CN117526344A - Reactive voltage control optimization method and system for power distribution network - Google Patents
Reactive voltage control optimization method and system for power distribution network Download PDFInfo
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Abstract
The invention discloses a reactive voltage control optimization method and a reactive voltage control optimization system for a power distribution network, which relate to the technical field of reactive voltage control and comprise the following steps: performing preliminary analysis on the power distribution network, analyzing reactive voltage and load characteristics, and setting an optimization target; based on a set optimization target, collecting power grid data in real time, and carrying out data analysis through a machine learning deep analysis model; designing a reactive voltage control strategy according to the analysis result, and optimizing the structure of the power distribution network; and updating the reactive voltage control strategy according to the monitoring result and the current power grid condition. The reactive voltage control optimization method for the power distribution network can improve the operation efficiency and stability of the power distribution network, and particularly under the condition of processing large-scale new energy access and high change load. Through predictive load management and real-time feedback adjustment, the invention can reduce voltage fluctuation and equipment faults and improve the overall performance and reliability of the power grid. In addition, the invention also enhances the coping capability of the power grid to extreme events and emergency situations.
Description
Technical Field
The invention relates to the technical field of reactive voltage control, in particular to a reactive voltage control optimization method and system for a power distribution network.
Background
In conventional distribution networks, a major concern is to maintain stable operation and voltage levels of the grid within a reasonable range. With the continuous change of new energy access and load demands, traditional voltage control strategies face more and more challenges, especially in terms of maintaining grid stability and efficient operation.
Conventional approaches typically rely on fixed control strategies and manual intervention, which appear to be inflexible and efficient in handling complex grid conditions and rapidly changing load demands. The problem of voltage instability caused by new energy access and fluctuation of demand cannot be effectively solved.
Therefore, there is a need for a reactive voltage control optimization method for a power distribution network, which provides a more flexible and adaptive voltage control strategy by utilizing big data analysis, machine learning and real-time monitoring technologies. And responding to the change of the condition of the power grid in real time, and predicting and solving the potential power grid problem in advance.
Disclosure of Invention
The present invention has been made in view of the above-described problems.
Therefore, the technical problems solved by the invention are as follows: the existing power distribution network control method has the problems of insufficient flexibility, incapability of effectively coping with the rapid change of load requirements and power grid stability caused by new energy access, and how to realize real-time, efficient and self-adaptive power grid optimization.
In order to solve the technical problems, the invention provides the following technical scheme: a power distribution network reactive voltage control optimization method, comprising:
performing preliminary analysis on the power distribution network, analyzing reactive voltage and load characteristics, and setting an optimization target;
based on a set optimization target, collecting power grid data in real time, and carrying out data analysis through a machine learning deep analysis model;
designing a reactive voltage control strategy according to the analysis result, and optimizing the structure of the power distribution network;
and updating the reactive voltage control strategy according to the monitoring result and the current power grid condition.
As a preferable scheme of the reactive voltage control optimization method of the power distribution network, the reactive voltage control optimization method comprises the following steps: the primary analysis comprises collecting configuration, historical fault records, maintenance logs, historical voltage and load data of the power distribution network, and identifying frequent voltage fluctuation, overload conditions and equipment fault rates in the historical performance data;
analyzing reactive voltage and load characteristics comprises the steps of analyzing reactive voltage fluctuation range, stability and load change modes of a power grid, and evaluating the influence of new energy access on the power grid;
the specific optimization targets comprise voltage stability optimization, load management optimization, reactive power control, new energy adaptability optimization and equipment performance optimization.
