CN118232533B - Power electronic transformer control method based on self-adaptive control strategy - Google Patents
Power electronic transformer control method based on self-adaptive control strategy Download PDFInfo
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Abstract
The invention provides a power electronic transformer control method based on a self-adaptive control strategy, which relates to the technical field of power electronic control and comprises the following steps: performing data collection of the power electronic transformer, and establishing a transformation topological structure characteristic; performing association analysis of each power electronic transformer, and establishing M transformer control networks; deploying a monitoring sensor group; collecting real-time monitoring data, predicting the state of a power grid, and establishing a time sequence self-adaptive optimizing strategy; acquiring load demands, establishing an adaptability function of each power electronic transformer through a time sequence self-adaptive optimizing strategy, establishing a decomposed load demand, optimizing control parameters, carrying out load collaborative optimizing analysis, and acquiring a control parameter optimizing result; and controlling the corresponding power electronic transformer. The invention solves the technical problems that the traditional transformer control method is often dependent on manual experience to set parameters, which not only has low efficiency, but also is difficult to adapt to the rapid change of the running state of the power grid.
Description
Technical Field
The invention relates to the technical field of power electronic control, in particular to a power electronic transformer control method based on a self-adaptive control strategy.
Background
With the continuous development of power systems, the use of power electronic transformers in power grids is more and more widespread, however, some technical problems still exist for the control of the power electronic transformers, on the one hand, complex association relations exist among transformers in the power systems, and the relations influence the running state and efficiency of the power systems, but the traditional analysis methods often have difficulty in comprehensively understanding and optimizing the relations, so that the management and control of the systems are difficult; on the other hand, the load demand of the power system has the characteristic of dynamic change, and the power grid load continuously fluctuates along with the change of the user demand and environmental factors, and the dynamic change brings challenges to the load management and operation optimization of the system.
Disclosure of Invention
The application provides a power electronic transformer control method based on a self-adaptive control strategy, and aims to solve the technical problems that the traditional transformer control method is often dependent on manual experience to set parameters, so that the efficiency is low, and the system is difficult to adapt to the rapid change of the running state of a power grid, so that the stability and the adaptability of the system are poor.
In view of the above, the present application provides a power electronic transformer control method based on an adaptive control strategy.
The application discloses a power electronic transformer control method based on a self-adaptive control strategy, which comprises the following steps: executing data collection of the power electronic transformers in the target area, wherein the collected data comprise connection information of each power electronic transformer and power grid line information, analyzing the connection relation of the power electronic transformers according to data collection results based on graph theory principles, and establishing transformation topological structure characteristics; performing association analysis on each power electronic transformer through the transformation topological structure characteristics, and establishing M transformer control networks, wherein each transformer control network at least controls one power electronic transformer; deploying monitoring sensor groups according to M transformer control networks, and establishing a mapping relation between the monitoring sensor groups and corresponding transformer control networks, wherein sensors in the monitoring sensor groups comprise voltage sensors, current sensors and temperature sensors; collecting real-time monitoring data of the monitoring sensor group through a data acquisition system, predicting the power grid state according to the real-time monitoring data and the power transmission line task through a prediction model built in the data acquisition system, and establishing a time sequence self-adaptive optimizing strategy according to a power grid state prediction result; acquiring load demands, wherein the load demands are load demands of M transformer control networks, establishing fitness functions of all power electronic transformers through a time sequence self-adaptive optimizing strategy, establishing a decomposed load demand, optimizing control parameters of the power electronic transformers in the network through the fitness functions and the decomposed load demands, carrying out load collaborative optimizing analysis through the load demands and the M transformer control networks, and acquiring control parameter optimizing results according to load collaborative optimizing analysis results; and controlling the corresponding power electronic transformer based on the control parameter optimizing result.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
Through data collection and graph theory principle analysis, connection relation analysis between power electronic transformers is carried out, transformation topological structure characteristics are established, and visualization and understanding of the system are improved; dividing a plurality of power electronic transformers according to the characteristics of a transformation topological structure, monitoring key parameters such as input voltage, output voltage, current, temperature and the like of a plurality of transformer control networks in real time, evaluating the state and load requirements of the transformer control networks, optimizing the control parameters of the power electronic transformers based on an intelligent optimization algorithm according to the state and load requirements of a power grid, realizing accurate control and performance optimization of the power electronic transformers, and improving control precision and efficiency; by means of the prediction model and the self-adaptive optimizing strategy, real-time prediction of the power grid state and dynamic adjustment of control parameters are achieved, and response speed and adaptability of the system to load fluctuation are improved. In general, the power electronic transformer control method based on the self-adaptive control strategy effectively solves the problems of network topology analysis, load demand optimization, predictive self-adaptive control and the like in the traditional power system, and achieves the technical effects of improving the stability and the adaptability of the system, improving the response speed of the system and the like.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
Fig. 1 is a schematic flow chart of a power electronic transformer control method based on an adaptive control strategy according to an embodiment of the present application;
fig. 2 is a schematic flow chart of establishing fitness functions of each power electronic transformer in a power electronic transformer control method based on an adaptive control strategy according to an embodiment of the present application.
