CN119511998A - An intelligent monitoring system for power plant unit operation status based on big data - Google Patents
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Abstract
本申请涉及电厂运行领域，公开了一种基于大数据的电厂机组运行状态智能监控系统，包括监控采集模块，所述监控采集模块包括温度传感器、压力传感器、振动频率传感器、电流传感器，通过将上述传感器设备安装在电厂机组上，从而对电厂机组的各项参数进行采集和监测，并将采集和监测到的各项参数数值传输到中央控制模块中，所述监控采集模块上连接有中央控制模块。本发明通过设置预测模块，并根据机组历史参数和故障信息，运用故障概率预测公式和故障时间预测公式进行计算，预测未来故障发生时间。这些预测功能使工作人员能提前了解机组可能出现的故障，从而在出现故障时，第一时间携带工具进行抢险，提高电厂机组的整体效率。
The present application relates to the field of power plant operation, and discloses an intelligent monitoring system for the operation status of power plant units based on big data, including a monitoring and acquisition module, wherein the monitoring and acquisition module includes a temperature sensor, a pressure sensor, a vibration frequency sensor, and a current sensor. By installing the above-mentioned sensor equipment on the power plant unit, various parameters of the power plant unit are collected and monitored, and the values of the collected and monitored parameters are transmitted to the central control module, and the monitoring and acquisition module is connected to the central control module. The present invention sets a prediction module, and uses the fault probability prediction formula and the fault time prediction formula to calculate according to the historical parameters and fault information of the unit to predict the time of occurrence of future faults. These prediction functions enable the staff to understand the possible faults of the unit in advance, so that when a fault occurs, they can carry tools for rescue in the first time, thereby improving the overall efficiency of the power plant unit.
Description
Technical Field
The invention relates to the technical field of power plant operation, in particular to an intelligent monitoring system for the operation state of a power plant unit based on big data.
Background
Along with the rapid development of the power industry, the power plant unit is continuously evolved towards the direction of large capacity and high parameters, and the structure and the operation principle of the power plant unit are increasingly complex. In this case, ensuring safe, stable and efficient operation of the power plant unit is a critical issue.
And traditional power plant unit control is gathered each item parameter of power plant unit through the sensor to in transmitting the supervisory equipment with it, contrast and analysis are carried out each item parameter through supervisory equipment, thereby know the running state of power plant unit, when preventing that power plant unit from breaking down, fail the timely warning staff of first time, thereby influence the life and the holistic work efficiency of power plant unit.
Considering that the feedback of the faults is usually reminded through real-time comparison, when the workers receive the fault feedback at the first time, the workers cannot be prepared for emergency or maintenance, a great amount of time is still required to select proper repair tools for the reasons of the faults, the maintenance work efficiency of the power plant unit after the faults occur is low, and the normal operation efficiency of the power plant unit is delayed.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an intelligent monitoring system for the running state of a power plant unit based on big data, which solves the problems that the existing monitoring system for the power plant unit is low in fault maintenance efficiency because the fault feedback is mainly carried out through real-time comparison reminding, workers do not need to rescue or maintain preparation when receiving the feedback, and a large amount of time is required for selecting repair tools.
The intelligent monitoring system for the running state of the power plant unit based on the big data comprises a monitoring acquisition module, wherein the monitoring acquisition module comprises a temperature sensor, a pressure sensor, a vibration frequency sensor and a current sensor, the sensor equipment is arranged on the power plant unit, so that all parameters of the power plant unit are acquired and monitored, the acquired and monitored parameter values are transmitted to a central control module, the central control module serves as a core of the whole system, the working flow and data interaction among all modules are coordinated, various parameters acquired by the monitoring acquisition module are received at the same time, the data analysis and processing module is coordinated to read the parameters, the data analysis and processing module is connected with a preset threshold value, after the parameters of the real-time power plant unit are received, the parameters and the threshold value are compared, the running state of the power plant unit is judged, the data analysis and processing module is connected with a prediction module, the data analysis and processing module is connected with all the prediction module according to a prediction fault state of the power plant unit, and the power plant unit is connected with a prediction fault state prediction module by using a calculation formula, and the power plant unit is used for carrying out the interaction with the prediction of the fault state of the power plant unit, and the fault prediction module is used for carrying out the interaction between the power plant unit and the fault prediction module.
