Static centralized and dynamic distributed coordinated control method for reactive power / voltage in distribution network

Abstract

The present invention relates to the field of smart grid regulation technology, and in particular to a method for coordinated control of reactive power / voltage in a distribution network with static centralization and dynamic distribution. The method comprises the following steps: acquiring basic data of the distribution network; performing multi-scale voltage stability domain analysis based on the basic data of the distribution network to generate multi-scale voltage stability domain data; performing static centralized control strategy processing on the multi-scale voltage stability domain data to generate a static centralized control strategy; performing dynamic event triggering control processing based on the multi-scale voltage stability domain data to generate a dynamic distributed control strategy; performing multi-region real-time collaborative control execution using the static centralized control strategy and the dynamic distributed control strategy to generate corrected coordinated control scheme data. The present invention achieves rapid dynamic response of reactive power and voltage by performing multi-scale voltage stability management and intelligent coordinated control on the distribution network, and effectively suppresses the voltage over-limit problem caused by the fluctuating output of active power.

Description

Technical Field

[0001] The present invention relates to the field of smart grid regulation technology, and in particular to a method for coordinated control of static centralized and dynamic distributed reactive power / voltage in a distribution network. Background Art

[0002] With the development of distributed generation and smart grid, the distribution network has become more complex, and the difficulty of voltage control has also increased. The access of distributed generation, especially renewable energy such as solar and wind power, has led to increased voltage fluctuations in the distribution network. Existing voltage control technologies mainly focus on the regulation of transformers and voltage regulators, but lack the ability to monitor and coordinate the entire distribution network in real time. In this context, the reactive power and voltage control problems of the distribution network have become increasingly prominent, directly affecting the safe and stable operation of the power system. Therefore, how to achieve a dynamic balance between reactive power and voltage in the distribution network, ensure the stable operation of the distribution network, and solve the voltage over-limit problem caused by the fluctuating output of active power has become the focus of current technical research. However, traditional reactive compensation and voltage regulation methods mainly rely on the regulation of static reactive compensation equipment and on-load tap-changing transformers, lack the ability to monitor and coordinate the entire distribution network in real time, and are difficult to meet the rapid and dynamic power demand, especially when the load changes drastically and the access of renewable energy is increasing. Summary of the invention

[0003] Based on this, the present invention provides a method for coordinated control of reactive power / voltage in a distribution network using static centralized control and dynamic distributed control to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for coordinated control of reactive power / voltage of a distribution network with static concentration and dynamic distribution is provided, comprising the following steps:

[0005] Step S1: acquiring basic data of the distribution network; extracting historical fault data of the power grid according to the basic data of the distribution network to obtain historical fault data of the power grid; performing multi-scale voltage constraint analysis according to the basic data of the distribution network to generate multi-scale voltage constraint data; performing multi-scale voltage stability domain analysis on the multi-scale voltage constraint data through the historical fault data of the power grid to generate multi-scale voltage stability domain data;

[0006] Step S2: using the distribution network topology data in the distribution network basic data to perform regional sensitivity difference analysis on the multi-scale voltage stability domain data, and generate multi-scale control sensitivity data;

[0007] Step S3: Perform multi-dimensional mapping of global voltage according to multi-scale control sensitivity data to obtain a global voltage optimization model; perform static centralized control strategy processing according to the global voltage optimization model to generate a static centralized control strategy; perform dynamic event triggering control processing on multi-scale voltage stability domain data through historical fault data of the power grid to generate a dynamic distributed control strategy;

[0008] Step S4: acquiring real-time status monitoring data of the distribution network to obtain real-time operation status monitoring data; performing monitoring event trigger identification based on the real-time operation status monitoring data to obtain event warning signal data; performing inter-regional coordinated control processing on the event warning signal data using a static centralized control strategy and a dynamic distributed control strategy to obtain coordinated control instruction data;

[0009] Step S5: Execute multi-area coordinated control according to the coordinated control instruction data, and evaluate the control effect to generate power grid coordinated control effect data; based on the power grid coordinated control effect data, correct the power grid control strategy through a static centralized control strategy to generate corrected coordinated control scheme data.

[0010] The present invention systematically analyzes the operation history and potential failure modes of the power grid by integrating the basic data of the distribution network, constructs a multi-scale voltage constraint framework, clarifies the operation boundaries of the power grid under different conditions, further subdivides the stable state of the power grid, helps to identify and prevent potential voltage stability problems, and improves the controllability and safety of the power grid. By identifying the response sensitivity of each region to the control operation, the sensitivity of different regions to voltage changes can be revealed, so that the control strategy can be adjusted preferentially and accurately for key areas and sensitive links in the power grid, thereby improving the efficiency and effect of control. According to the multi-scale control sensitivity data, the global voltage multi-dimensional mapping processing can be performed, and the complex voltage management objectives can be converted into an operational mathematical model, ensuring the stable operation of the power grid under normal and abnormal conditions. Through the historical fault data of the power grid, the multi-scale voltage stability domain data is subjected to dynamic event triggering control processing, and the corresponding control measures can be quickly activated when an emergency occurs, which enhances the adaptability and response speed of the power grid in a dynamic environment, and ensures that the power grid can remain stable under the conditions of drastic load changes and increased access to renewable energy. The real-time status monitoring and event warning mechanism significantly enhances the real-time response capability of the system, and can immediately identify and take measures at the early stage of faults or anomalies, reducing the impact range and recovery time of faults. The coordinated control processing between regions realizes the rapid transmission and execution of control instructions through the coordinated application of static centralized control strategy and dynamic distributed control strategy, and improves the coordinated operation capability and fault response efficiency of the power grid. The combination of the two takes into account both the macro stability of the power grid and the local rapid response and adaptability, and improves the dynamic balance capability and fault recovery speed of the entire power grid. Multi-regional coordinated control execution ensures the effective implementation of the control strategy in the actual power grid. Through the control effect evaluation, it not only verifies the effectiveness of the control strategy, but also provides feedback for the continuous optimization of the strategy. The strategy re-correction based on the evaluation results forms a closed-loop control optimization process, ensuring that in the operation of the power grid, it can effectively adjust the voltage limit or reactive power loss problems that occur in the operation of the power grid, improve the safe operation level of the power grid, and ensure that the power grid can remain stable and efficient under dynamic load changes. Therefore, a method for coordinated control of reactive power / voltage of a distribution network of the present invention adopts a multi-scale voltage stability domain analysis and considers the sensitivity of the adaptive control area to implement a coordinated control strategy of static concentration and dynamic distribution for the distribution network, thereby achieving precise control of the voltage / reactive power of the distribution network and solving the problems of insufficient voltage stability and weak fault self-healing capability of the distribution network.