Detailed Description
The embodiment of the application solves the technical problems that the traditional transformer control method is often dependent on manual experience to set parameters, has low efficiency, is difficult to adapt to the rapid change of the running state of a power grid, and causes poor system stability and adaptability by providing the power electronic transformer control method based on the self-adaptive control strategy.
Having described the basic principles of the present application, various non-limiting embodiments of the present application will now be described in detail with reference to the accompanying drawings.
As shown in fig. 1, an embodiment of the present application provides a power electronic transformer control method based on an adaptive control strategy, where the method includes:
step one: and executing data collection of the power electronic transformers in the target area, wherein the collected data comprise connection information and power grid line information of each power electronic transformer, analyzing the connection relation of the power electronic transformers according to the data collection result based on a graph theory principle, and establishing a transformation topological structure characteristic.
Specifically, the range and the number of the power electronic transformers in the target area are determined, connection information of each power electronic transformer is collected, the connection information comprises input and output ports, connection lines and the like, and grid line information is collected, the grid connection condition among the power electronic transformers is included.
And converting the collected data into a graph structure in graph theory, wherein the power electronic transformers are represented as nodes in the graph, the connection relations are represented as edges in the graph, analyzing the connection relations among the power electronic transformers by using graph theory algorithms such as a shortest path algorithm, a minimum spanning tree algorithm and the like, determining main connection paths and key nodes, and establishing transformation topological structure characteristics of the power electronic transformers according to graph theory analysis results, wherein the transformation topological structure characteristics comprise information such as the connection relations among the main power electronic transformers, main characteristics of a power grid topological structure, node importance and the like.
Step two: and carrying out association analysis on each power electronic transformer through the transformation topological structure characteristics, and establishing M transformer control networks, wherein each transformer control network at least controls one power electronic transformer.
Specifically, by utilizing the established topological structure characteristics of the transformation, the association relation among the power electronic transformers is analyzed to determine which power electronic transformers have a tighter connection relation or which transformers have a key control relation. According to the result of the association analysis, M transformer control networks are constructed, M is the number of the transformer control networks, the specific numerical value is determined according to the actual situation, the transformer control networks are of a graph structure, wherein nodes represent power electronic transformers, edges represent control relations or connection relations among the transformers, and each transformer control network at least controls one power electronic transformer, so that the stability and controllability of the system can be ensured.
Step three: and deploying monitoring sensor groups according to the M transformer control networks, and establishing a mapping relation between the monitoring sensor groups and the corresponding transformer control networks, wherein the sensors in the monitoring sensor groups comprise voltage sensors, current sensors and temperature sensors.
Specifically, according to the system demand and the monitoring purpose, a corresponding monitoring sensor group is selected, wherein the monitoring sensor group comprises a voltage sensor, a current sensor and a temperature sensor, so that the voltage, the current and the temperature parameters of the power electronic transformer are monitored in real time. For each power electronic transformer, a corresponding monitoring sensor is deployed to comprehensively monitor the working state and performance parameters of the transformer. Mapping the deployed monitoring sensor group with the established M transformer control networks, so that each sensor is connected to the corresponding transformer control network to realize data acquisition and transmission.
Step four: and collecting real-time monitoring data of the monitoring sensor group through a data acquisition system, predicting the power grid state according to the real-time monitoring data and the power transmission line task through a prediction model built in the data acquisition system, and establishing a time sequence self-adaptive optimizing strategy according to a power grid state prediction result.
