Stephen Oladipo

Temperature control of poultry house within thermal neutral zone of poultry birds is essential in order to reduce their mortality and increase production. The most advanced method to control the highly complex and nonlinear behaviour of the poultry house temperature, is fuzzy logic. On the other hand, PID controllers are used in most of poultry house due to its functional and structural simplicity. This paper presents a method of controlling the poultry house temperature by the combined action of both Fuzzy and PID controllers. In the design, fuzzy controller uses the structure of two inputs and three outputs. Deviation e and deviation rate ė are the inputs of the system. These are translated into a fuzzy form, fuzzy processed according to IF…THEN rules to arrive at a single outcome value and then defuzzified to get accurate values of d i p k k k , , which are used to auto-tune PID controller to control the poultry house temperature. The performances of the Fuzzy-PID based poultry h...

Energy forecasting is crucial for efficient energy management and planning for future energy needs. Previous studies have employed hybrid modeling techniques, but insufficient attention has been given to hyper-parameter tuning and parameter selection. In this study, we present a hybrid model, which combines fuzzy c-means clustered adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA), named GA–ANFIS–FCM, to model electricity consumption in Lagos districts, Nigeria. The model is simulated using the algorithms’ control settings, and the best model is identified after assessing their performance using renowned statistical indicators. To further narrow down the best viable model, the impact of the core parameter of the GA on the GA–ANFIS–FCM optimal model is examined by varying the crossover percentage in the range of 0.2–0.6. Firstly, the results reveal the better performance of the hybridized ANFIS model than the standalone ANFIS model. Additionally, the best model ...

Increasing economic and population growth has led to a rise in electricity consumption. Consequently, electrical utility firms must have a proper energy management strategy in place to improve citizens’ quality of life and ensure an organization’s seamless operation, particularly amid unanticipated circumstances such as coronavirus disease (COVID-19). There is a growing interest in the application of artificial intelligence models to electricity prediction during the COVID-19 pandemic, but the impacts of clustering methods and parameter selection have not been explored. Consequently, this study investigates the impacts of clustering techniques and different significant parameters of the adaptive neuro-fuzzy inference systems (ANFIS) model for predicting electricity consumption during the COVID-19 pandemic using districts of Lagos, Nigeria as a case study. The energy prediction of the dataset was examined in relation to three clustering techniques: grid partitioning (GP), subtractive...

Future energy planning relies on understanding how much energy is produced and consumed. In response, this study developed a multihybrid adaptive neuro-fuzzy inference system (ANFIS) for students’ residences, using the University of Johannesburg residence, South Africa as a case study. The model input variables are wind speed, temperature, and humidity, with the output being the equivalent energy consumption for the student housing. While the particle swarm optimization (PSO) technique is versatile and widely used, it falls short by exhibiting premature convergence. To address this problem, the velocity update equation of the original PSO algorithm is modified by incorporating a dynamic linear decreasing inertia weight, which improves the PSO algorithm’s convergence behaviour and aids both local and global search. Following that, the modified PSO (MPSO) is used to optimize the ANFIS parameters for the best model prediction. A comparative analysis is conducted between the MPSO, the o...

Electricity has become one of the most essential components of establishing a quality standard of living in any country. Consequently, considerable work has been focused on designing a sophisticated load frequency control (LFC) system. However, in light of limited resources and real-world challenges, computationally based control algorithms that are more effective and less expensive remain critically needed. Thus, this paper employs a modified honey badger algorithm (HBA) in conjunction with the concepts of Lévy flight and inertia weight to optimize the parameters of a new cascaded two-degree-of-freedom fractional-PID structure coupled with a proportional derivative (2DOF + FOPIDN)-PD controller to solve LFC problems in an interconnected power system (IPS) comprising conventional and renewable energy sources (RES). The proposed control technique is applied to a two-area IPS under diverse load conditions and in the presence of nonlinear elements and electronic devices. The proposed m...

This paper proposes a new hybridised approach comprising the flower pollination algorithm and pathfinder algorithm (FPAPFA), in order to address optimisation problems and for load frequency control system. Although the FPA is a popular algorithm that has been widely used in diverse applications, its implementation is met with the tendency to be trapped in local optimal due to an imbalance between the exploration and exploitation process. Consequently, the FPA's exploration functionality can be enhanced by using the PFA features to shift certain pollens to moderately enhanced locations rather than leading them to random positions. Furthermore, a modified fluctuation rate is incorporated into the PFA to reinforce the exploitative competence of the FPAPFA. Compared to other popular techniques, the proposed algorithm's performance was evaluated against 23 standard mathematical optimisation functions and statistically tested using the Wilcoxon rank-sum and Friedman rank tests. Moreover, the FPAPFA is applied to regulate two unequal multiarea interconnected power systems with different generating units (thermal, hydro, diesel, and wind power plants) while also integrating redox flow batteries (RFBs) and interline power flow controller (IPFC). Simulation results show that the proposed FPAPFA delivered better results than other algorithms with improved convergence speed, stability, and robustness.

This paper proposes a new hybridised approach comprising the flower pollination algorithm and pathfinder algorithm (FPAPFA), in order to address optimisation problems and for load frequency control system. Although the FPA is a popular algorithm that has been widely used in diverse applications, its implementation is met with the tendency to be trapped in local optimal due to an imbalance between the exploration and exploitation process. Consequently, the FPA's exploration functionality can be enhanced by using the PFA features to shift certain pollens to moderately enhanced locations rather than leading them to random positions. Furthermore, a modified fluctuation rate is incorporated into the PFA to reinforce the exploitative competence of the FPAPFA. Compared to other popular techniques, the proposed algorithm's performance was evaluated against 23 standard mathematical optimisation functions and statistically tested using the Wilcoxon rank-sum and Friedman rank tests. Moreover, the FPAPFA is applied to regulate two unequal multiarea interconnected power systems with different generating units (thermal, hydro, diesel, and wind power plants) while also integrating redox flow batteries (RFBs) and interline power flow controller (IPFC). Simulation results show that the proposed FPAPFA delivered better results than other algorithms with improved convergence speed, stability, and robustness.