Preferably, the data analysis and processing module is connected with a threshold module, and the threshold module stores a threshold range preset for each operation parameter of the power plant unit, wherein the specific threshold range comprises a temperature parameter threshold range, a pressure parameter threshold range, a vibration frequency parameter threshold range and a current parameter threshold range.
Preferably, the threshold module is connected with a comparison analysis module, the comparison analysis module compares various parameters of the real-time power plant unit with a threshold range, when the parameters are found to be beyond or below the threshold range, a preset scheme module is started, specific fault information of the power plant unit is judged according to a plurality of groups or a group of overall data with the parameters exceeding, the comparison analysis module is connected with a preset scheme module, different fault schemes are stored in the preset scheme module, and the specific fault schemes comprise a temperature abnormal fault scheme, a pressure abnormal fault scheme, a vibration frequency abnormal fault scheme and a current abnormal fault scheme.
Preferably, the specific calculation formula in the formula calculation module includes:
1. the failure probability prediction formula:
The method comprises the steps of determining the prior probability of the fault A, wherein P (A) is the prior probability of the fault A and can be obtained through historical fault data statistics, specifically, the occurrence frequency of a certain fault in the past year, and P (B|A) is the probability of the current value condition of the parameter B under the condition that the fault A occurs and is obtained through analysis of the parameter change condition in a large number of historical fault cases.
P (B) is the probability of the current value of the parameter B, calculated by a full probability formula, the value of the parameter B is monitored in real time, and the probability of the fault A in the current parameter state is calculated by using the calculation formula.
Preferably, the specific calculation formula in the formula calculation module further includes:
2. The failure time prediction formula:
1. calculating probability density function:
f(t)＝λe-λt,t≥0
2. calculating the probability of failure within the future time t:
P(T≤t+t0|T>t0)＝1-e-λt
Wherein:
T represents time, in a probability density function f (T), T is a continuous time variable after the unit starts to operate, and in fault probability calculation, T represents future prediction time, lambda is a fault rate and is a constant obtained through statistical analysis of historical fault data, T 0 represents the time that the unit has operated, and f (T) is a probability density function for describing the distribution condition of the fault time T;
P (T.ltoreq.t+t 0|T>t0) is a conditional probability that if the unit has been operated for a time T 0 and has not failed, the probability of failure occurs within a future time T.
Preferably, the formula calculation module is connected with a trend prediction module, and the trend prediction module predicts whether the parameters exceed the normal range and the occurring change rate in the next several hours according to the current, voltage and temperature parameter change trend by using the calculation result of the formula calculation module.
Preferably, the specific prediction formula in the trend prediction module is:
Wherein:
Y t is the actual observed value of the unit operation parameter at the time t; Is the predicted value of the unit operation parameter at the time t, alpha is a smooth constant, the value range is between (0, 1), and the actual observed value y t and the predicted value at the current time t are combined And according to the given smoothing constant alpha, the method is used for predicting the future change trend of the operation parameters.
Preferably, the formula calculation module is connected with a trend prediction module, and the trend prediction module predicts whether the parameters exceed the normal range and the occurring change rate in the next several hours according to the current, voltage and temperature parameter change trend by using the calculation result of the formula calculation module.
Preferably, the trend prediction module is connected with a risk assessment module, and the risk assessment module comprehensively assesses the risk faced by the operation of the power plant unit according to the formula calculation module and the conclusion of the trend prediction module.
Preferably, the interactive module is connected with a user interface module, the user interface module provides a platform for a user, system information is displayed through the platform, the user interface module is connected with an alarm and notification module, and the alarm and notification module timely sends the comparison analysis module to staff.
The invention provides an intelligent monitoring system for the running state of a power plant unit based on big data. The beneficial effects are as follows:
1. According to the invention, a prediction module is arranged, and according to the historical parameters and fault information of the unit, a fault probability prediction formula and a fault time prediction formula are used for calculation, so that the future fault occurrence time is predicted. The prediction functions enable staff to know possible faults of the unit in advance, preventive maintenance plans are made in advance, and therefore tools are carried for emergency rescue at the first time when faults occur, and the overall efficiency of the power plant unit is improved.
