A power distribution network stability control method and system for high-permeability distributed power supply

Abstract

This application provides a method and system for distribution network stability control with high-penetration distributed power sources, relating to the field of distribution network control technology. The method includes: real-time acquisition of electrical data from the distribution network operation and output information from distributed power sources; dynamic clustering and partitioning of the distribution network topology of the target distribution network; synchronous partitioning and analysis of edge control areas using distribution network stability control strategy channels; global collaborative optimization of regional control strategy parameters; and closed-loop control of distribution network stability through global control strategy parameters. This application addresses the technical problem in existing technologies where the lack of dynamic partitioning and regional coupling collaborative analysis mechanisms hinders the achievement of local and global coordinated optimization in distribution network control, further impacting operational stability and control accuracy under high-penetration distributed power source conditions. It achieves the technical goal of dynamic partitioning and global collaborative optimization, thereby improving stability and control accuracy.

Description

Technical Field

[0001] This application relates to the field of distribution network control technology, and in particular to a distribution network stability control method and system for high-penetration distributed power sources. Background Technology

[0002] With the large-scale integration of distributed power sources and the continuous increase in the proportion of new energy power generation, the operation mode of the distribution network is gradually changing from the traditional unidirectional power supply mode to the multi-source collaborative power supply mode. Problems such as voltage fluctuation, frequency deviation and power flow reversal in the distribution network are becoming increasingly prominent, which puts forward higher requirements for the stability control capability of the distribution network. At the same time, the complex topology and multi-source coupling characteristics make the operating state of the distribution network exhibit significant dynamism and uncertainty, and there is an urgent need to build an intelligent stability control method that can adapt to the environment of highly permeable distributed power sources.

[0003] Currently, most existing distribution network stability control methods adopt zonal control or centralized unified control based on fixed topology. In terms of zonal control, they usually rely on pre-set static zoning strategies and lack the ability to perceive and adaptively adjust to changes in the operating status of the distribution network in real time. In terms of centralized control, they focus on overall optimization while ignoring the dynamic differences and coupling effects between local areas. As a result, when the output of distributed power sources fluctuates greatly or the operating conditions change rapidly, the control strategy is difficult to respond to the actual operating needs in a timely manner. Moreover, the control coordination between different areas is insufficient, which can easily lead to situations where local regulation is effective but global performance degrades. It is difficult to meet the stable operation requirements under the condition of high proportion of distributed power source access.

[0004] In summary, the existing technology suffers from a lack of a collaborative analysis mechanism based on the dynamic zoning and inter-regional coupling relationships of the distribution network's real-time operating status. This makes it difficult to achieve coordinated optimization of local and global distribution network stability control strategies, further affecting the operational stability and control accuracy of the distribution network under conditions of high-penetration distributed power sources. Summary of the Invention

[0005] The purpose of this application is to provide a distribution network stability control method and system for high-penetration distributed power sources, in order to solve the technical problem in the prior art that the lack of a collaborative analysis mechanism based on the dynamic partitioning and inter-regional coupling relationship of the distribution network in real time operation status makes it difficult to achieve local and global coordination and optimization of distribution network stability control strategy, which further affects the operation stability and control accuracy of distribution network under high-penetration distributed power source conditions.

[0006] In view of the above problems, this application provides a method and system for distribution network stability control for high-penetration distributed power sources.

[0007] Firstly, this application provides a distribution network stability control method for high-penetration distributed power sources, implemented through a distribution network stability control system for high-penetration distributed power sources. The method includes: constructing a distribution network topology based on the structural design information of the target distribution network, and collecting real-time electrical data of the distribution network operation and output information of distributed power sources; dynamically clustering and dividing the distribution network topology according to the electrical data of the distribution network operation and the output information of distributed power sources to obtain Q edge control regions; establishing a distribution network stability control strategy channel, using the distribution network stability control strategy channel to synchronously partition and analyze the Q edge control regions to generate Q region control strategy parameters; performing global collaborative optimization on the Q region control strategy parameters to determine the global distribution network control strategy parameters, and performing closed-loop stability control of the distribution network using the global distribution network control strategy parameters.

[0008] Preferably, the distribution network stability control method for high-penetration distributed power sources further includes: performing data cleaning and feature extraction on the electrical data of the distribution network operation and the output information of the distributed power sources to obtain a distribution network electrical feature set and a power source output feature set; extracting P distribution network node information based on the distribution network topology; associating and arranging the distribution network electrical feature set and the power source output feature set with the P distribution network node information to construct P distribution network node feature vectors; and dynamically clustering the distribution network topology based on the P distribution network node feature vectors to obtain Q edge control regions, where P ≥ Q.

[0009] Preferably, the method for distribution network stability control for high-penetration distributed power sources further includes: performing node clustering analysis and intra-cluster sum of squares calculation on the distribution network topology network with different cluster numbers to obtain multiple intra-cluster sums of squares; identifying the inflection point of the rate of decline for the different cluster numbers according to the multiple intra-cluster sums of squares to determine the target number of clusters Q; sequentially calculating the distribution network node pair similarity matrix between the node features in the P distribution network node feature vectors; and performing dynamic clustering partitioning on the distribution network topology network based on the target number of clusters Q according to the distribution network node pair similarity matrix to obtain Q edge control regions.