Specifically, the data of the monitoring sensor group is collected in real time through the data acquisition system, and the real-time monitoring data of parameters such as voltage, current and temperature are included. The method comprises the steps of utilizing a prediction model built in a data acquisition system to perform prediction analysis on a power grid state according to real-time monitoring data and power transmission line tasks, wherein the prediction model can use methods such as machine learning, statistical analysis and the like to predict the running state of the power grid according to historical data and real-time monitoring data, including power requirements, load conditions, power loss and the like, and establishing a time sequence self-adaptive optimizing strategy according to a power grid state prediction result, wherein the efficiency strategy mainly focuses on the energy utilization efficiency of the system, and the stability strategy mainly focuses on the running stability of the system, and the two strategies can be realized by adjusting the efficiency and the stable weight factors. The optimal control of the system is realized through a time sequence self-adaptive optimizing strategy, so that the system can reach an optimal balance point between efficiency and stability under different running states.
Step five: the method comprises the steps of obtaining load demands, wherein the load demands are load demands of M transformer control networks, building fitness functions of all power electronic transformers through a time sequence self-adaptive optimizing strategy, building decomposed load demands, optimizing control parameters of the power electronic transformers in the network through the fitness functions and the decomposed load demands, carrying out load collaborative optimizing analysis through the load demands and the M transformer control networks, and obtaining control parameter optimizing results according to load collaborative optimizing analysis results.
Specifically, real-time load demand data is obtained from M transformer control networks, where the data includes information such as power demand, etc. of load devices to which the respective transformers are connected. And decomposing the obtained overall load demand by analyzing the load equipment connected with each transformer control network and the power demand thereof, namely subdividing the load demand into the load demands required by each transformer control network, and obtaining the decomposed load demand.
According to the time sequence self-adaptive optimizing strategy, an adaptability function is established for each power electronic transformer, the adaptability function integrates factors such as efficiency and stability, and the adaptability of each transformer can be determined according to load requirements and factor weights. And optimizing control parameters of the power electronic transformer in the network by decomposing load demands by utilizing the established fitness function, wherein the optimizing process is performed for the decomposed conditions of various load demands, and the goal is to maximize the fitness of the system under different load conditions.
And carrying out load collaborative optimization analysis on the control parameter optimizing result, the load requirements and the M transformer control networks, wherein the analysis process integrates the control parameters of each transformer under different load conditions, the synergies and the influences among transformers in the network, and a final control parameter optimizing result is obtained according to the load collaborative optimization analysis result, and covers the system adaptability, stability and efficiency under different load conditions, so that the optimal control parameters of each transformer are determined.
Step six: and controlling the corresponding power electronic transformer based on the control parameter optimizing result.
Specifically, according to the optimizing result of the control parameters, the control parameters of each power electronic transformer are set, the set control parameters are applied to the corresponding power electronic transformers, parameter adjustment is carried out, control of the power electronic transformers is achieved, the running condition of the power electronic transformers is continuously monitored, and parameter adjustment and control optimization are carried out according to requirements, so that the stability of the system is guaranteed.
Further, as shown in fig. 2, the establishing the fitness function of each power electronic transformer through the timing sequence adaptive optimization strategy further includes:
Carrying out digital modeling on the power electronic transformer, and establishing a digital model, wherein the digital model is established through experimental data and mathematical modeling; acquiring a use data set of the power electronic transformer, performing use simulation of the use data set through the digital model, extracting key parameters of the transformer based on a use simulation result, and establishing weight coefficients of the key parameters; reconstructing constraint weights through the weight coefficients and the time sequence self-adaptive optimizing strategy; and establishing the fitness function of each power electronic transformer based on the constraint weight.
In particular, experimental data of the power electronic transformer are collected, including parameters of input voltage, output voltage, input current, output current, temperature, etc., which can be obtained through laboratory measurements or existing data recording systems. Based on the collected experimental data, a digital model of the power electronic transformer is established by utilizing a mathematical modeling method, namely, the power electronic transformer is modeled as a combination of circuit elements, model parameters are obtained by a circuit analysis method, the experimental data are synchronized to the model, and the digital model of the power electronic transformer is obtained, and the established digital model is used for simulation of a power system.
And collecting actual use data sets of the power electronic transformer, wherein the actual use data sets comprise input voltage, output voltage, input current, output current, temperature and other parameters under different working conditions. Based on the established digital model, the collected actual use data set is used for simulation through simulation software, simulation results are analyzed, key power electronic transformer parameters including efficiency, stability, response speed, loss and the like are extracted, and according to the simulation results and actual requirements, which parameters have the greatest influence on the system performance are determined, so that key parameters are determined. And establishing a weight coefficient for each parameter according to the importance of the key parameter, wherein the weight coefficient reflects the contribution degree of different parameters in the system performance.