2. According to the invention, various parameters of the power plant unit are comprehensively acquired through the monitoring acquisition module formed by various sensors, data are transmitted to the central control module, and then the data are analyzed by the data analysis and processing module. Compared with the traditional single monitoring mode, the multi-parameter real-time monitoring mode can master the running state of the unit more comprehensively and accurately, avoid fault monitoring blind spots caused by missing part of parameters, and effectively improve the timeliness and accuracy of fault early warning.
3. According to the invention, a special calculation formula is used, and various historical parameters are substituted and calculated, so that the obtained prediction result is more accurate, a powerful support is provided for accurately judging faults and grasping the running trend of the unit by the monitoring system, and the accuracy of the prediction result is improved.
Drawings
FIG. 1 is a system flow diagram of the present invention;
FIG. 2 is a flow chart of a prediction module according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples:
Referring to fig. 1-2, an embodiment of the present invention provides an intelligent monitoring system for an operation state of a power plant unit based on big data, including a monitoring collection module, where the monitoring collection module includes a temperature sensor, a pressure sensor, a vibration frequency sensor, and a current sensor, the sensor devices are installed on the power plant unit, so as to collect and monitor various parameters of the power plant unit, and transmit the collected and monitored values of the various parameters to a central control module, the central control module is connected to the monitoring collection module as a core of the whole system, and coordinates a workflow and data interaction between the modules, and simultaneously receives various parameters collected by the monitoring collection module, and coordinates a data analysis and processing module to read the parameters, the central control module is connected with a data analysis and processing module, and after receiving parameters of the power plant unit in real time, the parameters are compared with the threshold, so as to determine the operation state of the power plant unit, and the data analysis and processing module is connected with a prediction module, and the prediction module is connected to the prediction module according to a prediction module, and the prediction module is connected to a fault prediction module, so as to implement a result of the interaction between the power plant unit and the fault prediction module and the operation state.
The data analysis and processing module is connected with a threshold module, and the threshold module stores a threshold range preset for each operation parameter of the power plant unit, wherein the specific threshold range comprises a temperature parameter threshold range, a pressure parameter threshold range, a vibration frequency parameter threshold range and a current parameter threshold range;
The threshold value module is connected with a comparison analysis module, the comparison analysis module compares various parameters of the real-time power plant unit with a threshold value range, when the parameters are found to be beyond or below the threshold value range, a preset scheme module is started, specific fault information of the power plant unit is judged according to a plurality of groups or a group of overall data with the parameters exceeding, the comparison analysis module is connected with a preset scheme module, different fault schemes are stored in the preset scheme module, and the specific fault scheme comprises:
A temperature abnormality fault scheme for checking whether the fuel supply system is abnormal, such as whether the fuel quantity is excessive or whether the burner is faulty, checking whether the ventilation system is normal, and ensuring proper air quantity when the temperature is too high;
The pressure abnormality fault scheme is that under the condition of overpressure, a safety valve and a regulating valve are checked to work normally, whether an outlet valve is closed or the opening degree is abnormal, whether the pump runs abnormally to cause the lift to be too high is checked, and under the condition of underpressure, the combustion condition and the evaporation capacity of a boiler are checked to be normal, and whether a pipeline leaks is checked;
the abnormal fault scheme of vibration frequency is that on-site inspection is carried out to check whether obvious mechanical looseness and part damage exist or not, and then professional vibration detection equipment is used for further analyzing the frequency spectrum characteristics of vibration to judge whether the rotor is unbalanced, not centered or the bearing fault cause;
and under the condition of overload fault, checking whether the winding of the generator is good, whether a turn-to-turn short circuit problem exists, checking whether a load is jammed or in overload operation, checking whether a protection device of the motor acts normally, and checking whether the load of the generator or the motor is suddenly reduced, checking whether electric connection is loose, so that current transmission is abnormal and checking whether the output power of the prime motor is normal.