[0010] Preferably, the method for distribution network stability control for high-penetration distributed power sources further includes: randomly selecting Q nodes in the distribution network topology as initial cluster centers according to the target cluster number Q; assigning the remaining nodes in the distribution network topology to the initial cluster centers with the highest similarity according to the distribution network node pair similarity matrix to obtain Q initial node clusters; recalculating the mean of the Q node feature vectors of the Q initial node clusters; and performing iterative clustering update of the distribution network topology based on the mean of the Q node feature vectors until a preset termination condition is met to obtain Q edge control regions.

[0011] Preferably, the distribution network stability control method for high-penetration distributed power sources further includes: collecting a historical stability control dataset of the distribution network, wherein the historical stability control dataset includes distribution network operation data, distributed power source output data, stability strategy control data, and stability control effect data; defining a distribution network stability control fitness function based on multiple objectives of distribution network stability control; using the distribution network stability control fitness function to evaluate and optimize the historical stability control dataset of the distribution network to obtain a feasible distribution network stability control dataset; and training strategy control based on the feasible distribution network stability control dataset to build a distribution network stability control strategy channel.

[0012] Preferably, the distribution network stability control method for high-penetration distributed power sources further includes: classifying the feasible distribution network stability control dataset into a voltage stability control strategy dataset, a frequency stability control strategy dataset, and a harmonic suppression control strategy dataset; training the voltage stability control strategy dataset, the frequency stability control strategy dataset, and the harmonic suppression control strategy dataset into policy control channels to generate voltage stability control strategy channels, frequency stability control strategy channels, and harmonic suppression control strategy channels; and merging the voltage stability control strategy channels, the frequency stability control strategy channels, and the harmonic suppression control strategy channels in parallel to build a distribution network stability control strategy channel.

[0013] Preferably, the distribution network stability control method for high-penetration distributed power sources further includes: performing correlation and impact analysis on the Q regional control strategy parameters based on the distribution network topology to generate a regional stability control directed influence network; and performing global collaborative optimization on the Q regional control strategy parameters according to the regional stability control directed influence network to determine the global control strategy parameters of the distribution network.

[0014] Preferably, the distribution network stability control method for high-penetration distributed power sources further includes: performing correlation impact analysis on the Q regional control strategy parameters based on the distribution network topology to obtain directed paths of influence of distribution network nodes; evaluating the degree of influence of the Q regional control strategy parameters according to the directed paths of influence of distribution network nodes to obtain a set of influence coefficients of distribution network nodes; and connecting the directed paths of influence of distribution network nodes based on the set of influence coefficients of distribution network nodes to generate a directed influence network for regional stability control.

[0015] Preferably, the distribution network stability control method for high-penetration distributed power sources further includes: performing global collaborative optimization of the Q regional control strategy parameters according to the regional stability control directed influence network to obtain global control strategy parameter thresholds; and using the distribution network stability control fitness function to evaluate and optimize the global control strategy parameter thresholds to determine the global control strategy parameters of the distribution network.

[0016] Secondly, this application also provides a distribution network stability control system for high-penetration distributed power sources, used to execute a distribution network stability control method for high-penetration distributed power sources as described in the first aspect, comprising: a data acquisition module, used to construct a distribution network topology network based on the structural design information of the target distribution network, and to collect distribution network operation electrical data and distributed power source output information in real time; an edge control region acquisition module, used to dynamically cluster and divide the distribution network topology network according to the distribution network operation electrical data and distributed power source output information to obtain Q edge control regions; a region control strategy parameter generation module, used to build a distribution network stability control strategy channel, and use the distribution network stability control strategy channel to synchronously partition and analyze the Q edge control regions to generate Q region control strategy parameters; and a closed-loop control module, used to perform global collaborative optimization of the Q region control strategy parameters, determine the global control strategy parameters of the distribution network, and perform distribution network stability closed-loop control through the global control strategy parameters of the distribution network.

[0017] The technical solution provided in this application has at least the following technical effects or advantages: by realizing the technical goal of dynamic clustering and partitioning, regional control strategy generation and global collaborative optimization of distribution network stability control based on distribution network operation electrical data and distributed power output information, the technical effect of improving the distribution network partition adaptive capability and regional control coordination capability, realizing the unification of local control and global optimization, and improving the stability, response speed and control accuracy of the distribution network in complex operating environments is achieved.

[0018] The above description is merely an overview of the technical solution of this application. To enable a clearer understanding of the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating a distribution network stability control method for high-penetration distributed power sources, as proposed in this application.

[0021] Figure 2 This is a schematic diagram of the distribution network stability control system for high-penetration distributed power sources proposed in this application.

[0022] Figure labeling: 1. Data acquisition module; 2. Edge control area acquisition module; 3. Area control strategy parameter generation module; 4. Closed-loop control module. Detailed Implementation

[0023] This application provides a distribution network stability control method and system for high-penetration distributed power sources. It addresses the technical problem in existing technologies where the lack of a collaborative analysis mechanism based on dynamic zoning and inter-regional coupling relationships under real-time distribution network operating conditions hinders the achievement of coordinated optimization of local and global stability control strategies, further impacting the operational stability and control accuracy of the distribution network under high-penetration distributed power source conditions. The method achieves the technical objectives of dynamic clustering and zoning based on distribution network operating electrical data and distributed power source output information, regional control strategy generation, and global collaborative optimization. This enhances the distribution network's adaptive capabilities and regional control coordination capabilities, unifies local control and global optimization, and improves the stability, response speed, and control accuracy of the distribution network under complex operating environments.

