taken as the decision variables. The probable locations of TCSC are pre-selected based on the values of Line Overload Sensitivity Index (LOSI) calculated for each branch in the system. Multi-objective genetic algorithm (MOGA) is applied to solve this security optimization problem. In the proposed GA, the decision variables are represented as floating point numbers in the GA population. The MOGA emphasize non-dominated solutions and simultaneously maintains diversity in the non-dominated solutions. A fuzzy set theory-based approach is employed to obtain the best compromise solution over the trade-off curve. The proposed approach has been evaluated on the IEEE 30-bus and IEEE 118-bus test systems. Simulation results show the effectiveness of the proposed approach for solving the multi-objective security enhancement problem.
This paper investigates about the enhancement in grid voltage stability while integrating the lar... more This paper investigates about the enhancement in grid voltage stability while integrating the large-scale variable speed wind turbine (VSWT) with direct drive synchronous generators (DDSG). A dynamic modeling and simulation of a grid connected VSWT driven DDSG with controllable power inverter strategies suitable for the study was developed, tested and verified. This dynamic model with its control scheme can regulate real power, maintain reactive power and generate voltage at different wind speeds. For this paper, studies were conducted on a standard IEEE 14 bus system augmented by a radially connected wind power plant (WPP) which contains 100 wind turbine generators (WTG). The studies include examining the voltage stability (λ-V) curves, voltage magnitude, reactive power delivered, loading margin and voltage collapse of the system. These voltage stability studies are done for the normal state as well as for line contingencies. It is found that large scale VSWT with DDSG at the transmission level has the potential to improve the long-term voltage stability of the grid by injecting reactive power with the help of controllable power inverter strategy
The growing demand of DG (diesel electric generators) has led to air pollution and green house ga... more The growing demand of DG (diesel electric generators) has led to air pollution and green house gas emissions, especially CO 2 (Carbon-dioxide). Hence, it is necessary to predict the level of CO 2 released from the DG, to ensure the minimum level of emission. Forecasting the CO/CO 2 ratio, flue gas temperature (T F) and gross efficiency (h), ensures the effective and smooth operations of DGs. Keeping this in view, in this paper, ANN (artificial neural network) models are proposed for the prediction of CO 2, CO/CO 2 ratio, T F and (h) of DG. The training and testing data required to develop the ANN were obtained through a number of experiments in 3 phase, 415 V, DG of different capacities operated at different loads, speed and torques. Three different capacities of DGs such as 180, 250, and 380 kVA have been investigated. Back propagation algorithm was used for training the ANN. The application of the newly developed models shows better results in terms of accuracy and percentage error. The coefficient of multiple determination values are found to be above 0.99 for all the models. It is evident that the ANN models are reliable tools for the prediction of the performance and exhaust emissions of DGs.
DNA microarray technology can monitor the expression levels of thousands of genes simultaneously ... more DNA microarray technology can monitor the expression levels of thousands of genes simultaneously during important biological processes and across collections of related samples. Knowledge gained through microarray data analysis is increasingly important as they are useful for phenotype classification of diseases. This paper presents an effective method for gene classification using Support Vector Machine (SVM). SVM is a supervised learning algorithm capable of solving complex classification problems. Mutual information (MI) between the genes and the class label is used for identifying the informative genes. The selected genes are utilized for training the SVM classifier and the testing ability is evaluated using Leave-one-Out Cross Validation (LOOCV) method. The performance of the proposed approach is evaluated using two cancer microarray datasets. From the simulation study it is observed that the proposed approach reduces the dimension of the input features by identifying the most informative gene subset and improve classification accuracy when compared to other approaches.
This paper presents the modelling and simulation of Dynamic Voltage Restorer (DVR)
for mitigation... more This paper presents the modelling and simulation of Dynamic Voltage Restorer (DVR) for mitigation of voltage sags and swells which are the major problems and issues on non linear loads.The Dynamic Voltage Restore (DVR) has become popular as a cost effective solutions for the protection of sensitive loads from voltage sags and swells.. The control of compensation voltages in DVR based on a-b-c to d-q-0 algorithm is discussed. The proposed control technique is cost effective and simple to design. Computer simulations are carried out in a suitable test system to investigate the effectiveness of control technique by using MATLAB/SIMULINK software.
Mammography is the most effective procedure
for the early detection of breast diseases. Mammogram... more Mammography is the most effective procedure for the early detection of breast diseases. Mammogram analysis refers the processing of mammograms with the goal of finding abnormality presented in the mammogram. In this paper, various steps involved in the recognition of breast tumours include image segmentation, feature extraction and classification. Back propagation Neural Network based identification along with wavelet based adaptive windowing technique can be done for detecting tumours in digital mammograms. The algorithm is validated with mammograms in Mammographic Image Analysis Society Mini Mammographic database which shows that the proposed technique is capable of detecting tumours of very different sizes.
Mammography is the most effective procedure for
the early detection of breast diseases. Mammogram... more Mammography is the most effective procedure for the early detection of breast diseases. Mammogram analysis refers the processing of mammograms with the goal of finding abnormality presented in the mammogram. In this paper, the tumour can be detected by using wavelet based adaptive windowing technique. Coarse segmentation is the first step which can be done by using wavelet based histogram thresholding where, the thereshold value is chosen by performing 1-D wavelet based analysis of PDFs of wavelet transformed images at different channels. Fine segmentation can be done by partitioning the image into fixed number of large and small windows. By calculating the mean, maximum and minimum pixel values for the windows a threshold value has been obtained. Depending upon the threshold values the suspicious areas have been segmented. Intensity adjustment is applied as a preprocessing step to improve the quality of an image before applying the proposed technique. The algorithm is validated with mammograms in Mammographic Image Analysis Society Mini Mammographic database which shows that the proposed technique is capable of detecting lesions of very different sizes.