The prediction module is connected with a data arrangement module, the data arrangement module is used for arranging parameters, fault problems and fault time nodes of the power plant unit under long-time work and transmitting the arranged data to the formula calculation module, the formula calculation module is connected with the data arrangement module, the formula calculation module uses a special formula to substitute each parameter arranged and collected by the data arrangement module to predict the fault problems and the fault time of the power plant unit, and the specific calculation formula is as follows;
1. the failure probability prediction formula:
The method comprises the steps of determining the prior probability of the fault A, wherein P (A) is the prior probability of the fault A and can be obtained through historical fault data statistics, specifically, the occurrence frequency of a certain fault in the past year, and P (B|A) is the probability of the current value condition of the parameter B under the condition that the fault A occurs and is obtained through analysis of the parameter change condition in a large number of historical fault cases.
P (B) is the probability of the current value of the parameter B, calculated by a full probability formula, the value of the parameter B is monitored in real time, and the probability of the fault A in the current parameter state is calculated by using the calculation formula.
2. The failure time prediction formula:
1. calculating probability density function:
f(t)＝λe-λt,t≥0
2. calculating the probability of failure within the future time t:
P(T≤t+t0|T>t0)＝1-e-λt
Wherein:
T represents time, in a probability density function f (T), T is a continuous time variable after the unit starts to operate, and in fault probability calculation, T represents future prediction time, lambda is a fault rate and is a constant obtained through statistical analysis of historical fault data, T 0 represents the time that the unit has operated, and f (T) is a probability density function for describing the distribution condition of the fault time T;
p (T.ltoreq.t+t 0|T>t0) is a conditional probability indicating the probability of failure occurring within a future time T in the case where the unit has been operated for a time T 0 and has not failed;
The formula calculation module is connected with a trend prediction module, the trend prediction module predicts whether the parameters exceed the normal range and the occurring change rate within the next several hours according to the current, voltage and temperature parameter change trend by using the calculation result of the formula calculation module, and the specific prediction formula is as follows:
Wherein:
Y t is the actual observed value of the unit operation parameter at the time t; Is the predicted value of the unit operation parameter at the time t, alpha is a smooth constant, the value range is between (0, 1), and the actual observed value y t and the predicted value at the current time t are combined And according to the given smoothing constant alpha, the method is used for predicting the future change trend of the operation parameters.
The trend prediction module is connected with a risk assessment module, and the risk assessment module comprehensively assesses the risk of running of the power plant unit according to the formula calculation module and the conclusion of the trend prediction module;
The interaction module is connected with a user interface module, the user interface module provides a platform for a user, system information is displayed through the platform, the user interface module is convenient for a worker to watch, the user interface module is connected with an alarm and notification module, and the alarm and notification module timely sends the comparison analysis module to the worker, so that the effects of fault early warning and timely warning are achieved.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. The intelligent monitoring system for the running state of the power plant unit based on big data is characterized by comprising a monitoring acquisition module, wherein the monitoring acquisition module comprises a temperature sensor, a pressure sensor, a vibration frequency sensor and a current sensor, the sensor equipment is arranged on the power plant unit so as to acquire and monitor various parameters of the power plant unit, the acquired and monitored various parameter values are transmitted to a central control module, the monitoring acquisition module is connected with the central control module, the central control module serves as a core of the whole system, the work flow and the data interaction between the modules are coordinated, various parameters acquired by the monitoring acquisition module are received at the same time, the data analysis and processing module is coordinated to read the parameters, the central control module is connected with a data analysis and processing module, the data analysis and processing module is provided with a preset threshold value, after receiving various parameters of the real-time power plant unit, the parameters and the threshold value are compared, so that the running state of the power plant unit is judged, the data analysis and processing module is connected with a prediction module, and the prediction module is connected with a prediction module according to historical parameters and fault information of the unit, and the fault information is used for carrying out the interaction of the prediction module, and the fault analysis and the fault state is sent to a real-time prediction module.
2. The intelligent monitoring system for the operation state of the power plant unit based on big data according to claim 1, wherein the data analysis and processing module is connected with a threshold module, the threshold module stores a threshold range preset for each operation parameter of the power plant unit, and the specific threshold range comprises a temperature parameter threshold range, a pressure parameter threshold range, a vibration frequency parameter threshold range and a current parameter threshold range.