As a preferable scheme of the reactive voltage control optimization method of the power distribution network, the reactive voltage control optimization method comprises the following steps: the real-time collection of the power grid data comprises the steps of arranging sensors and data acquisition equipment, monitoring voltage, current, load and equipment state, establishing a communication network for real-time data transmission, and determining key nodes;
the equipment key nodes comprise historical fault records, maintenance logs, voltage and load data of the power distribution network are processed through a data analysis tool, abnormal modes and potential problem areas are identified through a statistical analysis and mode identification algorithm, voltage and load changes are simulated through power grid simulation software, areas with frequent voltage fluctuation or obvious load changes are identified, the influence of new energy access points on the areas is considered, the operation states and fault risks of key power grid equipment are evaluated through an equipment health monitoring system and a predictive maintenance tool, comprehensive analysis results are obtained, the importance of each node in the aspects of voltage stability, load management, new energy adaptability and equipment performance is comprehensively evaluated through a multi-factor decision analysis method, and the key nodes are determined.
As a preferable scheme of the reactive voltage control optimization method of the power distribution network, the reactive voltage control optimization method comprises the following steps: the reactive voltage control strategy includes determining a critical node characteristic, custom reactive voltage control strategy, denoted,
S(N)＝w v ·V(N)+w l ·L(N)+w e ·E(N)+w d ·D(N)
wherein N represents a key node, V (N) represents a voltage stability score of the node N, L (N) represents a load fluctuation score of the node N, E (N) represents an adaptability score of the node N to new energy access, D (N) represents an equipment performance score of the node N, and w v ,w l ,w e ,w d Weights representing voltage stability, load fluctuation, new energy adaptation capability, and device performance, S (N) represents the composite score of node N.
As a preferable scheme of the reactive voltage control optimization method of the power distribution network, the reactive voltage control optimization method comprises the following steps: determining key node characteristics, namely judging a first risk level when S (N) is less than 0.45, analyzing load fluctuation data of nodes, determining a fluctuation mode and a fluctuation reason, formulating a predictive load management strategy based on an analysis result, adjusting voltage regulating equipment, and automatically adjusting reactive power output according to real-time load change;
when S (N) is more than or equal to 0.45 and less than or equal to 0.85, judging the risk level as a second risk level, analyzing the voltage stability and reactive power demand of the node, determining reactive power compensation demand, formulating reactive power compensation strategy based on analysis result, re-evaluating and optimizing the power grid structure, re-evaluating the voltage stability after implementing structural optimization, analyzing the influence of new energy output on the power grid, and adjusting new energy access strategy;
and when S (N) is more than 0.85, judging the third risk level, implementing an emergency response plan, adding reactive compensation equipment, adjusting voltage regulating equipment, re-planning a power grid structure, after the power grid structure is reconstructed, evaluating the overall performance of the power grid, building a monitoring system, and continuously monitoring the state of the power grid in the key region after the power grid is reconstructed.
As a preferable scheme of the reactive voltage control optimization method of the power distribution network, the reactive voltage control optimization method comprises the following steps: the method comprises the steps of formulating a predictive load management strategy, wherein the predictive load management strategy comprises the steps of analyzing historical load data of key nodes, identifying a load fluctuation mode and a fluctuation reason;
the evaluation and optimization of the power grid structure comprises the steps of analyzing the current topological structure of the power grid, identifying an overload line or a voltage unstable region, determining the optimal position of a reactive compensation device by using an optimization algorithm, simulating an optimization scheme of the optimal position by using power grid simulation software, and adjusting by combining simulation test results;
the re-planning of the power grid structure comprises the steps of designing a new power grid structure plan, carrying out reconstruction measures on key nodes, monitoring reconstruction effects in the implementation process, and evaluating the performance of the whole power grid.