Photovoltaic inverters are important solar energy application. This paper presents a novel
Fuzzy ... more Photovoltaic inverters are important solar energy application. This paper presents a novel Fuzzy Adaptive Hysteresis Current Controller to control the inverter, used in the non-linear timevariant solar photovoltaic cell. The proposed controller has the advantages of both fuzzy as well as adaptive controller. It is capable of reducing the total harmonic distortion and to provide acceptable switching frequency. The mathematical model of Photovoltaic array is developed using the Newton’s method using the parameter obtained from a commercial photovoltaic data sheet under variable weather conditions, in which the effect of irradiance and temperature are considered. The modeled Photovoltaic array is interfaced with DC-DC boost converter, AC-DC inverter and load. A DC-DC boost converter is used to step up the input DC voltage of the Photovoltaic array while the DC-AC single-phase inverter converts the input DC comes from boost converter into AC. The performance of the proposed controller of inverter is evaluated through MATLAB-Simulation. Unlike standard adaptive controller designs, this adaptive fuzzy controller does not require an explicit mathematical model of the system. The results obtained with the proposed algorithm are compared with those obtained when using conventional fixed hysteresis current controller for single-phase photovoltaic inverter in terms of THD and switching frequency
This paper presents a fuzzy logic based three phase four wire four-leg shunt active power filter ... more This paper presents a fuzzy logic based three phase four wire four-leg shunt active power filter to suppress harmonic currents. Modified instantaneous p-q theory is adopted for calculating the compensating current. Fuzzy-adaptive hysteresis band technique is applied for the current control to derive the switching signals for the voltage source inverter. A fuzzy logic controller is developed to control the voltage of the DC capacitor. Computer simulations are carried out on a sample power system to demonstrate the suitability of the pro-posed control strategy, for harmonic reduction under three different conditions namely, ideal, unbalance, unbalance and distorted source voltage conditions. The proposed control strategy is found to be effective to reduce the harmonics and compensate reactive power and neutral current and balance load currents under ideal and non-ideal source voltage conditions.
Knowledge gained through classification of microarray gene expression data is increasingly import... more Knowledge gained through classification of microarray gene expression data is increasingly important as they are useful for phenotype classification of diseases. Different from black box methods, fuzzy expert system can produce interpretable classifier with knowledge expressed in terms of if-then rules and membership function. This paper proposes a novel Genetic Swarm Algorithm (GSA) for obtaining near optimal rule set and membership function tuning. Advanced and problem specific genetic operators are proposed to improve the convergence of GSA and classification accuracy. The performance of the proposed approach is evaluated using six gene expression data sets. From the simulation study it is found that the proposed approach generated a compact fuzzy system with high classification accuracy for all the data sets when compared with other approaches.
A targeted energy analysis was carried out in a textile industry focusing on lighting
system. It ... more A targeted energy analysis was carried out in a textile industry focusing on lighting system. It is observed from the study that the industry can replace the existing TL-D 36 W/54 fluorescent tube lights with energy efficient TL-5 28 W/865 fluorescent tube lights with electronic ballast. This has resulted in energy saving of 206388 kWh per annum. The corresponding CO2 mitigation is estimated at 167.174 t CO2/annum. An experiment was conducted to identify the exact light intensity of the proposed lighting system. The result shows that the light intensity has reduced by 11.1 % which is insignificant compared to energy saving and CO2 mitigation. The investment for the new lighting fixtures is estimated at INR 0.468 million with cost saving of INR 1.65 million which has return on investment of just 3.3 months
The electric power supplied by a photovoltaic power
generation systems depends on the solar irrad... more The electric power supplied by a photovoltaic power generation systems depends on the solar irradiation and temperature. The PV system can supply the maximum power to the load at a particular operating point which is generally called as maximum power point (MPP), at which the entire PV system operates with maximum efficiency and produces its maximum power. Hence, a Maximum power point tracking (MPPT) methods are used to maximize the PV array output power by tracking continuously the maximum power point. The proposed MPPT controller is designed for 10kW solar PV system installed at Cape Institute of Technology. This paper presents the fuzzy logic based MPPT algorithm. A fuzzy logic based MPPT control technique is implemented to generate the optimal voltage from the photovoltaic system by modulating the duty cycle applied to the buck boost dc-dc converter. The proposed algorithm gives a good maximum power operation of the PV system. Simulation results obtained are presented and compared with the conventional P&O controller. Simulation results show the effectiveness of the proposed technique.
This paper describes the modelling and control of a pH neutralization process using a Local Linea... more This paper describes the modelling and control of a pH neutralization process using a Local Linear Model Tree (LOLIMOT) and an adaptive neuro–fuzzy inference system (ANFIS). The Direct and Inverse model building using LOLIMOT and ANFIS structures is described and compared. The direct and inverse models of the pH system are identified based on experimental data for the LOLIMOT and ANFIS structures. The identified models are implemented in the experimental pH system with IMC structure using a GUI developed in the MATLAB -SIMULINK platform. The main aim is to illustrate the online modelling and control of the experimental setup. The results of real-time control of an experimental pH process using the Internal Model Control (IMC) strategy are also presented.