[0024] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0025] Example 1, please refer to the appendix. Figure 1 This application provides a distribution network stability control method for high-penetration distributed power sources, applied to a distribution network stability control system for high-penetration distributed power sources, specifically including the following steps: Based on the structural design information of the target distribution network, a distribution network topology network is constructed, and electrical data of distribution network operation and output information of distributed power sources are collected in real time.

[0026] Specifically, the structural design information of the target distribution network is obtained. This information includes basic data such as the connection relationships of various electrical devices, node locations, line parameters, and switch states. By modeling this structural design information, a distribution network topology reflecting electrical connections and energy transmission paths is established. This topology describes the connection structure between nodes and the energy flow paths, providing fundamental structural support for subsequent operation analysis and control.

[0027] Furthermore, after the distribution network topology is constructed, measuring devices deployed within the distribution network collect real-time electrical data on its operation. This data includes key parameters reflecting the network's operating status, such as node voltage, current, power, frequency, and phase angle, which characterize the network's dynamic electrical properties at different operating times. Additionally, distributed generation power output information is synchronously acquired by connecting to a distributed generation monitoring unit. This information includes the active power, reactive power, and output fluctuation characteristics of distributed generation units such as photovoltaic, wind, and energy storage units, reflecting the real-time power injection and fluctuation behavior of distributed generation within the distribution network.

[0028] The distribution network topology is dynamically clustered and divided according to the electrical data of the distribution network operation and the output information of distributed power sources to obtain Q edge control regions.

[0029] Furthermore, this application also includes: performing data cleaning and feature extraction on the electrical data of the power distribution network operation and the output information of distributed power sources to obtain a power distribution network electrical feature set and a power source output feature set; extracting P power distribution network node information based on the power distribution network topology; associating and arranging the power distribution network electrical feature set and the power source output feature set with the P power distribution network node information to construct P power distribution network node feature vectors; and dynamically clustering and dividing the power distribution network topology based on the P power distribution network node feature vectors to obtain Q edge control regions, where P ≥ Q.

[0030] Furthermore, this application also includes: performing node clustering analysis and intra-cluster sum of squares calculation on the distribution network topology network with different cluster numbers to obtain multiple intra-cluster sums of squares; identifying the inflection point of the rate of decline of the different cluster numbers according to the multiple intra-cluster sums of squares of squares to determine the target number of clusters Q; sequentially calculating the distribution network node pair similarity matrix between the node features in the P distribution network node feature vectors; and performing dynamic clustering partitioning on the distribution network topology network based on the target number of clusters Q according to the distribution network node pair similarity matrix to obtain Q edge control regions.

[0031] Furthermore, this application also includes: randomly selecting Q nodes in the distribution network topology as initial cluster centers according to the target cluster number Q; assigning the remaining nodes in the distribution network topology to the initial cluster centers with the highest similarity according to the distribution network node pair similarity matrix to obtain Q initial node clusters; recalculating the mean of the Q node feature vectors of the Q initial node clusters; performing iterative clustering update of the distribution network topology based on the mean of the Q node feature vectors until a preset termination condition is met to obtain Q edge control regions.

[0032] Specifically, data cleaning and feature extraction are performed on electrical data of the distribution network operation and power output information of distributed generation sources. Data cleaning includes removing, correcting, and completing missing values, outliers, and noisy data generated during the acquisition process to improve data quality and consistency. Feature extraction involves extracting representative statistical features, time-series features, and trend features from raw data such as voltage, current, active power, reactive power, frequency, and distributed generation output fluctuations, thereby forming a distribution network electrical feature set and a power output feature set that can reflect the operating status of the distribution network and the power generation output behavior.

[0033] Furthermore, based on the structural relationship of the distribution network topology, each node in the network is analyzed and identified, and P distribution network node information is extracted. The distribution network node information includes node number, node type, node connection relationship and corresponding electrical attributes, which are used to identify functional units and their interconnection structure in different locations in the distribution network, providing a basic carrier for feature mapping and subsequent analysis.

[0034] Furthermore, the electrical feature set and power output feature set of the distribution network are associated and arranged with the information of P distribution network nodes according to the time dimension and spatial node correspondence, so that each node corresponds to a complete set of operating feature data. By combining and encoding the multi-dimensional features corresponding to each node, P distribution network node feature vectors are constructed. The distribution network node feature vectors are used to uniformly describe the electrical state of the node and the influence characteristics of distributed power sources at a specific time.

[0035] Subsequently, different numbers of clusters were selected to perform node clustering analysis and intra-cluster sum of squares calculation on the distribution network topology. Different numbers of clusters represent multiple partitioning schemes that divide the node set into different numbers of categories. Node clustering analysis is used to group nodes based on the distance or similarity relationship between node feature vectors. Intra-cluster sum of squares represents the cumulative value of the squared distances between all nodes in each cluster and the corresponding cluster center, which is used to measure the density of nodes in the same cluster. By calculating the intra-cluster sum of squares for different numbers of clusters, multiple cluster intra-cluster sum of squares indicators for evaluating the clustering effect were obtained.

[0036] Furthermore, the inflection point of the rate of decrease is identified for different numbers of clusters based on the sum of squares within multiple clusters. The rate of decrease is used to describe the trend of the sum of squares within clusters decreasing as the number of clusters increases. By analyzing the change curves, the position where the rate of decrease changes from rapid decrease to gradual change is identified as the inflection point, and the corresponding number of clusters is determined as the target number of clusters Q, so as to achieve a balance between clustering accuracy and computational complexity.