As a preferable scheme of the reactive voltage control optimization method of the power distribution network, the reactive voltage control optimization method comprises the following steps: the method comprises the steps of continuously monitoring real-time data of a power grid, deeply analyzing the data collected from three risk level strategies by using a big data analysis tool, identifying a problem mode which cannot be solved by the power grid under different conditions by adopting a mode identification algorithm, visually presenting an analysis result by using a visualization tool, establishing a real-time feedback mechanism, feeding the analysis result back to a power grid control center in real time, monitoring voltage and load data of key nodes in real time by combining an Internet of things technology, comparing the voltage and load data with the analysis result, quickly identifying the condition deviating from a normal range, automatically sending an alarm to the control center when the system detects the problem mode, and providing an adjustment suggestion;
the adjustment proposal comprises the steps of continuously collecting and analyzing power grid data by utilizing a real-time data stream processing technology, performing advanced analysis on the real-time data by applying a convolutional neural network deep learning algorithm, predicting power grid risks possibly occurring in a short period, automatically adjusting a reactive voltage control strategy according to a prediction result of the deep learning algorithm, and integrating the automatic adjustment of a reinforcement learning self-adaptive algorithm to the power grid state in a power grid control system;
the automatic adjustment of the power grid state comprises the steps of monitoring power grid data in real time by utilizing a machine learning algorithm, rapidly identifying abnormal events of the power grid, deploying an automatic response system, immediately executing a preset response program when the abnormality is detected, dynamically adjusting reactive power output or switching power grid operation modes, analyzing the real-time state of the power grid and predicting future trend, providing an optimal response strategy based on the current power grid state and historical data when an emergency occurs, guiding operators or automatically executing decisions, simulating the behavior of the power grid under the extreme events by utilizing a PSCAD/EMTDC network simulation tool, evaluating the current elasticity level of the power grid, and identifying a key area for improving the elasticity of the power grid based on simulation results.
Another object of the present invention is to provide a reactive voltage control optimization system for a power distribution network, which can solve the shortcomings of the conventional power grid control method in terms of flexibility and coping with rapidly changing loads through advanced data analysis and real-time adaptive control strategies.
In order to solve the technical problems, the invention provides the following technical scheme: a power distribution network reactive voltage control optimization system, comprising: the system comprises a data analysis module, a data acquisition module, a voltage control module and a strategy updating module; the data analysis module is used for carrying out preliminary analysis on the power distribution network, analyzing reactive voltage and load characteristics and setting specific targets; the data acquisition module is used for collecting power grid data in real time based on a set target and carrying out data analysis through a machine learning deep analysis model; the voltage control module is used for designing a reactive voltage control strategy according to the analysis result and optimizing the structure of the power distribution network; and the strategy updating module is used for updating the reactive voltage control strategy according to the monitoring result and the current power grid condition.
A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method for optimizing reactive voltage control of a distribution network as described above.
A computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor implements the steps of a method for optimizing reactive voltage control of a power distribution network as described above.
The invention has the beneficial effects that: the reactive voltage control optimization method for the power distribution network can improve the operation efficiency and stability of the power distribution network, and particularly under the condition of processing large-scale new energy access and high change load. Through predictive load management and real-time feedback adjustment, the invention can reduce voltage fluctuation and equipment faults and improve the overall performance and reliability of the power grid. In addition, the invention also enhances the coping capability of the power grid to extreme events and emergency situations.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is an overall flowchart of a reactive voltage control optimization method for a power distribution network according to an embodiment of the present invention.
Fig. 2 is an overall structure diagram of a reactive voltage control optimization system for a power distribution network according to a second embodiment of the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Example 1
Referring to fig. 1, for one embodiment of the present invention, there is provided a reactive voltage control optimization method for a power distribution network, including:
performing preliminary analysis on the power distribution network, analyzing reactive voltage and load characteristics, and setting an optimization target;
based on a set optimization target, collecting power grid data in real time, and carrying out data analysis through a machine learning deep analysis model;
designing a reactive voltage control strategy according to the analysis result, and optimizing the structure of the power distribution network;
and updating the reactive voltage control strategy according to the monitoring result and the current power grid condition.
The primary analysis comprises the steps of collecting configuration, historical fault records, maintenance logs, historical voltage and load data of the power distribution network, and identifying frequent voltage fluctuation, overload conditions and equipment fault rate in the historical performance data;
analyzing reactive voltage and load characteristics comprises analyzing reactive voltage fluctuation range, stability and load change modes of the power grid, and evaluating the influence of new energy access on the power grid.
The specific optimization targets are set to comprise voltage stability optimization, load management optimization, reactive power control, new energy adaptability optimization and equipment performance optimization.