Accuracy maximization and Complexity minimization are the two main goals of a fuzzy expert system... more Accuracy maximization and Complexity minimization are the two main goals of a fuzzy expert system based microarray data classification. Our previous Genetic Swarm Algorithm (GSA) approach has improved the classification accuracy of the fuzzy expert system at the cost of their interpretability. The if-then rules produced by the GSA are lengthy and complex which is difficult for the physician to understand. To address this interpretability-accuracy tradeoff, the rule set is represented using integer numbers and the task of rule generation is treated as a combinatorial optimization task. Ant Colony Optimization (ACO) with local and global pheromone updations are applied to find out the fuzzy partition based on the gene expression values for generating simpler rule set. In order to address the formless and continuous expression values of a gene, this paper employs Artificial Bee Colony (ABC) algorithm to evolve the points of membership function. Mutual Information is used for idenfication of informative genes. The performance of the proposed hybrid Ant Bee Algorithm (ABA) is evaluated using six gene expression data sets. From the simulation study, it is found that the proposed approach generated an accurate fuzzy system with highly interpretable and compact rules for all the data sets when compared with other approaches
Voltage stability is an important issue in the
planning and operation of deregulated power system... more Voltage stability is an important issue in the planning and operation of deregulated power systems. The voltage stability problems is a most challenging one for the system operators in deregulated power systems because of the intense use of transmission line capabilities and poor regulation in market environment. This article addresses the congestion management problem avoiding offline transmission capacity limits related to voltage stability by considering Voltage Security Constrained Optimal Power Flow (VSCOPF) problem in deregulated environment. This article presents the application of Multi Objective Differential Evolution (MODE) algorithm to solve the VSCOPF problem in new competitive power systems. The maximum of L-index of the load buses is taken as the indicator of voltage stability and is incorporated in the Optimal Power Flow (OPF) problem. The proposed method in hybrid power market which also gives solutions to voltage stability problems by considering the generation rescheduling cost and load shedding cost which relieves the congestion problem in deregulated environment. The buses for load shedding are selected based on the minimum eigen value of Jacobian with respect to the load shed. In the proposed approach, real power settings of generators in base case and contingency cases, generator bus voltage magnitudes, real and reactive power demands of selected load buses using sensitivity analysis are taken as the control variables and are represented as the combination of floating point numbers and integers. DE/randSF/1/bin strategy scheme of differential evolution with self-tuned parameter which employs binomial crossover and difference vector based mutation is used for the VSCOPF problem. A fuzzy based mechanism is employed to get the best compromise solution from the pareto front to aid the decision maker. The proposed VSCOPF planning model is implemented on IEEE 30-bus system, IEEE 57 bus practical system and IEEE 118 bus system. The pareto optimal front obtained from MODE is compared with reference pareto front and the best compromise solution for all the cases are obtained from fuzzy decision making strategy. The performance measures of proposed MODE in two test systems are calculated using suitable performance metrices. The simulation results show that the proposed approach provides considerable improvement in the congestion management by generation rescheduling and load shedding while enhancing the voltage stability in deregulated power system.
In solar PV (photovoltaic) system, tracking the module’s MPP (maximum power point) is challenging... more In solar PV (photovoltaic) system, tracking the module’s MPP (maximum power point) is challenging due to varying climatic conditions. Moreover, the tracking algorithm becomes more complicated under the condition of partial shading due to the presence of multiple peaks in the power voltage characteristics.This paper presents a NN (neural network) based modified IC (incremental conductance) algorithm for MPPT (maximum power point tracking) in PV system. The PV system along with the proposed MPPT algorithm was simulated using Matlab/Simulink simscape tool box. The simulated system was evaluated under uniform and non-uniform irradiation conditions and the results are presented. For comparison, P&O (perturb and observe) and Fuzzy based Modified Hill Climbing algorithms were used for MPP tracking, and the results show that the proposed approach is effective in tracking the MPP under partial shading conditions. To validate the simulated system hardware implementation of the proposed algorithm was carried out using FPGA (Field Programmable Gate Array).
Voltage stability has become an important issue in planning and operation of many power systems. ... more Voltage stability has become an important issue in planning and operation of many power systems. This work includes multi-objective evolutionary algorithm techniques such as Genetic Algorithm (GA) and Non-dominated Sorting Genetic Algorithm II (NSGA II) approach for solving Voltage Stability Constrained-Optimal Power Flow (VSC-OPF). Base case generator power output, voltage magnitude of generator buses are taken as the control variables and maximum L-index of load buses is used to specify the voltage stability level of the system. Multi-Objective OPF, formulated as a multi-objective mixed integer nonlinear optimization problem, minimizes fuel cost and minimizes emission of gases, as well as improvement of voltage profile in the system. NSGA-II based OPF-case 1-Two objective-Min Fuel cost and Voltage stability index; case 2-Three objective-Min Fuel cost, Min Emission cost and Voltage stability index. The above method is tested on standard IEEE 30-bus test system and simulation results are done for base case and the two severe contingency cases and also on loaded conditions.