Based on the weight coefficients and policies, constraint weights in the system are readjusted, for example, higher stability is required, then stability weights are increased, and efficiency weights are reduced, so that optimal control and operation effects of the system under different operation states are achieved.
And determining weight coefficients of all indexes in the fitness function according to the established constraint weights, combining the determined key indexes and the weight coefficients into a mathematical expression, constructing the fitness function, and applying the established fitness function to evaluate the adaptability of all power electronic transformers.
Further, the establishing the fitness function of each power electronic transformer based on the constraint weight further includes:
The fitness function is constructed as follows:
；
wherein, Is the firstThe fitness function of the individual power electronic transformers,Respectively characterize the firstTemperature constraint weights, efficiency constraint weights and stability constraint weights for the individual power electronic transformers,As a general temperature evaluation function of the power electronic transformer,, For the maximum score to be the maximum score,In order to be able to operate at the actual operating temperature,In order to achieve a desired operating temperature,As a general efficiency evaluation function of power electronic transformers,Is a general stable evaluation function of the power electronic transformer.
Specifically, the fitness function constructed is as follows:
；
Wherein the fitness function Represent the firstThe degree of adaptation of the individual power electronic transformers, the higher the value of this is, meaning that the better the transformer operates under various constraints; general temperature evaluation functionEvaluating the temperature condition of the transformer according to the actual operating temperature, the ideal operating temperature and the maximum score; general efficiency evaluation functionFor evaluating the efficiency of the power electronic transformer, the efficiency can be calculated from the ratio between the actual output power and the rated power; general stability evaluation functionFor evaluating the stability of a power electronic transformer, a stability score may be calculated based on factors such as the output waveform, voltage stability, and frequency stability of the system.
By integrating the above evaluation functions, the fitness function integrates the constraints of temperature, efficiency and stability, and weights the constraints through the weight coefficient, so that the fitness function can comprehensively evaluate the performance of the power electronic transformer in multiple aspects, and help the system to select optimal control parameters.
Further, the load collaborative optimization analysis is performed through the load requirement and the M transformer control networks, and the control parameter optimization result is obtained according to the load collaborative optimization analysis result, which further includes:
Performing collaborative analysis on the control parameter optimizing result based on the load demand, and establishing an adaptability comprehensive value under various optimizing schemes; performing punishment trigger analysis of the power electronic transformer under corresponding control parameters for each optimizing scheme, and establishing punishment trigger factors; and punishing the fitness comprehensive value through the punishment trigger factor, and carrying out adaptive screening based on a punishment result to generate a load collaborative optimizing analysis result.
Specifically, a plurality of different control parameter optimizing schemes are established according to load demands, and a control parameter optimizing process is executed according to each optimizing scheme to obtain a plurality of different optimizing results which reflect the performance of the system under different schemes.
According to the load demand and the simulation result, evaluating the performance of the system under each optimizing scheme, evaluating indexes such as system efficiency, stability, response speed and the like, and establishing an adaptability comprehensive value of each optimizing scheme based on the evaluation result, wherein the comprehensive value can comprehensively consider the weight of each index, for example, the weighted evaluation value of each index can be calculated firstly in a weighted summation mode, and then all the values are summed to obtain the adaptability comprehensive value of the scheme.
Conditions triggering punishment are determined, for example, conditions that the system performance is lower than a certain threshold value, the system is abnormal or fails, the load requirement cannot be met, and the like are set. According to the determined trigger conditions, establishing corresponding punishment trigger rules, for example, triggering efficiency punishment when the system efficiency is lower than a certain threshold value; when a problem occurs in system stability, a stability penalty is triggered. According to punishment trigger rules and actual running conditions, punishment trigger factors of the power electronic transformer under corresponding control parameters under each optimizing scheme are calculated, wherein the punishment trigger factors are numerical values, represent the degree of triggering punishment under specific conditions, and can be obtained through linear function calculation.
According to the established punishment trigger factors, punishment degrees of different trigger factors on the fitness comprehensive values are determined, punishment coefficients are determined, the punishment trigger factors are applied to the fitness comprehensive values under each optimizing scheme, punishment results of each scheme are calculated, for example, the fitness comprehensive values of the optimizing schemes are multiplied by the punishment trigger factors, and then the punishment coefficients are multiplied, so that punishment results are obtained according to the calculation results.