The method comprises the steps of collecting power grid data in real time, including arranging sensors and data acquisition equipment, monitoring voltage, current, load and equipment state, establishing a communication network to transmit the data in real time, and determining key nodes;
the key nodes of the equipment comprise historical fault records, maintenance logs, voltage and load data of the power distribution network are processed through a data analysis tool, abnormal modes and potential problem areas are identified through a statistical analysis and mode identification algorithm, basic data are provided for voltage stability and equipment performance assessment, voltage and load changes are simulated through power grid simulation software, areas with frequent voltage fluctuation or obvious load changes are identified, the influence of new energy access points on the areas is considered, the operation state and fault risk of key power grid equipment are assessed through an equipment health monitoring system and a predictive maintenance tool, the physical basic stability of the power grid is ensured, comprehensive analysis results are obtained, the importance of each node in the aspects of voltage stability, load management, new energy adaptability and equipment performance is comprehensively assessed through a multi-factor decision analysis method, and the key nodes are determined.
The key node determination method comprises the following steps: and collecting historical fault records, maintenance logs, voltage and load data of the power distribution network. The data should include time stamps, fault types, fault durations, scope of influence, repair measures, etc. Preliminary statistical analyses, such as failure frequency, average maintenance time, voltage and load fluctuations, were performed using data analysis software. The fault and maintenance data is processed using an anomaly detection machine learning algorithm to identify anomaly patterns and potential problem areas. The voltage and load data are analyzed to identify areas where fluctuations are frequent or significant. And according to the algorithm result, identifying the areas with high failure rate, frequent maintenance requirements, serious voltage fluctuation or severe load change in the power grid. The operation of the grid is simulated using grid simulation software, with particular attention being paid to the identified problem areas. The simulation includes the influence of a new energy access point, and the influence of the new energy access point on voltage stability and load distribution is analyzed. The impact of new energy output fluctuations on the problem area, especially during peak load periods and unstable climatic conditions, was evaluated. The equipment health monitoring system comprises an on-line monitoring device, and is used for tracking the operation state of key equipment (including transformers and circuit breakers) in real time. And collecting equipment performance data including temperature, vibration and load current by utilizing the internet of things technology. Using predictive maintenance software, the collected equipment data is analyzed based on a machine-learned fault prediction model to predict possible faults and maintenance requirements. And comprehensively evaluating the importance of each node in the aspects of voltage stability, load management, new energy adaptability and equipment performance by adopting a multi-factor decision analysis method of a decision tree. And (5) incorporating the pattern recognition result, the power grid simulation data and the equipment health monitoring information into the evaluation. And determining key nodes in the power grid according to the comprehensive evaluation result.
The reactive voltage control strategy includes determining a critical node characteristic, custom reactive voltage control strategy, denoted,
S(N)＝w v ·V(N)+w l ·L(N)+w e ·E(N)+w d ·D(N)
wherein N represents a key node, V (N) represents a voltage stability score of the node N, L (N) represents a load fluctuation score of the node N, E (N) represents an adaptability score of the node N to new energy access, D (N) represents an equipment performance score of the node N, and w v ,w l ,w e ,w d Weights representing voltage stability, load fluctuation, new energy adaptation capability, and device performance, S (N) represents the composite score of node N.
Determining key node characteristics comprises judging a first risk level when S (N) is less than 0.45, analyzing load fluctuation data of nodes, determining a fluctuation mode and a fluctuation reason, formulating a predictive load management strategy based on an analysis result, adjusting voltage regulating equipment, and automatically adjusting reactive power output according to real-time load change;
when S (N) is more than or equal to 0.45 and less than or equal to 0.85, judging the risk level as a second risk level, analyzing the voltage stability and reactive power demand of the node, determining reactive power compensation demand, formulating reactive power compensation strategy based on analysis result, re-evaluating and optimizing the power grid structure, re-evaluating the voltage stability after implementing structural optimization, analyzing the influence of new energy output on the power grid, and adjusting new energy access strategy;
and when S (N) is more than 0.85, judging the third risk level, implementing an emergency response plan, adding reactive compensation equipment, adjusting voltage regulating equipment, re-planning a power grid structure, after the power grid structure is reconstructed, evaluating the overall performance of the power grid, building a monitoring system, and continuously monitoring the state of the power grid in the key region after the power grid is reconstructed.