[0037] Then, the similarity matrix between the features of each node in the feature vectors of the P distribution network nodes is calculated in turn. The feature vectors of the distribution network nodes contain multi-dimensional feature parameters that reflect the operating status of the nodes. By using distance metric or similarity function to calculate the similarity between any two node feature vectors, the similarity between the node pairs is obtained and organized in matrix form to form a similarity matrix of distribution network nodes used to describe the relationship between all nodes.

[0038] Subsequently, Q nodes are randomly selected from the distribution network topology as initial cluster centers based on the target cluster number Q. Here, the target cluster number Q represents the number of pre-determined node classification categories. The distribution network topology contains multiple sets of nodes with electrical connections. Q representative nodes are randomly selected from the set of nodes as initial cluster centers to provide initial reference positions for subsequent node classification.

[0039] Furthermore, the remaining nodes in the distribution network topology are assigned according to the distribution network node pair similarity matrix. The distribution network node pair similarity matrix is ​​used to describe the degree of similarity between the features of any two nodes. By calculating the similarity between each node that is not selected as an initial cluster center and each initial cluster center, the node is assigned to the category corresponding to the initial cluster center with the highest similarity, thereby forming Q initial node clusters. The initial node cluster represents the set of node groups obtained based on the current partitioning result.

[0040] Then, the mean of the node feature vectors is calculated for each of the Q initial node clusters. The node feature vector is a multi-dimensional feature set used to describe the running state of the nodes. By averaging the feature vectors of all nodes in the same node cluster in one dimension, the central feature representation of the corresponding node cluster is obtained, forming the updated mean of the Q node feature vectors, which is used to reflect the overall feature distribution of each node cluster.

[0041] Subsequently, based on the mean of the updated Q node feature vectors, iterative clustering updates are performed on the distribution network topology. By repeatedly executing the node allocation and mean update process, the node partitioning results continuously converge towards directions with higher feature similarity until a preset termination condition is met. The preset termination condition includes that the node partitioning results no longer change or the change amplitude of the cluster centers is less than a set threshold, thereby obtaining stable Q edge control regions and achieving the final clustering partitioning of the node set. In one embodiment, the change amplitude of the cluster centers is represented by the mean of the Euclidean distance between the feature vectors of the corresponding cluster center nodes in two adjacent iterations. Let C be the feature vector of the i-th cluster center node obtained in the t-th iteration. i (t), where C is the feature vector of the i-th cluster center node obtained in the (t+1)-th iteration. i (t+1), then the change in cluster centers Δ is expressed as: Δ=(1 / Q)×Σ||C i (t+1)-C i (t)||. Where Q represents the target number of clusters. When the change in cluster centers Δ is less than a preset convergence threshold, the clustering process is considered to have reached convergence. Preferably, the preset convergence threshold is 0.001 to 0.01 after normalization of the node feature vector; more preferably, the preset convergence threshold is 0.005. Alternatively, when the node assignment results obtained from two consecutive iterations are completely consistent, the node partitioning result is considered to no longer change. To avoid infinite iteration under abnormal operating conditions, a maximum number of iterations N is set. max As an auxiliary termination condition, when the number of iterations reaches N max Clustering calculations are stopped when N is reached; preferably, N max Take 50-300; more preferably, N max Take 100.

[0042] A distribution network stability control strategy channel is established, and the Q edge control areas are synchronously partitioned and parsed using the distribution network stability control strategy channel to generate Q area control strategy parameters.

[0043] Furthermore, this application also includes: collecting a historical stability control dataset of the distribution network, wherein the historical stability control dataset includes distribution network operation data, distributed power generation output data, stability strategy control data, and stability control effect data; defining a fitness function for distribution network stability control based on multiple objectives of distribution network stability control; using the fitness function for distribution network stability control to evaluate and optimize the historical stability control dataset to obtain a feasible distribution network stability control dataset; and training strategy control based on the feasible distribution network stability control dataset to build a distribution network stability control strategy channel.

[0044] Furthermore, this application also includes: classifying the feasible distribution network stability control dataset into strategies to obtain a voltage stability control strategy dataset, a frequency stability control strategy dataset, and a harmonic suppression control strategy dataset; training the voltage stability control strategy dataset, the frequency stability control strategy dataset, and the harmonic suppression control strategy dataset into strategy control to generate a voltage stability control strategy channel, a frequency stability control strategy channel, and a harmonic suppression control strategy channel; and merging the voltage stability control strategy channel, the frequency stability control strategy channel, and the harmonic suppression control strategy channel in parallel to build a distribution network stability control strategy channel.

[0045] Specifically, a historical stability control dataset for the distribution network is collected. This dataset includes multi-dimensional data recorded during past operations. Distribution network operation data reflects the operating status parameters of each node, such as voltage, current, active power, reactive power, and frequency. Distributed power generation output data describes the power output and fluctuation of photovoltaic, wind power, and energy storage units. Stability strategy control data records the control measures and parameter configurations adopted under different operating conditions. Stability control effect data characterizes the changes in voltage deviation, frequency fluctuation, harmonic levels, and system stability indicators after the implementation of corresponding control measures. By uniformly collecting and organizing multiple types of data, a structured historical stability control data foundation is formed.

[0046] Furthermore, based on the multi-objective of distribution network stability control, a fitness function for distribution network stability control is defined. The multi-objective of distribution network stability control includes multiple evaluation dimensions such as voltage stability, frequency stability, harmonic suppression level, and system operation economy. By weighting and combining various evaluation indicators or using multi-objective collaborative modeling, a fitness function expression for comprehensively evaluating the merits of control strategies is constructed, making different control strategies comparable under a unified evaluation standard.