Modeling and controlling of level process is one of the most common problems in the process indus... more Modeling and controlling of level process is one of the most common problems in the process industry. As the level process is nonlinear, Model Reference Adaptive Control (MRAC) strategy is employed in this paper. To design an MRAC with equally good transient and steady state performance is a challenging task. The main objective of this paper is to design an MRAC with very good steady-state and transient performance for a nonlinear process such as the hybrid tank process. A modification to the MRAC scheme is proposed in this study. Real-coded Genetic Algorithm (RGA) is used to tune off-line the controller parameters. Three different versions of MRAC and also a Proportional Integral Derivative (PID) controller are employed, and their performances are compared by using MATLAB. Input– output data of a coupled tank setup of the hybrid tank process are obtained by using Lab VIEW and a system identification procedure is carried out. The accuracy of the resultant model is further improved by parameter tuning using RGA. The simulation results shows that the proposed controller gives better transient performance than the well-designed PID controller or the MRAC does; while giving equally good steady-state performance. It is concluded that the proposed controllers can be used to achieve very good transient and steady state performance during the control of any nonlinear process.
The PID Controllers are commonly used in industries for nearly a century due to its
simplicity, e... more The PID Controllers are commonly used in industries for nearly a century due to its simplicity, efficiency and flexibility. Recently, the control of non-linear processes in the industries have turned the attention towards the intelligent controllers such as, Neural Networks, Fuzzy Logic Controller, Genetic Algorithm tuned Controllers, Adaptive Controller, etc. This paper focuses on the Investigation of Intelligent Controllers for conical tank level process. A conical tank is a highly nonlinear process due to the variation in the area of cross section of the level system with change in shape. In this work, Fuzzy Logic Controller is designed for the control of nonlinear process to ensure the exact level maintenance. The simulation results are obtained by Servo and Regulator operation of the nonlinear conical tank process. For this work, Fuzzy Logic Controller is compared with Conventional PI Controller.
Power system security enhancement is a major concern in the operation of power system. In this pa... more Power system security enhancement is a major concern in the operation of power system. In this paper, the task of security enhancement is formulated as a multi-objective optimization problem with minimization of fuel cost and minimization of FACTS device investment cost as objectives. Generator active power, generator bus voltage magnitude and the reactance of Thyristor Controlled Series Capacitors (TCSC) are taken as the decision variables. The probable locations of TCSC are pre-selected based on the values of Line Overload Sensitivity Index (LOSI) calculated for each branch in the system. Multi-objective genetic algorithm (MOGA) is applied to solve this security optimization problem. In the proposed GA, the decision variables are represented as floating point numbers in the GA population. The MOGA emphasize non-dominated solutions and simultaneously maintains diversity in the non-dominated solutions. A fuzzy set theory-based approach is employed to obtain the best compromise solution over the trade-off curve. The proposed approach has been evaluated on the IEEE 30-bus and IEEE 118-bus test systems. Simulation results show the effectiveness of the proposed approach for solving the multi-objective security enhancement problem.
This paper investigates about the enhancement in grid voltage stability while integrating the lar... more This paper investigates about the enhancement in grid voltage stability while integrating the large-scale variable speed wind turbine (VSWT) with direct drive synchronous generators (DDSG). A dynamic modeling and simulation of a grid connected VSWT driven DDSG with controllable power inverter strategies suitable for the study was developed, tested and verified. This dynamic model with its control scheme can regulate real power, maintain reactive power and generate voltage at different wind speeds. For this paper, studies were conducted on a standard IEEE 14 bus system augmented by a radially connected wind power plant (WPP) which contains 100 wind turbine generators (WTG). The studies include examining the voltage stability (λ-V) curves, voltage magnitude, reactive power delivered, loading margin and voltage collapse of the system. These voltage stability studies are done for the normal state as well as for line contingencies. It is found that large scale VSWT with DDSG at the transmission level has the potential to improve the long-term voltage stability of the grid by injecting reactive power with the help of controllable power inverter strategy
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Papers by D. Devaraj
for mitigation of voltage sags and swells which are the major problems and issues on non linear
loads.The Dynamic Voltage Restore (DVR) has become popular as a cost effective solutions for the
protection of sensitive loads from voltage sags and swells.. The control of compensation voltages in
DVR based on a-b-c to d-q-0 algorithm is discussed. The proposed control technique is cost
effective and simple to design. Computer simulations are carried out in a suitable test system to
investigate the effectiveness of control technique by using MATLAB/SIMULINK software.
for the early detection of breast diseases. Mammogram
analysis refers the processing of mammograms with the
goal of finding abnormality presented in the
mammogram. In this paper, various steps involved in
the recognition of breast tumours include image
segmentation, feature extraction and classification.
Back propagation Neural Network based identification
along with wavelet based adaptive windowing technique
can be done for detecting tumours in digital
mammograms. The algorithm is validated with
mammograms in Mammographic Image Analysis
Society Mini Mammographic database which shows
that the proposed technique is capable of detecting
tumours of very different sizes.
the early detection of breast diseases. Mammogram analysis
refers the processing of mammograms with the goal of
finding abnormality presented in the mammogram. In this
paper, the tumour can be detected by using wavelet based
adaptive windowing technique. Coarse segmentation is the
first step which can be done by using wavelet based
histogram thresholding where, the thereshold value is chosen
by performing 1-D wavelet based analysis of PDFs of
wavelet transformed images at different channels. Fine
segmentation can be done by partitioning the image into
fixed number of large and small windows. By calculating the
mean, maximum and minimum pixel values for the windows
a threshold value has been obtained. Depending upon the
threshold values the suspicious areas have been segmented.