Automatic Voltage Regulator (AVR) system is one of the major devices broadly used in many industrial applications for regulating the voltage of the synchronous generator within its nominal values. Consequently, providing a suitable controller for the AVR system becomes a necessity to prevent instability and error in the system’s output response. Studies from past works have shown that an adequately tuned PID controller will maximize the efficiency of the AVR system. In recent decades metaheuristic algorithms have become increasingly prevalent due to their tremendous success in addressing real-world optimization problems. This paper, therefore, presents a concise survey of the optimization of the PID and FOPID controllers with new generation metaheuristic algorithms for controlling the AVR system. A short description of each algorithm is presented with papers published in various reputable journals. Finally, the paper presents some future directions for research.

As the world's population grows and energy demand increases, it is necessary to increase the scale of the electrical system, which is more complicated. Consequently, adopting automatic generation control (AGC) scheme to meet the demand becomes inevitable. In this article, the fusion of flower pollinated algorithm (FPA) and pathfinder algorithm (PFA), named hereafter as hFPAPFA, is proposed to achieve maximum control efficiency by combining the exploitation of FPA with the exploration capacity of PFA. The proposed hFPAPFA is meant to regulate two unequal multi-area interconnected power system with different generating units such as thermal, hydro, wind power and diesel plants. The proposed control scheme aims to achieve this by using the new algorithm to optimize the fractional-order set-point weighted PID (FOSWPID) parameters under time domain-based fitness functions namely, integral time square error (ITSE) and integral time absolute error (ITAE) while simultaneously minimizing the power losses. Employing the same interconnected power systems, a comparative study with some recent approaches in renowned journals is conducted. The performance of the proposed method is observed under diverse load conditions scenarios. Moreover, three nonlinearities including boiler dynamics, the governor dead band (GDB) and generation rate constraints (GRC) are further integrated into the system from a pragmatic context. Finally, sensitivity tests involving various parameter changes and the introduction of random step load perturbations are carried out. From the results, the proposed approach outperformed other approaches under different load condition scenarios, incorporation of nonlinearities and random load perturbation, demonstrating the proposed technique's efficacy and reliability. INDEX TERMS Automatic generation control (AGC), flower pollinated algorithm, pathfinder algorithm integral time absolute error, governor dead band (GDB), generation rate constraints (GRC).

Providing a reliable and stable electric power system is one of the significant tasks of engineers in many industries. Consequently, a strong focus on the stability of the nominal frequency and adequate supply of voltage management is imperative. This paper aims to develop an efficient proportional integral derivative acceleration (PIDA) controller optimized by the combination of the flower pollinated algorithm (FPA) and pathfinder algorithm (PFA) to regulate combined load frequency and terminal voltage control regulation (LFC-AVR) systems. Whereas the FPA has the competence to exploit the search space, it lacks the potency to explore it, resulting in getting stuck in local optima. Hence, the PFA is integrated into the FPA to improve its performance. The integral time absolute error (ITAE) is selected as the performance index. Results obtained show that the proposed technique surpassed others in improving the step response of the LFC-AVR system under different scenarios.

Direct Current (DC) motors are broadly used in various industrial applications such as robotics, automobiles, toys and for many other motoring purposes. This is attributable to their extraordinary flexibility, durability and low implementation cost. It is essential to control the speed, position, torque and other variables of the DC motor to achieve the needed performance depending on the area of application. Many classical techniques have been used in the past to control the DC motor, however, such methods typically take a long time, particularly when used for complex nonlinear systems. The application of metaheuristic algorithms as a means of implementing Artificial Intelligence (AI) in this area has proven to be highly effective in overcoming these shortcomings. In recent decades, metaheuristic algorithms have become increasingly prevalent due to their tremendous success in addressing a number real-world optimization challenges in various fields of human activities extending from economic, pharmaceutical and industrial applications to intellectual applications. This review, therefore, presents the optimization of the PID controller with metaheuristic algorithms for controlling the DC motor drives. A short description for each algorithm is presented along with papers published in various renowned journals. For a robust review, the application of various forms of PID controller, as well as different types of DC motors are examined. Finally, the paper presents some open issues and future directions for research.

Abstract— Temperature control of poultry house within
thermal neutral zone of poultry birds is essential in order to
reduce their mortality and increase production. The most
advanced method to control the highly complex and nonlinear
behaviour of the poultry house temperature, is fuzzy logic. On
the other hand, PID controllers are used in most of poultry house
due to its functional and structural simplicity. This paper
presents a method of controlling the poultry house temperature
by the combined action of both Fuzzy and PID controllers. In the
design, fuzzy controller uses the structure of two inputs and
three outputs. Deviation e and deviation rate ė are the inputs of
the system. These are translated into a fuzzy form, fuzzy
processed according to IF…THEN rules to arrive at a
single outcome value and then defuzzified to get accurate
values of k p , ki , kd which are used to auto-tune PID
controller to control the poultry house temperature. The
performances of the Fuzzy-PID based poultry house temperature
control scheme during hot weather are compared with the
classical PID controller. The results show that the Fuzzy-PID
scheme is able to control the poultry house temperature more
effectively in terms of both the steady-state error and the settling
time than that of PID controller.
Keywords—Poultry house temperature, Fuzzy-PID control,
Defuzzification.