[0047] Furthermore, the fitness function of distribution network stability control is used to evaluate and optimize the historical stability control dataset of distribution network. By substituting the control strategies and corresponding effect data in the historical data into the fitness function for calculation, the fitness evaluation results of each group of data are obtained. Based on the evaluation results, the data are screened, retaining data samples with high fitness and good stability performance, and removing data samples with poor performance or instability, thereby obtaining a feasible distribution network stability control dataset for subsequent modeling and training.

[0048] Subsequently, the feasible distribution network stability control dataset is classified into strategies. The feasible distribution network stability control dataset is a high-quality data set obtained after fitness evaluation and screening. The data is divided according to the type of control target and the object of control action. Data involving voltage regulation behavior is classified into voltage stability control strategy dataset, data involving frequency regulation behavior is classified into frequency stability control strategy dataset, and control behavior data used to suppress harmonic distortion is classified into harmonic suppression control strategy dataset. This achieves data decoupling and structured organization for different stability control targets.

[0049] Furthermore, policy control training is performed on the voltage stability control policy dataset, frequency stability control policy dataset, and harmonic suppression control policy dataset, respectively. The policy control training includes model learning using the mapping relationship between historical operating states and corresponding control policies, and establishing the functional relationship between input features and output control parameters through supervised learning, optimization solution, or rule extraction. This generates voltage stability control policy channels, frequency stability control policy channels, and harmonic suppression control policy channels, enabling different types of control tasks to have independent modeling and response capabilities.

[0050] Furthermore, the voltage stability control strategy channel, frequency stability control strategy channel, and harmonic suppression control strategy channel are connected and merged in parallel. Parallel fusion means that each strategy channel receives unified input data at the same time and outputs corresponding control results independently. By integrating and coordinating the output results of multiple channels, a unified distribution network stability control strategy channel is constructed, enabling different control objectives to work together under the same control framework and achieving the integration of multi-dimensional stability control capabilities.

[0051] A distribution network stability control strategy channel is used to synchronously partition and analyze Q edge control areas. Synchronous partition analysis means processing the operating status of each edge control area simultaneously at the same time scale. By ensuring the time consistency of data input and the parallelism of processing in each area, the control strategy generation process between different areas remains coordinated, thereby avoiding control deviations caused by time differences and improving the overall control response speed. Furthermore, during the synchronous partition analysis process, based on the electrical characteristics and distributed power output characteristics of each edge control area, the distribution network stability control strategy channel outputs corresponding control results, forming Q area control strategy parameters. These parameters include voltage regulation parameters, frequency regulation parameters, and harmonic suppression parameters for each edge control area, which guide the equipment within the area to execute specific control actions.

[0052] The control strategy parameters of the Q regions are globally coordinated and optimized to determine the global control strategy parameters of the distribution network, and the distribution network stability closed-loop control is performed through the global control strategy parameters of the distribution network.

[0053] Furthermore, this application also includes: performing correlation and impact analysis on the Q regional control strategy parameters based on the distribution network topology to generate a regional stability control directed influence network; and performing global collaborative optimization on the Q regional control strategy parameters according to the regional stability control directed influence network to determine the global control strategy parameters of the distribution network.

[0054] Furthermore, this application also includes: performing correlation impact analysis on the Q regional control strategy parameters based on the distribution network topology to obtain the directed paths of distribution network node influence; evaluating the degree of influence of the Q regional control strategy parameters according to the directed paths of distribution network node influence to obtain a set of distribution network node influence coefficients; and connecting the directed paths of distribution network node influence based on the set of distribution network node influence coefficients to generate a regional stability control directed influence network.

[0055] Furthermore, this application also includes: performing global collaborative optimization of the Q regional control strategy parameters according to the regional stability control directed influence network to obtain global control strategy parameter thresholds; and using the distribution network stability control fitness function to evaluate and solve the global control strategy parameter thresholds to determine the global control strategy parameters of the distribution network.

[0056] Specifically, based on the distribution network topology, the correlation and impact analysis of Q regional control strategy parameters is performed to obtain the directed paths of influence of distribution network nodes. The distribution network topology is used to characterize the connection relationship between nodes and lines in the distribution network and the direction of power transmission. The Q regional control strategy parameters are the sets of control parameters corresponding to different edge control regions. By combining the topology and electrical coupling characteristics, the propagation process of the impact of different regional control strategies between nodes is analyzed, and the transmission path and direction of control action in the network are identified, thereby obtaining the directed paths of influence of distribution network nodes to describe the propagation relationship of influence.

[0057] Furthermore, the influence of the control strategy parameters of Q regions is assessed according to the directed paths of the distribution network nodes. The influence assessment is carried out by quantifying the strength of the control strategy on the voltage, power distribution and frequency changes of the nodes on different paths, calculating the influence weight of each path, and statistically analyzing the comprehensive influence of multiple paths to form a set of distribution network node influence coefficients. The set of distribution network node influence coefficients is used to represent the degree and scope of influence of different nodes under the control strategy.

[0058] Furthermore, the directed paths of influence of distribution network nodes are connected in a network based on the set of influence coefficients of distribution network nodes. The network connection is a weighted combination and structured connection of each influence path according to the node influence coefficient, so that the paths form a network structure with hierarchical relationship and weight distribution, thereby generating a directed influence network for regional stability control, which is used to comprehensively reflect the influence relationship and propagation mechanism between control strategies in various regions.