Intensity adjustment is applied as a preprocessing step to
improve the quality of an image before applying the proposed
technique. The algorithm is validated with mammograms in
Mammographic Image Analysis Society Mini
Mammographic database which shows that the proposed
technique is capable of detecting lesions of very different
sizes.
Fuzzy Adaptive Hysteresis Current Controller to control the inverter, used in the non-linear timevariant
solar photovoltaic cell. The proposed controller has the advantages of both fuzzy as well as
adaptive controller. It is capable of reducing the total harmonic distortion and to provide acceptable
switching frequency. The mathematical model of Photovoltaic array is developed using the
Newton’s method using the parameter obtained from a commercial photovoltaic data sheet under
variable weather conditions, in which the effect of irradiance and temperature are considered. The
modeled Photovoltaic array is interfaced with DC-DC boost converter, AC-DC inverter and load. A
DC-DC boost converter is used to step up the input DC voltage of the Photovoltaic array while the
DC-AC single-phase inverter converts the input DC comes from boost converter into AC. The
performance of the proposed controller of inverter is evaluated through MATLAB-Simulation.
Unlike standard adaptive controller designs, this adaptive fuzzy controller does not require an
explicit mathematical model of the system. The results obtained with the proposed algorithm are
compared with those obtained when using conventional fixed hysteresis current controller for
single-phase photovoltaic inverter in terms of THD and switching frequency
they are useful for phenotype classification of diseases. Different from black box methods, fuzzy expert
system can produce interpretable classifier with knowledge expressed in terms of if-then rules and membership
function. This paper proposes a novel Genetic Swarm Algorithm (GSA) for obtaining near optimal
rule set and membership function tuning. Advanced and problem specific genetic operators are proposed
to improve the convergence of GSA and classification accuracy. The performance of the proposed
approach is evaluated using six gene expression data sets. From the simulation study it is found that
the proposed approach generated a compact fuzzy system with high classification accuracy for all the
data sets when compared with other approaches.
system. It is observed from the study that the industry can replace the existing TL-D 36 W/54
fluorescent tube lights with energy efficient TL-5 28 W/865 fluorescent tube lights with electronic
ballast. This has resulted in energy saving of 206388 kWh per annum. The corresponding CO2
mitigation is estimated at 167.174 t CO2/annum. An experiment was conducted to identify the exact
light intensity of the proposed lighting system. The result shows that the light intensity has reduced
by 11.1 % which is insignificant compared to energy saving and CO2 mitigation. The investment for
the new lighting fixtures is estimated at INR 0.468 million with cost saving of INR 1.65 million
which has return on investment of just 3.3 months
generation systems depends on the solar irradiation and
temperature. The PV system can supply the maximum power to
the load at a particular operating point which is generally called
as maximum power point (MPP), at which the entire PV system
operates with maximum efficiency and produces its maximum
power. Hence, a Maximum power point tracking (MPPT)
methods are used to maximize the PV array output power by
tracking continuously the maximum power point. The proposed
MPPT controller is designed for 10kW solar PV system installed
at Cape Institute of Technology. This paper presents the fuzzy
logic based MPPT algorithm. A fuzzy logic based MPPT control
technique is implemented to generate the optimal voltage from
the photovoltaic system by modulating the duty cycle applied to
the buck boost dc-dc converter. The proposed algorithm gives a
good maximum power operation of the PV system. Simulation
results obtained are presented and compared with the
conventional P&O controller. Simulation results show the
effectiveness of the proposed technique.
Model Tree (LOLIMOT) and an adaptive neuro–fuzzy inference system (ANFIS). The Direct and Inverse
model building using LOLIMOT and ANFIS structures is described and compared. The direct and inverse
models of the pH system are identified based on experimental data for the LOLIMOT and ANFIS structures.
The identified models are implemented in the experimental pH system with IMC structure using a GUI
developed in the MATLAB -SIMULINK platform. The main aim is to illustrate the online modelling and control of the experimental setup. The results of real-time control of an experimental pH process using the Internal Model Control (IMC) strategy are also presented.
classification. Our previous Genetic Swarm Algorithm (GSA) approach has improved the classification accuracy of the fuzzy expert system at
the cost of their interpretability. The if-then rules produced by the GSA are lengthy and complex which is difficult for the physician to understand.
To address this interpretability-accuracy tradeoff, the rule set is represented using integer numbers and the task of rule generation is
treated as a combinatorial optimization task. Ant Colony Optimization (ACO) with local and global pheromone updations are applied to find
out the fuzzy partition based on the gene expression values for generating simpler rule set. In order to address the formless and continuous
expression values of a gene, this paper employs Artificial Bee Colony (ABC) algorithm to evolve the points of membership function. Mutual
Information is used for idenfication of informative genes. The performance of the proposed hybrid Ant Bee Algorithm (ABA) is evaluated using
six gene expression data sets. From the simulation study, it is found that the proposed approach generated an accurate fuzzy system with
highly interpretable and compact rules for all the data sets when compared with other approaches
planning and operation of deregulated power systems.
The voltage stability problems is a most challenging one
for the system operators in deregulated power systems
because of the intense use of transmission line capabilities
and poor regulation in market environment. This
article addresses the congestion management problem
avoiding offline transmission capacity limits related to
voltage stability by considering Voltage Security
Constrained Optimal Power Flow (VSCOPF) problem in
deregulated environment. This article presents the application
of Multi Objective Differential Evolution (MODE)
algorithm to solve the VSCOPF problem in new competitive
power systems. The maximum of L-index of the load
buses is taken as the indicator of voltage stability and is
incorporated in the Optimal Power Flow (OPF) problem.