The method comprises the steps of formulating a predictive load management strategy, wherein the predictive load management strategy comprises the steps of analyzing historical load data of key nodes, identifying a mode of load fluctuation, identifying a fluctuation reason and formulating the predictive load management strategy.
Load data including peak load, valley load and average load was collected for the last 3 years of critical nodes. The periodicity, trending and randomness of the load data were analyzed using a time series analysis statistical method. Cluster analysis or anomaly detection algorithms are applied to identify different load fluctuation patterns. Based on the result of pattern recognition, regular load fluctuations and abnormal load fluctuations including seasonal changes and fluctuations caused by sudden events are recognized. The reasons for load fluctuations are analyzed in combination with local events (e.g. holidays, large activities) and weather conditions (e.g. high temperature, cold weather). Judging result: if certain load fluctuations are associated with a particular event or weather condition, a predictable fluctuation is marked; otherwise, the mark is randomly fluctuated. And (3) based on the analysis result, a load prediction model is formulated to predict short-term and long-term load changes. And formulating an adjustment strategy of the voltage regulating equipment and reactive power output according to the output of the load prediction model. The predictive load management strategy is implemented in actual grid operation. And according to the deviation of the actual operation data and the prediction result, the prediction model and the load management strategy are timely adjusted so as to improve the accuracy and the efficiency.
The current topology of the urban power grid is analyzed by using a power grid analysis tool, and the lines near the load center and at the new energy access point are concerned. Line overload conditions and voltage ripple frequencies are detected. If the lines of the area are frequently overloaded or voltage fluctuations are frequent, they are marked as potential weaknesses. Based on the identified potential weaknesses, an optimization algorithm is used to determine the optimal position and capacity of the reactive compensation device. If voltage problems frequently occur in a certain area, reactive compensation is preferentially added in the area; if the line near the center of the load is prone to overload, compensation devices are added or optimized in these areas. And simulating an optimization scheme by using power grid simulation software, and observing the change of voltage and load. If the simulation result shows that the voltage stability and the load distribution are improved, continuing to implement the scheme; if the simulation result is not ideal, returning to the reactive compensation position optimization step for adjustment.
The power grid is comprehensively evaluated, and old equipment and circuits and new energy access points are concerned. Analysis of which older equipment and lines are most prone to failure determines the focus of the reconstruction of these areas. And carrying out power grid reconstruction aiming at the identified key areas, wherein the power grid reconstruction comprises equipment updating, transformer substation adding or line transformation. The reconstruction effect is monitored during the implementation. If the reconstruction improves the performance and the stability, continuing to process; if a new problem occurs, the reconstruction plan is re-evaluated. And after the reconstruction is completed, evaluating the overall performance of the power grid reconstruction area. If the performance evaluation shows that the stability and the efficiency of the power grid are obviously improved, a long-term monitoring system is established; if the effect is not as expected, further adjustments or optimizations are considered.