3. The intelligent monitoring system for the running state of the power plant unit based on big data according to claim 2 is characterized in that a comparison analysis module is connected to the threshold module, the comparison analysis module compares various parameters of the real-time power plant unit with a threshold range, when the parameters are found to be beyond or below the threshold range, a preset scheme module is started, specific fault information of the power plant unit is judged according to a plurality of groups or a group of overall data with the parameters exceeding, the comparison analysis module is connected with a preset scheme module, different fault schemes are stored in the preset scheme module, and the specific fault schemes comprise a temperature abnormal fault scheme, a pressure abnormal fault scheme, a vibration frequency abnormal fault scheme and a current abnormal fault scheme.
4. The intelligent monitoring system for the running state of the power plant unit based on big data according to claim 1, wherein the prediction module is connected with a data arrangement module, the data arrangement module arranges parameters, fault problems and fault time nodes of the power plant unit under long-time working, and transmits the arranged data to a formula calculation module, the data arrangement module is connected with the formula calculation module, and the formula calculation module predicts the fault problems and the fault time of the power plant unit by using a special formula and substituting each parameter arranged and collected by the data arrangement module.
5. The intelligent monitoring system for the operation state of a power plant unit based on big data according to claim 4, wherein the specific calculation formula in the formula calculation module comprises:
1. the failure probability prediction formula:
P (A) is the prior probability of the fault A, and can be obtained through statistics of historical fault data, specifically, the occurrence frequency of a certain fault in the past year;
P (B) is the probability of the current value of the parameter B, calculated by a full probability formula, the value of the parameter B is monitored in real time, and the probability of the fault A in the current parameter state is calculated by using the calculation formula.
6. The intelligent monitoring system for the operation state of a power plant unit based on big data according to claim 4, wherein the specific calculation formula in the formula calculation module further comprises:
2. The failure time prediction formula:
1. calculating probability density function:
f(t)=λe-λt,t≥0
2. calculating the probability of failure within the future time t:
P(T≤t+t0|T>t0)＝1-e-λt
Wherein:
T represents time, in a probability density function f (T), T is a continuous time variable after the unit starts to operate, and in fault probability calculation, T represents future prediction time, lambda is a fault rate and is a constant obtained through statistical analysis of historical fault data, T 0 represents the time that the unit has operated, and f (T) is a probability density function for describing the distribution condition of the fault time T;
P (T.ltoreq.t+t 0|T>t0) is a conditional probability that if the unit has been operated for a time T 0 and has not failed, the probability of failure occurs within a future time T.
7. The intelligent monitoring system for the operation state of the power plant unit based on big data according to claim 4, wherein the formula calculation module is connected with a trend prediction module, and the trend prediction module predicts whether the parameters will exceed the normal range and the occurring change rate in the next several hours according to the current, voltage and temperature parameter change trend by using the calculation result of the formula calculation module.
8. The intelligent monitoring system for the operation state of a power plant unit based on big data according to claim 7, wherein the specific prediction formula in the trend prediction module is:
Wherein:
Y t is the actual observed value of the unit operation parameter at the time t; Is the predicted value of the unit operation parameter at the time t, alpha is a smooth constant, the value range is between (0, 1), and the actual observed value y t and the predicted value at the current time t are combined And according to the given smoothing constant alpha, the method is used for predicting the future change trend of the operation parameters.
9. The intelligent monitoring system for the running state of the power plant unit based on big data according to claim 7, wherein the trend prediction module is connected with a risk assessment module, and the risk assessment module comprehensively assesses the risk faced by the running of the power plant unit according to the formula calculation module and the conclusion of the trend prediction module.
10. The intelligent monitoring system for the running state of the power plant unit based on big data according to claim 1, wherein the interactive module is connected with a user interface module, the user interface module provides a platform for a user, system information is displayed through the platform, the user interface module is connected with an alarm and notification module, and the alarm and notification module timely sends the comparison analysis module to staff.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN121024910A (en) * | 2025-10-31 | 2025-11-28 | 厚普智慧物联科技有限公司 | A condition monitoring and fault prediction system for hydrogen compressors |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN121024910A (en) * | 2025-10-31 | 2025-11-28 | 厚普智慧物联科技有限公司 | A condition monitoring and fault prediction system for hydrogen compressors |
| CN121024910B (en) * | 2025-10-31 | 2026-01-30 | 厚普智慧物联科技有限公司 | State monitoring and fault prediction system suitable for hydrogen energy compressor |
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