The proposed method in hybrid power market which also
gives solutions to voltage stability problems by considering
the generation rescheduling cost and load shedding
cost which relieves the congestion problem in deregulated
environment. The buses for load shedding are
selected based on the minimum eigen value of Jacobian
with respect to the load shed. In the proposed approach,
real power settings of generators in base case and contingency
cases, generator bus voltage magnitudes, real
and reactive power demands of selected load buses using
sensitivity analysis are taken as the control variables and
are represented as the combination of floating point
numbers and integers. DE/randSF/1/bin strategy scheme
of differential evolution with self-tuned parameter which
employs binomial crossover and difference vector based
mutation is used for the VSCOPF problem. A fuzzy based
mechanism is employed to get the best compromise solution
from the pareto front to aid the decision maker. The
proposed VSCOPF planning model is implemented on
IEEE 30-bus system, IEEE 57 bus practical system and
IEEE 118 bus system. The pareto optimal front obtained
from MODE is compared with reference pareto front and
the best compromise solution for all the cases are
obtained from fuzzy decision making strategy. The performance
measures of proposed MODE in two test systems
are calculated using suitable performance metrices.
The simulation results show that the proposed approach
provides considerable improvement in the congestion
management by generation rescheduling and load shedding
while enhancing the voltage stability in deregulated
power system.
MPPT (maximum power point tracking) in PV system. The PV system along with the proposed MPPT algorithm was simulated using Matlab/Simulink simscape tool box. The simulated system was evaluated under uniform and non-uniform irradiation conditions and the results are presented. For comparison, P&O (perturb and observe) and Fuzzy based Modified Hill Climbing algorithms were used for MPP
tracking, and the results show that the proposed approach is effective in tracking the MPP under partial shading conditions. To validate the simulated system hardware implementation of the proposed algorithm was carried out using FPGA (Field Programmable Gate Array).
steady state performance is a challenging task. The main objective of this paper is to design an MRAC with very good steady-state and transient performance for a nonlinear process such as the hybrid tank process. A modification to the MRAC scheme is proposed in this
study. Real-coded Genetic Algorithm (RGA) is used to tune off-line the controller parameters. Three different versions of MRAC and also a Proportional Integral Derivative (PID)
controller are employed, and their performances are compared by using MATLAB. Input–
output data of a coupled tank setup of the hybrid tank process are obtained by using Lab
VIEW and a system identification procedure is carried out. The accuracy of the resultant
model is further improved by parameter tuning using RGA. The simulation results shows that the proposed controller gives better transient performance than the well-designed
PID controller or the MRAC does; while giving equally good steady-state performance. It is concluded that the proposed controllers can be used to achieve very good transient
and steady state performance during the control of any nonlinear process.
simplicity, efficiency and flexibility. Recently, the control of non-linear processes in the
industries have turned the attention towards the intelligent controllers such as, Neural
Networks, Fuzzy Logic Controller, Genetic Algorithm tuned Controllers, Adaptive
Controller, etc. This paper focuses on the Investigation of Intelligent Controllers for conical
tank level process. A conical tank is a highly nonlinear process due to the variation in the
area of cross section of the level system with change in shape. In this work, Fuzzy Logic
Controller is designed for the control of nonlinear process to ensure the exact level
maintenance. The simulation results are obtained by Servo and Regulator operation of the
nonlinear conical tank process. For this work, Fuzzy Logic Controller is compared with
Conventional PI Controller.
the task of security enhancement is formulated as a multi-objective optimization problem with minimization
of fuel cost and minimization of FACTS device investment cost as objectives. Generator active
power, generator bus voltage magnitude and the reactance of Thyristor Controlled Series Capacitors
(TCSC) are taken as the decision variables. The probable locations of TCSC are pre-selected based on the
values of Line Overload Sensitivity Index (LOSI) calculated for each branch in the system. Multi-objective
genetic algorithm (MOGA) is applied to solve this security optimization problem. In the proposed GA, the
decision variables are represented as floating point numbers in the GA population. The MOGA emphasize
non-dominated solutions and simultaneously maintains diversity in the non-dominated solutions. A
fuzzy set theory-based approach is employed to obtain the best compromise solution over the trade-off
curve. The proposed approach has been evaluated on the IEEE 30-bus and IEEE 118-bus test systems. Simulation
results show the effectiveness of the proposed approach for solving the multi-objective security
enhancement problem.
for mitigation of voltage sags and swells which are the major problems and issues on non linear
loads.The Dynamic Voltage Restore (DVR) has become popular as a cost effective solutions for the
protection of sensitive loads from voltage sags and swells.. The control of compensation voltages in
DVR based on a-b-c to d-q-0 algorithm is discussed. The proposed control technique is cost
effective and simple to design. Computer simulations are carried out in a suitable test system to
investigate the effectiveness of control technique by using MATLAB/SIMULINK software.
for the early detection of breast diseases. Mammogram
analysis refers the processing of mammograms with the
goal of finding abnormality presented in the
mammogram. In this paper, various steps involved in
the recognition of breast tumours include image
segmentation, feature extraction and classification.