The method comprises the steps of updating reactive voltage control strategies, namely continuously monitoring real-time data of a power grid, deeply analyzing the data collected from three risk level strategies by using a big data analysis tool, identifying a failure problem mode of the power grid under different conditions by adopting a mode identification algorithm (such as a random forest or a support vector machine), visually presenting an analysis result by using a visualization tool, establishing a real-time feedback mechanism, feeding the analysis result back to a power grid control center in real time, monitoring voltage and load data of key nodes in real time by combining an Internet of things technology, comparing the voltage and load data with the analysis result, quickly identifying the condition of deviation from a normal range, automatically sending an alarm to the control center when the system detects the failure mode, and providing an adjustment suggestion;
the adjustment proposal comprises the steps of continuously collecting and analyzing the power grid data by utilizing a real-time data stream processing technology, performing advanced analysis on the real-time data by applying a convolutional neural network deep learning algorithm, predicting the power grid risk possibly occurring in a short period, automatically adjusting a reactive voltage control strategy according to the prediction result of the deep learning algorithm, and integrating the automatic adjustment of the power grid state by a reinforcement learning self-adaptive algorithm in a power grid control system;
the automatic adjustment of the power grid state comprises the steps of utilizing a machine learning algorithm, including an anomaly detection algorithm and time sequence analysis, monitoring power grid data in real time, quickly identifying power grid anomaly events, deploying an automatic response system, immediately executing a preset response program when anomaly is detected, dynamically adjusting reactive power output or switching power grid operation modes, analyzing the real-time state of the power grid and predicting future trend, providing an optimal response strategy based on the current power grid state and historical data when an emergency occurs, guiding an operator or automatically executing a decision, utilizing a PSCAD/EMTDC network simulation tool to simulate the behavior of the power grid under extreme events, evaluating the current elasticity level of the power grid, identifying key areas for improving the power grid elasticity, including equipment needing enhanced connection points or easy damage, integrating micro-grids and distributed energy resources, including solar photovoltaic, wind power generation and energy storage systems, into the power grid, improving the adaptability of the power grid to load fluctuation and power supply interruption, implementing intelligent power grid technologies, such as demand response and load management, and enhancing the response capability and self-recovery capability of the power grid to changing conditions.
Example 2
Referring to fig. 2, for one embodiment of the present invention, there is provided a reactive voltage control optimization system for a power distribution network, including:
the system comprises a data analysis module, a data acquisition module, a voltage control module and a strategy updating module.
The data analysis module is used for carrying out preliminary analysis on the power distribution network, analyzing reactive voltage and load characteristics and setting specific targets.
The data acquisition module is used for collecting power grid data in real time based on a set target, and carrying out data analysis through the machine learning deep analysis model.
The voltage control module is used for designing a reactive voltage control strategy according to the analysis result and optimizing the structure of the power distribution network.
And the strategy updating module is used for updating the reactive voltage control strategy according to the monitoring result and the current power grid condition.
Example 3
One embodiment of the present invention, which is different from the first two embodiments, is:
the functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only memory (ROM), a random access memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: electrical connection (electronic device), portable computer disk cartridge (magnetic device), random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (eeprom) with one or more wiring
(EPROM or flash memory), fiber optic means, and portable Compact Disc Read Only Memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Example 4
For one embodiment of the invention, a reactive voltage control optimization method of a power distribution network is provided, and in order to verify the beneficial effects of the invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.
And (3) a power grid model: a standard power distribution network model comprising a plurality of power stations, load points and new energy access points is constructed.
Test situation: different operation scenes are set, including high load demand, rapid load change and new energy fluctuation.
And simulating the operation of the power distribution network by using MATLAB power grid simulation software, and respectively applying the traditional reactive voltage control method and the optimization method to carry out a series of simulation experiments.
The voltage stability, fault response time, energy consumption efficiency, new energy adaptability and system operation stability of the two methods under different conditions are recorded and compared, and the experimental results are shown in table 1.
Table 1 comparison table of experimental data
The method can more effectively cope with load fluctuation and instability of new energy output. Reliable operation of the power grid is ensured, voltage-related faults are avoided, power supply is more stable and reliable, and the risk of equipment damage is reduced. The problems in the power grid can be rapidly identified and solved, so that the influence of faults on the operation of the power grid is reduced. The quick response not only improves the operation efficiency of the power grid, but also is beneficial to reducing the loss and inconvenience caused by faults and improving the user satisfaction. The method has the effects of optimizing load distribution and voltage regulation, reducing operation cost and reducing energy waste. The higher energy efficiency also means that the impact of the grid on the environment is reduced, meeting the goal of sustainable development. With the wide application of renewable energy sources, the method of the invention has the advantages of adapting to new energy source fluctuation, and the excellent new energy source adaptability ensures that the power grid can effectively utilize the renewable energy sources and simultaneously keeps stable operation. Advantages in maintaining the overall stability of the grid, especially when faced with complex and variable operating conditions.