And carrying out adaptive screening on each optimizing scheme according to the punishment result obtained by calculation, namely selecting a scheme corresponding to the maximum value in the punishment result as the load collaborative optimizing analysis result so as to realize the optimal operation of the system under different load demands.
Furthermore, the collaborative analysis of the control parameter optimizing result based on the load demand establishes an adaptability comprehensive value under a plurality of optimizing schemes, and the method further comprises the following steps:
Establishing power electronic transformer parameter synchronization constraints in the M transformer control networks based on the load demands; parameter adaptation adjustment is carried out on the control parameter optimizing result through the parameter synchronization constraint of the power electronic transformer, and fitness value calculation is carried out again according to the parameter adaptation adjusting result; and generating the fitness comprehensive value under various optimizing schemes according to the all updated fitness value calculation results.
In particular, synchronous power electronic transformer parameters are determined, the important focus is on phase and frequency, the parameters are important for stable operation and load coordination of a power system, wherein phase synchronization can ensure that all components of the system operate under the same phase condition, and frequency synchronization can ensure that the system operates under the same frequency. Based on the determined synchronization constraint target, establishing a corresponding synchronization constraint rule, for example, for phase synchronization, providing that the phase difference of all transformer outputs does not exceed a certain threshold; for frequency synchronization, it is provided that the output frequency of all transformers should be within an allowable range, thereby ensuring synchronous operation of the system in phase and frequency.
And carrying out adaptation adjustment on control parameters in the optimizing result according to the parameter synchronization constraint rule of the power electronic transformer, wherein the adaptation adjustment comprises adjustment of the output phase and frequency of the transformer so as to meet the requirement of the synchronization constraint rule, and after parameter adaptation adjustment is completed, carrying out adaptation value calculation on the adjusted parameters again, namely substituting the adjusted parameters into an adaptation function to carry out calculation, so as to obtain a new adaptation value.
And (3) re-calculating the fitness comprehensive value according to the all updated fitness value calculation results, wherein the calculation process is the same as that of the previous steps, the fitness comprehensive value under various optimization schemes is obtained through calculation, and the optimal optimization scheme is selected according to the fitness comprehensive value, so that the optimal control and operation of the system are realized.
Further, the method further comprises:
Performing network importance evaluation on the M transformer control networks based on the load demand, and configuring M early warning trigger thresholds according to network importance evaluation results; and executing early warning monitoring of the corresponding M transformer control networks through the M early warning trigger thresholds, and executing exception management according to the trigger results of the early warning monitoring.
Specifically, importance indexes for evaluating the M transformer control networks are determined, including the load rate of the transformers, the system frequency stability, the transformer temperature, the load balancing, the system response time and the like. Based on the determined importance indexes, M transformer control networks are evaluated, early warning trigger thresholds of all the importance indexes are determined according to the network importance evaluation results, the thresholds can be set according to actual conditions and system requirements, for example, the thresholds are divided into two levels of warning thresholds and warning thresholds, the warning thresholds represent that the system is in a safer state, and the system needs to be monitored and potential problems are prevented; the alarm threshold value indicates that the system has abnormal or near abnormal state and needs to take measures in time for treatment.
The system can monitor key indexes of each transformer in real time, compare the key indexes with pre-configured pre-alarm trigger thresholds, continuously monitor the operation state and key indexes of each transformer through the pre-alarm monitoring system, and trigger pre-alarm signals when the index of one transformer exceeds the pre-set pre-alarm trigger threshold. When the early warning signal is received, preset abnormal management measures, such as system parameter adjustment, standby equipment switching, energy source scheduling and the like, are automatically executed according to the triggered early warning signal, so that the safety and stability of the system operation are ensured.
Further, the method further comprises:
When any transformer control network has early warning abnormality, judging whether the corresponding transformer control network controls the backup power electronic transformer; if the backup power electronic transformer exists, executing control takeover of the backup power electronic transformer, and reporting the equipment abnormality; and if the backup power electronic transformer does not exist, executing cooperative dynamic load distribution of the internal power electronic transformer, and reporting the equipment abnormality.
Specifically, when an early warning abnormality occurs in a certain transformer control network, whether the corresponding transformer control network has a backup power electronic transformer or not is judged according to a preset backup strategy and system design.