Back propagation Neural Network based identification
along with wavelet based adaptive windowing technique
can be done for detecting tumours in digital
mammograms. The algorithm is validated with
mammograms in Mammographic Image Analysis
Society Mini Mammographic database which shows
that the proposed technique is capable of detecting
tumours of very different sizes.
the early detection of breast diseases. Mammogram analysis
refers the processing of mammograms with the goal of
finding abnormality presented in the mammogram. In this
paper, the tumour can be detected by using wavelet based
adaptive windowing technique. Coarse segmentation is the
first step which can be done by using wavelet based
histogram thresholding where, the thereshold value is chosen
by performing 1-D wavelet based analysis of PDFs of
wavelet transformed images at different channels. Fine
segmentation can be done by partitioning the image into
fixed number of large and small windows. By calculating the
mean, maximum and minimum pixel values for the windows
a threshold value has been obtained. Depending upon the
threshold values the suspicious areas have been segmented.
Intensity adjustment is applied as a preprocessing step to
improve the quality of an image before applying the proposed
technique. The algorithm is validated with mammograms in
Mammographic Image Analysis Society Mini
Mammographic database which shows that the proposed
technique is capable of detecting lesions of very different
sizes.
Fuzzy Adaptive Hysteresis Current Controller to control the inverter, used in the non-linear timevariant
solar photovoltaic cell. The proposed controller has the advantages of both fuzzy as well as
adaptive controller. It is capable of reducing the total harmonic distortion and to provide acceptable
switching frequency. The mathematical model of Photovoltaic array is developed using the
Newton’s method using the parameter obtained from a commercial photovoltaic data sheet under
variable weather conditions, in which the effect of irradiance and temperature are considered. The
modeled Photovoltaic array is interfaced with DC-DC boost converter, AC-DC inverter and load. A
DC-DC boost converter is used to step up the input DC voltage of the Photovoltaic array while the
DC-AC single-phase inverter converts the input DC comes from boost converter into AC. The
performance of the proposed controller of inverter is evaluated through MATLAB-Simulation.
Unlike standard adaptive controller designs, this adaptive fuzzy controller does not require an
explicit mathematical model of the system. The results obtained with the proposed algorithm are
compared with those obtained when using conventional fixed hysteresis current controller for
single-phase photovoltaic inverter in terms of THD and switching frequency
they are useful for phenotype classification of diseases. Different from black box methods, fuzzy expert
system can produce interpretable classifier with knowledge expressed in terms of if-then rules and membership
function. This paper proposes a novel Genetic Swarm Algorithm (GSA) for obtaining near optimal
rule set and membership function tuning. Advanced and problem specific genetic operators are proposed
to improve the convergence of GSA and classification accuracy. The performance of the proposed
approach is evaluated using six gene expression data sets. From the simulation study it is found that
the proposed approach generated a compact fuzzy system with high classification accuracy for all the
data sets when compared with other approaches.
system. It is observed from the study that the industry can replace the existing TL-D 36 W/54
fluorescent tube lights with energy efficient TL-5 28 W/865 fluorescent tube lights with electronic
ballast. This has resulted in energy saving of 206388 kWh per annum. The corresponding CO2
mitigation is estimated at 167.174 t CO2/annum. An experiment was conducted to identify the exact
light intensity of the proposed lighting system. The result shows that the light intensity has reduced
by 11.1 % which is insignificant compared to energy saving and CO2 mitigation. The investment for
the new lighting fixtures is estimated at INR 0.468 million with cost saving of INR 1.65 million
which has return on investment of just 3.3 months
generation systems depends on the solar irradiation and
temperature. The PV system can supply the maximum power to
the load at a particular operating point which is generally called
as maximum power point (MPP), at which the entire PV system
operates with maximum efficiency and produces its maximum
power. Hence, a Maximum power point tracking (MPPT)
methods are used to maximize the PV array output power by
tracking continuously the maximum power point. The proposed
MPPT controller is designed for 10kW solar PV system installed
at Cape Institute of Technology. This paper presents the fuzzy
logic based MPPT algorithm. A fuzzy logic based MPPT control
technique is implemented to generate the optimal voltage from
the photovoltaic system by modulating the duty cycle applied to
the buck boost dc-dc converter. The proposed algorithm gives a
good maximum power operation of the PV system. Simulation
results obtained are presented and compared with the
conventional P&O controller. Simulation results show the
effectiveness of the proposed technique.
Model Tree (LOLIMOT) and an adaptive neuro–fuzzy inference system (ANFIS). The Direct and Inverse
model building using LOLIMOT and ANFIS structures is described and compared. The direct and inverse
models of the pH system are identified based on experimental data for the LOLIMOT and ANFIS structures.
The identified models are implemented in the experimental pH system with IMC structure using a GUI
developed in the MATLAB -SIMULINK platform. The main aim is to illustrate the online modelling and control of the experimental setup. The results of real-time control of an experimental pH process using the Internal Model Control (IMC) strategy are also presented.
classification. Our previous Genetic Swarm Algorithm (GSA) approach has improved the classification accuracy of the fuzzy expert system at
the cost of their interpretability. The if-then rules produced by the GSA are lengthy and complex which is difficult for the physician to understand.