[0059] Global collaborative optimization of Q regional control strategy parameters is performed using a regional stability control directed influence network to obtain global control strategy parameter thresholds. The regional stability control directed influence network describes the influence direction and intensity relationship between control strategies in different edge control regions. The Q regional control strategy parameters represent the set of local control parameters generated for each edge control region. By jointly adjusting the regional control strategy parameters under the constraints of the directed influence network, the control behavior of each region can meet local stability requirements while taking into account the overall coupling relationship. This yields the range of control parameter values ​​that reflects the global coordination state, i.e., the global control strategy parameter thresholds, which are used to limit the feasible value range of each control parameter under the global optimal coordination condition.

[0060] Furthermore, the fitness function of distribution network stability control is used to evaluate and optimize the threshold values ​​of global control strategy parameters. The fitness function comprehensively evaluates multi-dimensional indicators such as voltage stability, frequency stability, harmonic suppression effect, and operational performance. By substituting the global control strategy parameter threshold values ​​into the fitness function, the performance of different parameter combinations within the threshold range is evaluated. An optimization algorithm iteratively adjusts the parameters to search for the optimal parameter solution that meets the multi-objective optimization requirements, thereby determining the global control strategy parameters of the distribution network. In one embodiment, the optimization algorithm uses a particle swarm optimization algorithm for optimization. First, M particles are randomly generated within the global control strategy parameter threshold range as initial solutions, each particle corresponding to a set of global control strategy parameter combinations. Then, each set of parameter combinations is substituted into the fitness function of distribution network stability control to obtain a fitness value. The optimal position of the individual particle and the optimal position of the group are updated based on the fitness value. Subsequently, the particles are iteratively updated according to the particle velocity update formula and the position update formula: V i (k+1)=ωV i (k)+c1r1(P besti -X i (k))+c2r2(G best -X i (k)), X i (k+1)=X i (k)+V i (k+1), where V i (k) represents the particle velocity at the k-th iteration, X i (k) represents the particle position at the k-th iteration, ω represents the inertia weight, c1 and c2 represent the learning factors, r1 and r2 represent random numbers between 0 and 1, and P besti G represents the best historical position of an individual particle. best This represents the global historical optimal position. The optimization calculation terminates when the fitness change rate is less than 0.1% for two consecutive iterations or when the maximum number of iterations is reached. The parameter combination corresponding to the current globally optimal particle is output as the global control strategy parameters for the distribution network. Preferably, the number of particles M is 30–100, the inertia weight ω is 0.4–0.9, and the learning factors c1 and c2 are both 1.5–2.5.

[0061] Distribution network stability closed-loop control is implemented through global control strategy parameters. This closed-loop control is a dynamic control process based on a feedback mechanism. During control execution, electrical data of the distribution network operation and output information of distributed generation sources are continuously collected. The real-time operating state is compared and analyzed with the target stable state. Based on the deviation results, the control strategy parameters are dynamically corrected, thus forming a cyclical control structure consisting of state perception, strategy generation, control execution, and feedback correction. Furthermore, during the closed-loop control process, by continuously iterating and updating the control parameters, key operating indicators such as distribution network voltage, frequency, and harmonics gradually approach a stable range. At the same time, the disturbance impact of distributed generation output fluctuations on the system is suppressed, thereby improving the overall operational stability and regulation accuracy of the distribution network.

[0062] In summary, the distribution network stability control method for high-penetration distributed power sources provided in this application has the following technical effects: by realizing the technical objectives of dynamic clustering and partitioning based on distribution network operation electrical data and distributed power source output information, generating regional control strategies and achieving global collaborative optimization, the method enhances the distribution network's regional adaptive capability and regional control coordination capability, achieves the unification of local control and global optimization, and improves the stability, response speed and control accuracy of the distribution network in complex operating environments.

[0063] Example 2: Based on the same inventive concept as the distribution network stability control method for high-penetration distributed power sources in the foregoing examples, this application also provides a distribution network stability control system for high-penetration distributed power sources. Please refer to the appendix. Figure 2 The system includes: a data acquisition module 1, used to construct a distribution network topology based on the structural design information of the target distribution network, and to collect distribution network operation electrical data and distributed power generation output information in real time; an edge control region acquisition module 2, used to dynamically cluster and divide the distribution network topology according to the distribution network operation electrical data and distributed power generation output information to obtain Q edge control regions; a region control strategy parameter generation module 3, used to build a distribution network stability control strategy channel, and to use the distribution network stability control strategy channel to perform synchronous partitioning and parsing of the Q edge control regions to generate Q region control strategy parameters; and a closed-loop control module 4, used to perform global collaborative optimization of the Q region control strategy parameters, determine the global control strategy parameters of the distribution network, and perform distribution network stability closed-loop control through the global control strategy parameters of the distribution network.

[0064] Furthermore, the aforementioned distribution network stability control system for high-penetration distributed power sources is also used for: performing data cleaning and feature extraction on the electrical data of the distribution network operation and the output information of the distributed power sources to obtain a distribution network electrical feature set and a power source output feature set; extracting P distribution network node information based on the distribution network topology; associating and arranging the distribution network electrical feature set and the power source output feature set with the P distribution network node information to construct P distribution network node feature vectors; and dynamically clustering and dividing the distribution network topology based on the P distribution network node feature vectors to obtain Q edge control regions, where P ≥ Q.