If the backup transformer exists, executing control takeover of the backup power electronic transformer, and in the control takeover process, stably taking over the system load by the backup transformer, so as to ensure continuous power supply and stable operation of the system.
If the backup power electronic transformer does not exist, executing a cooperative dynamic load distribution strategy of the internal power electronic transformer according to the current load demand and the state of each power electronic transformer, wherein the cooperative dynamic load distribution strategy comprises dynamically adjusting the load distribution proportion of each transformer according to the load condition and the performance characteristics of the transformer so as to realize load balancing and stable system operation, generating an equipment abnormality report after executing the cooperative dynamic load distribution of the internal power electronic transformer, wherein the report content comprises information such as abnormality occurrence time, abnormality reason, processing measures and the like so as to facilitate subsequent fault analysis and processing.
Further, the method further comprises:
Recording equipment abnormality, and reconstructing a maintenance inspection strategy according to equipment abnormality frequency; and carrying out maintenance inspection management on the power electronic transformer through the reconstructed maintenance inspection strategy.
Specifically, specific information of each equipment abnormality is recorded, including abnormality occurrence time, abnormality type, abnormality cause, processing measures and the like, the recorded equipment abnormality information is counted, and the frequency and occurrence mode of the equipment abnormality are calculated. Based on the analysis results of the abnormal frequency and the occurrence mode, reevaluating and reconstructing the maintenance inspection strategy, including adjusting the inspection frequency, for example, increasing the inspection frequency for equipment with high abnormal frequency or equipment with obvious abnormal mode, so as to ensure the timely discovery and processing of the abnormality; optimizing the inspection content, for example, adjusting the inspection content and method according to the type and cause of the abnormality, and enhancing the monitoring and inspection of the part or index where the abnormality may occur.
And carrying out maintenance inspection management on the power electronic transformer according to the reconstructed maintenance inspection strategy, and finding out abnormal conditions in time, so as to realize effective management and maintenance on the power electronic transformer and improve the reliability and continuous operability of the equipment.
In summary, the power electronic transformer control method based on the adaptive control strategy provided by the embodiment of the application has the following technical effects:
1. Through data collection and graph theory principle analysis, connection relation analysis between power electronic transformers is carried out, transformation topological structure characteristics are established, and visualization and understanding of the system are improved;
2. Dividing a plurality of power electronic transformers according to the characteristics of a transformation topological structure, monitoring key parameters such as input voltage, output voltage, current, temperature and the like of a plurality of transformer control networks in real time, evaluating the state and load requirements of the transformer control networks, optimizing the control parameters of the power electronic transformers based on an intelligent optimization algorithm according to the state and load requirements of a power grid, realizing accurate control and performance optimization of the power electronic transformers, and improving control precision and efficiency;
3. By means of the prediction model and the self-adaptive optimizing strategy, real-time prediction of the power grid state and dynamic adjustment of control parameters are achieved, and response speed and adaptability of the system to environmental changes and load fluctuation are improved.
In general, the power electronic transformer control method based on the self-adaptive control strategy effectively solves the problems of network topology analysis, load demand optimization, predictive self-adaptive control and the like in the traditional power system, and achieves the technical effects of improving the stability and the adaptability of the system, improving the response speed of the system and the like.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (6)
1. A power electronic transformer control method based on an adaptive control strategy, the method comprising:
Executing data collection of the power electronic transformers in the target area, wherein the collected data comprise connection information of each power electronic transformer and power grid line information, analyzing the connection relation of the power electronic transformers according to data collection results based on graph theory principles, and establishing transformation topological structure characteristics;
Performing association analysis on each power electronic transformer through the transformation topological structure characteristics, and establishing M transformer control networks, wherein each transformer control network at least controls one power electronic transformer;
Deploying monitoring sensor groups according to M transformer control networks, and establishing a mapping relation between the monitoring sensor groups and corresponding transformer control networks, wherein sensors in the monitoring sensor groups comprise voltage sensors, current sensors and temperature sensors;
Collecting real-time monitoring data of the monitoring sensor group through a data acquisition system, predicting a power grid state according to the real-time monitoring data and a power transmission line task through a prediction model built in the data acquisition system, and establishing a time sequence self-adaptive optimizing strategy according to a power grid state prediction result, wherein the time sequence self-adaptive optimizing strategy comprises an efficiency strategy and a stability strategy, the efficiency strategy focuses on the energy utilization efficiency of the system, the stability strategy focuses on the running stability of the system, and the efficiency strategy and the stability strategy are realized by adjusting efficiency and stable weight factors in the fitness functions of all power electronic transformers;