To address this interpretability-accuracy tradeoff, the rule set is represented using integer numbers and the task of rule generation is
treated as a combinatorial optimization task. Ant Colony Optimization (ACO) with local and global pheromone updations are applied to find
out the fuzzy partition based on the gene expression values for generating simpler rule set. In order to address the formless and continuous
expression values of a gene, this paper employs Artificial Bee Colony (ABC) algorithm to evolve the points of membership function. Mutual
Information is used for idenfication of informative genes. The performance of the proposed hybrid Ant Bee Algorithm (ABA) is evaluated using
six gene expression data sets. From the simulation study, it is found that the proposed approach generated an accurate fuzzy system with
highly interpretable and compact rules for all the data sets when compared with other approaches
planning and operation of deregulated power systems.
The voltage stability problems is a most challenging one
for the system operators in deregulated power systems
because of the intense use of transmission line capabilities
and poor regulation in market environment. This
article addresses the congestion management problem
avoiding offline transmission capacity limits related to
voltage stability by considering Voltage Security
Constrained Optimal Power Flow (VSCOPF) problem in
deregulated environment. This article presents the application
of Multi Objective Differential Evolution (MODE)
algorithm to solve the VSCOPF problem in new competitive
power systems. The maximum of L-index of the load
buses is taken as the indicator of voltage stability and is
incorporated in the Optimal Power Flow (OPF) problem.
The proposed method in hybrid power market which also
gives solutions to voltage stability problems by considering
the generation rescheduling cost and load shedding
cost which relieves the congestion problem in deregulated
environment. The buses for load shedding are
selected based on the minimum eigen value of Jacobian
with respect to the load shed. In the proposed approach,
real power settings of generators in base case and contingency
cases, generator bus voltage magnitudes, real
and reactive power demands of selected load buses using
sensitivity analysis are taken as the control variables and
are represented as the combination of floating point
numbers and integers. DE/randSF/1/bin strategy scheme
of differential evolution with self-tuned parameter which
employs binomial crossover and difference vector based
mutation is used for the VSCOPF problem. A fuzzy based
mechanism is employed to get the best compromise solution
from the pareto front to aid the decision maker. The
proposed VSCOPF planning model is implemented on
IEEE 30-bus system, IEEE 57 bus practical system and
IEEE 118 bus system. The pareto optimal front obtained
from MODE is compared with reference pareto front and
the best compromise solution for all the cases are
obtained from fuzzy decision making strategy. The performance
measures of proposed MODE in two test systems
are calculated using suitable performance metrices.
The simulation results show that the proposed approach
provides considerable improvement in the congestion
management by generation rescheduling and load shedding
while enhancing the voltage stability in deregulated
power system.
MPPT (maximum power point tracking) in PV system. The PV system along with the proposed MPPT algorithm was simulated using Matlab/Simulink simscape tool box. The simulated system was evaluated under uniform and non-uniform irradiation conditions and the results are presented. For comparison, P&O (perturb and observe) and Fuzzy based Modified Hill Climbing algorithms were used for MPP
tracking, and the results show that the proposed approach is effective in tracking the MPP under partial shading conditions. To validate the simulated system hardware implementation of the proposed algorithm was carried out using FPGA (Field Programmable Gate Array).
steady state performance is a challenging task. The main objective of this paper is to design an MRAC with very good steady-state and transient performance for a nonlinear process such as the hybrid tank process. A modification to the MRAC scheme is proposed in this
study. Real-coded Genetic Algorithm (RGA) is used to tune off-line the controller parameters. Three different versions of MRAC and also a Proportional Integral Derivative (PID)
controller are employed, and their performances are compared by using MATLAB. Input–
output data of a coupled tank setup of the hybrid tank process are obtained by using Lab
VIEW and a system identification procedure is carried out. The accuracy of the resultant
model is further improved by parameter tuning using RGA. The simulation results shows that the proposed controller gives better transient performance than the well-designed
PID controller or the MRAC does; while giving equally good steady-state performance. It is concluded that the proposed controllers can be used to achieve very good transient
and steady state performance during the control of any nonlinear process.
simplicity, efficiency and flexibility. Recently, the control of non-linear processes in the
industries have turned the attention towards the intelligent controllers such as, Neural
Networks, Fuzzy Logic Controller, Genetic Algorithm tuned Controllers, Adaptive
Controller, etc. This paper focuses on the Investigation of Intelligent Controllers for conical
tank level process. A conical tank is a highly nonlinear process due to the variation in the
area of cross section of the level system with change in shape. In this work, Fuzzy Logic
Controller is designed for the control of nonlinear process to ensure the exact level
maintenance. The simulation results are obtained by Servo and Regulator operation of the
nonlinear conical tank process. For this work, Fuzzy Logic Controller is compared with
Conventional PI Controller.
the task of security enhancement is formulated as a multi-objective optimization problem with minimization
of fuel cost and minimization of FACTS device investment cost as objectives. Generator active
power, generator bus voltage magnitude and the reactance of Thyristor Controlled Series Capacitors
(TCSC) are taken as the decision variables. The probable locations of TCSC are pre-selected based on the
values of Line Overload Sensitivity Index (LOSI) calculated for each branch in the system. Multi-objective
genetic algorithm (MOGA) is applied to solve this security optimization problem. In the proposed GA, the
decision variables are represented as floating point numbers in the GA population. The MOGA emphasize
non-dominated solutions and simultaneously maintains diversity in the non-dominated solutions. A
fuzzy set theory-based approach is employed to obtain the best compromise solution over the trade-off
curve. The proposed approach has been evaluated on the IEEE 30-bus and IEEE 118-bus test systems. Simulation
results show the effectiveness of the proposed approach for solving the multi-objective security
enhancement problem.