[0065] Furthermore, the aforementioned distribution network stability control system for high-penetration distributed power sources is also used for: selecting different numbers of clusters to perform node clustering analysis and intra-cluster sum of squares calculation on the distribution network topology, obtaining multiple intra-cluster sums of squares; identifying the inflection point of the rate of decline for the different numbers of clusters according to the multiple intra-cluster sums of squares, and determining the target number of clusters Q; sequentially calculating the distribution network node pair similarity matrix between the node features in the P distribution network node feature vectors; and performing dynamic clustering partitioning on the distribution network topology based on the target number of clusters Q according to the distribution network node pair similarity matrix, obtaining Q edge control regions.

[0066] Furthermore, the aforementioned distribution network stability control system for high-penetration distributed power sources is further configured to: randomly select Q nodes in the distribution network topology as initial cluster centers according to the target cluster number Q; assign the remaining nodes in the distribution network topology to the initial cluster centers with the highest similarity according to the distribution network node pair similarity matrix, thereby obtaining Q initial node clusters; recalculate the mean of the Q node feature vectors of the Q initial node clusters; and perform iterative clustering update of the distribution network topology based on the mean of the Q node feature vectors until a preset termination condition is met, thereby obtaining Q edge control regions.

[0067] Furthermore, the aforementioned distribution network stability control system for high-penetration distributed power sources is also used for: collecting historical stability control datasets of the distribution network, which include distribution network operation data, distributed power source output data, stability strategy control data, and stability control effect data; defining a fitness function for distribution network stability control based on multiple objectives of distribution network stability control; using the fitness function to evaluate and optimize the historical stability control dataset to obtain a feasible distribution network stability control dataset; and training strategy control based on the feasible distribution network stability control dataset to build a distribution network stability control strategy channel.

[0068] Furthermore, the aforementioned distribution network stability control system for high-penetration distributed power sources is also used for: classifying the feasible distribution network stability control dataset into a voltage stability control strategy dataset, a frequency stability control strategy dataset, and a harmonic suppression control strategy dataset; training the voltage stability control strategy dataset, the frequency stability control strategy dataset, and the harmonic suppression control strategy dataset into policy control channels, generating voltage stability control strategy channels, frequency stability control strategy channels, and harmonic suppression control strategy channels; and merging the voltage stability control strategy channels, the frequency stability control strategy channels, and the harmonic suppression control strategy channels in parallel to build a distribution network stability control strategy channel.

[0069] Furthermore, the aforementioned distribution network stability control system for high-penetration distributed power sources is also used for: performing correlation and impact analysis on the Q regional control strategy parameters based on the distribution network topology to generate a regional stability control directed influence network; and performing global collaborative optimization on the Q regional control strategy parameters according to the regional stability control directed influence network to determine the global control strategy parameters of the distribution network.

[0070] Furthermore, the aforementioned distribution network stability control system for high-penetration distributed power sources is also used for: performing correlation and impact analysis on the Q regional control strategy parameters based on the distribution network topology to obtain the directed paths of influence of distribution network nodes; evaluating the degree of influence of the Q regional control strategy parameters according to the directed paths of influence of distribution network nodes to obtain a set of influence coefficients for distribution network nodes; and connecting the directed paths of influence of distribution network nodes based on the set of influence coefficients to generate a directed influence network for regional stability control.

[0071] Furthermore, the aforementioned distribution network stability control system for high-penetration distributed power sources is also used to: perform global collaborative optimization of the Q regional control strategy parameters according to the regional stability control directed influence network to obtain global control strategy parameter thresholds; and use the distribution network stability control fitness function to evaluate and solve the global control strategy parameter thresholds to determine the global control strategy parameters of the distribution network.

[0072] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The distribution network stability control method and specific examples for high-penetration distributed power sources described in the foregoing Embodiment 1 are also applicable to the distribution network stability control system for high-penetration distributed power sources described in this embodiment. Through the foregoing detailed description of the distribution network stability control method for high-penetration distributed power sources, those skilled in the art can clearly understand the distribution network stability control system for high-penetration distributed power sources described in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0073] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not 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.

[0074] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A distribution network stability control method for high-penetration distributed power sources, characterized in that, The method includes: Based on the structural design information of the target distribution network, a distribution network topology network is constructed, and electrical data of distribution network operation and output information of distributed power sources are collected in real time. The distribution network topology is dynamically clustered and divided according to the electrical data of the distribution network operation and the output information of distributed power sources to obtain Q edge control regions. A distribution network stability control strategy channel is established, and the distribution network stability control strategy channel is used to synchronously partition and analyze the Q edge control areas to generate Q area control strategy parameters. The control strategy parameters of the Q regions are globally coordinated and optimized to determine the global control strategy parameters of the distribution network, and the distribution network stability closed-loop control is performed through the global control strategy parameters of the distribution network.

2. The distribution network stability control method for high-penetration distributed power sources as described in claim 1, characterized in that, The distribution network topology is dynamically clustered and divided according to the electrical data of the distribution network operation and the output information of distributed power sources to obtain Q edge control regions, including: Data cleaning and feature extraction are performed on the electrical data of the power distribution network operation and the output information of distributed power sources to obtain the electrical feature set of the power distribution network and the power output feature set of the power source. Based on the power distribution network topology, information on P power distribution network nodes is extracted; The electrical feature set and power output feature set of the distribution network are associated and arranged with the information of the P distribution network nodes to construct the feature vectors of the P distribution network nodes; Based on the feature vectors of the P distribution network nodes, the distribution network topology is dynamically clustered to obtain Q edge control regions, where P ≥ Q.