Acquiring load requirements, establishing decomposition load requirements, wherein the load requirements are load requirements of M transformer control networks, the decomposition load requirements are load requirements required by each transformer control network, establishing fitness functions of each power electronic transformer through a time sequence self-adaptive optimizing strategy, optimizing control parameters of the power electronic transformers in the network through the fitness functions and the decomposition load requirements, specifically, performing fitness calculation on the control parameters of the power electronic transformers in the network based on the decomposition load requirements by utilizing the fitness functions, acquiring control parameter optimizing results corresponding to the highest fitness of the system under different load conditions, performing load collaborative optimizing analysis through the load requirements and the M transformer control networks, and acquiring control parameter optimizing results according to the load collaborative optimizing analysis results;
Based on the control parameter optimizing result, controlling the corresponding power electronic transformer;
the step of establishing the fitness function of each power electronic transformer through the time sequence self-adaptive optimizing strategy further comprises the following steps:
carrying out digital modeling on the power electronic transformer, and establishing a digital model, wherein the digital model is established through experimental data and mathematical modeling;
Acquiring a use data set of the power electronic transformer, performing use simulation of the use data set through the digital model, extracting key parameters of the transformer based on a use simulation result, and establishing weight coefficients of the key parameters;
Reconstructing constraint weights through the weight coefficients and the time sequence self-adaptive optimizing strategy;
Establishing an adaptability function of each power electronic transformer based on the constraint weight;
The establishing the fitness function of each power electronic transformer based on the constraint weight further comprises:
The fitness function is constructed as follows:
；
wherein, Is the firstThe fitness function of the individual power electronic transformers,Respectively characterize the firstTemperature constraint weights, efficiency constraint weights and stability constraint weights for the individual power electronic transformers,As a general temperature evaluation function of the power electronic transformer,,For the maximum score to be the maximum score,In order to be able to operate at the actual operating temperature,In order to achieve a desired operating temperature,As a general efficiency evaluation function of power electronic transformers,Is a general stable evaluation function of the power electronic transformer.
2. The method of claim 1, wherein the load co-optimizing analysis is performed through the load demand and the M transformer control networks, and the control parameter optimizing result is obtained according to the load co-optimizing analysis result, further comprising:
Performing collaborative analysis on the control parameter optimizing result based on the load demand, and establishing an adaptability comprehensive value under various optimizing schemes;
Performing punishment trigger analysis of the power electronic transformer under corresponding control parameters for each optimizing scheme, and establishing punishment trigger factors;
And punishing the fitness comprehensive value through the punishment trigger factor, and carrying out adaptive screening based on a punishment result to generate a load collaborative optimizing analysis result.
3. The method of claim 1, wherein the collaborative analysis of control parameter optimization results based on the load demand establishes a fitness composite value under a plurality of optimization schemes, further comprising:
Establishing power electronic transformer parameter synchronization constraints in the M transformer control networks based on the load demands;
Parameter adaptation adjustment is carried out on the control parameter optimizing result through the parameter synchronization constraint of the power electronic transformer, and fitness value calculation is carried out again according to the parameter adaptation adjusting result;
and generating the fitness comprehensive value under various optimizing schemes according to the all updated fitness value calculation results.
4. The method of claim 1, wherein the method further comprises:
Performing network importance evaluation on the M transformer control networks based on the load demand, and configuring M early warning trigger thresholds according to network importance evaluation results;
And executing early warning monitoring of the corresponding M transformer control networks through the M early warning trigger thresholds, and executing exception management according to the trigger results of the early warning monitoring.
5. The method of claim 4, wherein the method further comprises:
When any transformer control network has early warning abnormality, judging whether the corresponding transformer control network controls the backup power electronic transformer;
If the backup power electronic transformer exists, executing control takeover of the backup power electronic transformer, and reporting the equipment abnormality;
and if the backup power electronic transformer does not exist, executing cooperative dynamic load distribution of the internal power electronic transformer, and reporting the equipment abnormality.
6. The method of claim 4, wherein the method further comprises:
recording equipment abnormality, and reconstructing a maintenance inspection strategy according to equipment abnormality frequency;
And carrying out maintenance inspection management on the power electronic transformer through the reconstructed maintenance inspection strategy.
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