The higher system stability score also means that the grid is more resistant to external disturbances and internal faults, guaranteeing the continuity and safety of the power supply. According to the design and expected data of the theoretical experiments, compared with the traditional method, the reactive voltage control optimization method of the power distribution network has obvious improvement in a plurality of key aspects. By combining big data analysis, machine learning and real-time monitoring technologies, the invention not only improves the running efficiency and stability of the power grid, but also enhances the adaptability of the power grid to new energy fluctuation and the quick response capability to faults. These improvements make the method of the present invention significantly advantageous in ensuring stable operation of the grid, improving energy efficiency, and adapting to future trends in the grid.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (10)
1. The reactive voltage control optimization method for the power distribution network is characterized by comprising the following steps of:
performing preliminary analysis on the power distribution network, analyzing reactive voltage and load characteristics, and setting an optimization target;
based on a set optimization target, collecting power grid data in real time, and carrying out data analysis through a machine learning deep analysis model;
designing a reactive voltage control strategy according to the analysis result, and optimizing the structure of the power distribution network;
and updating the reactive voltage control strategy according to the monitoring result and the current power grid condition.
2. The power distribution network reactive voltage control optimization method according to claim 1, wherein: the primary analysis comprises collecting configuration, historical fault records, maintenance logs, historical voltage and load data of the power distribution network, and identifying frequent voltage fluctuation, overload conditions and equipment fault rates in the historical performance data;
analyzing reactive voltage and load characteristics comprises the steps of analyzing reactive voltage fluctuation range, stability and load change modes of a power grid, and evaluating the influence of new energy access on the power grid;
the specific optimization targets comprise voltage stability optimization, load management optimization, reactive power control, new energy adaptability optimization and equipment performance optimization.
3. The power distribution network reactive voltage control optimization method according to claim 2, wherein: the real-time collection of the power grid data comprises the steps of arranging sensors and data acquisition equipment, monitoring voltage, current, load and equipment state, establishing a communication network for real-time data transmission, and determining key nodes;
the equipment key nodes comprise historical fault records, maintenance logs, voltage and load data of the power distribution network are processed through a data analysis tool, abnormal modes and potential problem areas are identified through a statistical analysis and mode identification algorithm, voltage and load changes are simulated through power grid simulation software, areas with frequent voltage fluctuation or obvious load changes are identified, the influence of new energy access points on the areas is considered, the operation states and fault risks of key power grid equipment are evaluated through an equipment health monitoring system and a predictive maintenance tool, comprehensive analysis results are obtained, the importance of each node in the aspects of voltage stability, load management, new energy adaptability and equipment performance is comprehensively evaluated through a multi-factor decision analysis method, and the key nodes are determined.
4. A method of optimizing reactive voltage control of a power distribution network as claimed in claim 3, wherein: the reactive voltage control strategy includes determining a critical node characteristic customized reactive voltage control strategy, expressed as S (N) =w v ·V(N)+w l ·L(N)+w e ·E(N)+w d ·D(N)
Wherein N represents a key node, V (N) represents a voltage stability score of the node N, L (N) represents a load fluctuation score of the node N, E (N) represents an adaptability score of the node N to new energy access, D (N) represents an equipment performance score of the node N, and w v ,w l ,w e ,w d Weights representing voltage stability, load fluctuation, new energy adaptation capability, and device performance, S (N) represents the composite score of node N.
5. The power distribution network reactive voltage control optimization method according to claim 4, wherein: determining key node characteristics, namely judging a first risk level when S (N) is less than 0.45, analyzing load fluctuation data of nodes, determining a fluctuation mode and a fluctuation reason, formulating a predictive load management strategy based on an analysis result, adjusting voltage regulating equipment, and automatically adjusting reactive power output according to real-time load change;
when S (N) is more than or equal to 0.45 and less than or equal to 0.85, judging the risk level as a second risk level, analyzing the voltage stability and reactive power demand of the node, determining reactive power compensation demand, formulating reactive power compensation strategy based on analysis result, re-evaluating and optimizing the power grid structure, re-evaluating the voltage stability after implementing structural optimization, analyzing the influence of new energy output on the power grid, and adjusting new energy access strategy;
and when S (N) is more than 0.85, judging the third risk level, implementing an emergency response plan, adding reactive compensation equipment, adjusting voltage regulating equipment, re-planning a power grid structure, after the power grid structure is reconstructed, evaluating the overall performance of the power grid, building a monitoring system, and continuously monitoring the state of the power grid in the key region after the power grid is reconstructed.