3. The distribution network stability control method for high-penetration distributed power sources as described in claim 2, characterized in that, Based on the feature vectors of the P distribution network nodes, the distribution network topology is dynamically clustered to obtain Q edge control regions, including: Different numbers of clusters were selected to perform node clustering analysis and intra-cluster sum of squares calculation on the power distribution network topology, resulting in multiple intra-cluster sums of squares. Based on the sum of squares within the multiple clusters, the inflection point of the rate of decrease of the different numbers of clusters is identified to determine the target number of clusters Q; Calculate the similarity matrix of distribution network node pairs between the features of each node in the feature vectors of the P distribution network nodes in sequence; Based on the similarity matrix of the distribution network nodes, dynamic clustering is performed on the distribution network topology according to the target cluster number Q to obtain Q edge control regions.

4. The distribution network stability control method for high-penetration distributed power sources as described in claim 3, characterized in that, Based on the similarity matrix of the distribution network nodes and the target cluster number Q, dynamic clustering is performed on the distribution network topology to obtain Q edge control regions, including: Based on the target cluster number Q, Q nodes in the power distribution network topology are randomly selected as initial cluster centers; According to the distribution network node pair similarity matrix, the remaining nodes in the distribution network topology are assigned to the initial cluster center with the highest similarity, resulting in Q initial node clusters; Recalculate the mean of the Q node feature vectors of the Q initial node clusters; Based on the mean of the feature vectors of the Q nodes, the distribution network topology is updated by iterative clustering until a preset termination condition is met, resulting in Q edge control regions.

5. The distribution network stability control method for high-penetration distributed power sources as described in claim 1, characterized in that, Establish a distribution network stability control strategy channel, including: Collect historical stability control datasets of the distribution network, which include distribution network operation data, distributed power generation output data, stability strategy control data, and stability control effect data. Based on the multi-objective nature of distribution network stability control, a fitness function for distribution network stability control is defined. The distribution network stability control fitness function is used to evaluate and optimize the historical stability control dataset of the distribution network to obtain a feasible distribution network stability control dataset. Based on the feasible distribution network stability control dataset, strategy control training is performed to build a distribution network stability control strategy channel.

6. The distribution network stability control method for high-penetration distributed power sources as described in claim 5, characterized in that, Based on the aforementioned feasible distribution network stability control dataset, strategy control training is performed to establish a distribution network stability control strategy channel, including: The feasible distribution network stability control dataset is classified into strategies to obtain voltage stability control strategy dataset, frequency stability control strategy dataset, and harmonic suppression control strategy dataset. The voltage stability control strategy dataset, frequency stability control strategy dataset, and harmonic suppression control strategy dataset are trained to generate voltage stability control strategy channels, frequency stability control strategy channels, and harmonic suppression control strategy channels. The voltage stability control strategy channel, frequency stability control strategy channel, and harmonic suppression control strategy channel are connected in parallel and merged to build a distribution network stability control strategy channel.

7. A distribution network stability control method for high-penetration distributed power sources as described in claim 5, characterized in that, Global collaborative optimization is performed on the Q regional control strategy parameters to determine the global control strategy parameters for the distribution network, including: Based on the distribution network topology, the correlation and impact analysis of the Q regional control strategy parameters is performed to generate a regional stability control directed influence network. The global control strategy parameters of the Q regions are globally coordinated and optimized according to the regional stability control directed influence network to determine the global control strategy parameters of the distribution network.

8. The distribution network stability control method for high-penetration distributed power sources as described in claim 7, characterized in that, Generate a regional stability control directed influence network, including: Based on the distribution network topology, the correlation and impact analysis of the control strategy parameters of the Q regions is performed to obtain the directed paths of the distribution network nodes' influence. The influence degree of the control strategy parameters of the Q regions is evaluated according to the directed path of the influence of the distribution network nodes, and the set of influence coefficients of the distribution network nodes is obtained. Based on the set of influence coefficients of the distribution network nodes, the directed paths of influence of the distribution network nodes are interconnected to generate a directed influence network for regional stability control.

9. A distribution network stability control method for high-penetration distributed power sources as described in claim 7, characterized in that, The global control strategy parameters for the Q regions are globally and collaboratively optimized according to the regional stability control directed influence network to determine the global control strategy parameters for the distribution network, including: The global collaborative optimization of the Q regional control strategy parameters is performed according to the regional stability control directed influence network to obtain the global control strategy parameter threshold. The global control strategy parameter thresholds are evaluated and optimized using the aforementioned distribution network stability control fitness function to determine the global control strategy parameters for the distribution network.

10. A distribution network stability control system for high-penetration distributed power sources, characterized in that, The steps for implementing the distribution network stability control method for high-penetration distributed power sources as described in any one of claims 1 to 9 include: The data acquisition module is used to construct the distribution network topology based on the structural design information of the target distribution network, and to collect the electrical data of the distribution network operation and the output information of distributed power sources in real time. The edge control region acquisition module is used to dynamically cluster and divide the distribution network topology according to the distribution network operation electrical data and distributed power output information to obtain Q edge control regions. The regional control strategy parameter generation module is used to build a distribution network stability control strategy channel, and to perform synchronous partitioning and parsing on the Q edge control areas using the distribution network stability control strategy channel to generate Q regional control strategy parameters. The closed-loop control module is used to perform global collaborative optimization of the control strategy parameters of the Q regions, determine the global control strategy parameters of the distribution network, and perform closed-loop control of distribution network stability through the global control strategy parameters of the distribution network.

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