6. The power distribution network reactive voltage control optimization method according to claim 5, wherein: the method comprises the steps of formulating a predictive load management strategy, wherein the predictive load management strategy comprises the steps of analyzing historical load data of key nodes, identifying a load fluctuation mode and a fluctuation reason;
the evaluation and optimization of the power grid structure comprises the steps of analyzing the current topological structure of the power grid, identifying an overload line or a voltage unstable region, determining the optimal position of a reactive compensation device by using an optimization algorithm, simulating an optimization scheme of the optimal position by using power grid simulation software, and adjusting by combining simulation test results;
the re-planning of the power grid structure comprises the steps of designing a new power grid structure plan, carrying out reconstruction measures on key nodes, monitoring reconstruction effects in the implementation process, and evaluating the performance of the whole power grid.
7. The power distribution network reactive voltage control optimization method according to claim 6, wherein: the method comprises the steps of continuously monitoring real-time data of a power grid, deeply analyzing the data collected from three risk level strategies by using a big data analysis tool, identifying a problem mode which cannot be solved by the power grid under different conditions by adopting a mode identification algorithm, visually presenting an analysis result by using a visualization tool, establishing a real-time feedback mechanism, feeding the analysis result back to a power grid control center in real time, monitoring voltage and load data of key nodes in real time by combining an Internet of things technology, comparing the voltage and load data with the analysis result, quickly identifying the condition deviating from a normal range, automatically sending an alarm to the control center when the system detects the problem mode, and providing an adjustment suggestion;
the adjustment proposal comprises the steps of continuously collecting and analyzing power grid data by utilizing a real-time data stream processing technology, performing advanced analysis on the real-time data by applying a convolutional neural network deep learning algorithm, predicting power grid risks possibly occurring in a short period, automatically adjusting a reactive voltage control strategy according to a prediction result of the deep learning algorithm, and integrating the automatic adjustment of a reinforcement learning self-adaptive algorithm to the power grid state in a power grid control system;
the automatic adjustment of the power grid state comprises the steps of monitoring power grid data in real time by utilizing a machine learning algorithm, rapidly identifying abnormal events of the power grid, deploying an automatic response system, immediately executing a preset response program when the abnormality is detected, dynamically adjusting reactive power output or switching power grid operation modes, analyzing the real-time state of the power grid and predicting future trend, providing an optimal response strategy based on the current power grid state and historical data when an emergency occurs, guiding operators or automatically executing decisions, simulating the behavior of the power grid under the extreme events by utilizing a PSCAD/EMTDC network simulation tool, evaluating the current elasticity level of the power grid, and identifying a key area for improving the elasticity of the power grid based on simulation results.
8. A system employing the power distribution network reactive voltage control optimization method according to any one of claims 1 to 7, comprising: the system comprises a data analysis module, a data acquisition module, a voltage control module and a strategy updating module;
the data analysis module is used for carrying out preliminary analysis on the power distribution network, analyzing reactive voltage and load characteristics and setting specific targets;
the data acquisition module is used for collecting power grid data in real time based on a set target and carrying out data analysis through a machine learning deep analysis model;
the voltage control module is used for designing a reactive voltage control strategy according to the analysis result and optimizing the structure of the power distribution network;
and the strategy updating module is used for updating the reactive voltage control strategy according to the monitoring result and the current power grid condition.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the power distribution network reactive voltage control optimization method of any one of claims 1 to 7.
10. A computer readable storage medium having stored thereon a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the power distribution network reactive voltage control optimization method of any of claims 1 to 7.
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