[0011] Preferably, step S1 comprises the following steps:

[0012] Step S11: Obtain basic data of the distribution network, including distribution network topology data and historical distribution network operation status data;

[0013] Step S12: preprocessing the historical distribution network operation status data to generate clean historical operation status data;

[0014] Step S13: Analyze the historical faults of the power grid according to the historical cleaning operation status data to obtain the historical fault data of the power grid;

[0015] Step S14: performing multi-scale voltage constraint analysis according to the cleaning history operation status data to generate multi-scale voltage constraint data;

[0016] Step S15: Calculate the static voltage stability margin of the distribution network topology data using the multi-scale voltage constraint data to obtain static voltage stability margin data; construct a static voltage stability domain for the distribution network topology data based on the static voltage stability margin data to generate static voltage stability domain data;

[0017] Step S16: Performing a dynamic disturbance scenario response on the static voltage stability domain data through the historical fault data of the power grid to generate dynamic voltage stability margin data; constructing a dynamic voltage stability domain for the distribution network topology data based on the dynamic voltage stability margin data to generate dynamic voltage stability domain data;

[0018] Step S17: performing multi-scale voltage domain fusion according to the static voltage stability domain data and the dynamic voltage stability domain data to generate multi-scale voltage stability domain data.

[0019] The present invention obtains basic data of the distribution network, including distribution network topological structure data and historical distribution network operation status data, and can provide basic information for subsequent analysis. The topological structure data provides the node, line and equipment configuration of the distribution network, ensuring the accuracy of subsequent calculation and analysis. By cleaning the historical operation status data, such as removing outliers, filling missing data, smoothing noise and other operations, it can be ensured that the subsequent analysis is based on high-quality data to avoid misjudgment or analysis deviation caused by data quality problems. According to the cleaning historical operation status data, the historical fault analysis of the power grid can identify and summarize the fault types, frequencies and their effects that occurred in the past operation of the distribution network. According to the cleaning historical operation status data, multi-scale voltage constraint analysis can be performed, and the voltage stability of the distribution network under different loads and operating conditions can be evaluated, which is helpful to understand the voltage fluctuation range under various working conditions. Using multi-scale voltage constraint data to calculate the static voltage stability margin of the distribution network topological structure data can measure the voltage stability capability of the distribution network under static conditions and identify the safe operation range of the distribution network under different operating conditions. By using historical fault data of the power grid to respond to dynamic disturbance scenarios on static voltage stability domain data, it is possible to evaluate the voltage stability of the distribution network under actual faults or disturbances, and reveal the recovery capability and stability of the distribution network when subjected to dynamic disturbances. Multi-scale voltage domain fusion based on static voltage stability domain data and dynamic voltage stability domain data can comprehensively consider voltage stability under static and dynamic conditions, ensuring efficient voltage management and reactive power coordination under different operating conditions, thereby improving the overall operating efficiency and reliability of the distribution network.

[0020] Preferably, step S14 comprises the following steps:

[0021] Step S141: performing time multi-scale aggregation processing on the cleaning history operation status data to generate multi-scale operation status data, wherein the multi-scale operation status data includes second-level operation status data, hierarchical operation status data, and hour-level operation status data;

[0022] Step S142: performing a short-term voltage sag value analysis on the second-level operation status data to obtain short-term voltage characteristic data; performing a mid-term voltage deviation fluctuation analysis on the graded operation status data to obtain mid-term voltage characteristic data; performing a long-term voltage stability margin analysis on the hour-level operation status data to obtain long-term voltage characteristic data;

[0023] Step S143: identifying voltage sag boundaries according to short-term voltage characteristic data, and generating load voltage sag range data; identifying voltage fluctuation boundaries according to medium-term voltage characteristic data, and generating load voltage deviation range data; identifying load voltage stability margin range according to long-term voltage characteristic data, and generating load voltage margin range data;

[0024] Step S144: performing boundary constraint condition quantization processing on the load voltage sag range data, the load voltage deviation range data and the load voltage margin range data to generate voltage quantization boundary condition data;

[0025] Step S145: performing multi-scale constraint fusion on the voltage quantization boundary condition data through the multi-scale operating status data to generate multi-scale voltage constraint data.

[0026] The present invention performs time multi-scale aggregation processing based on the historical operation status data of the cleaning, which can provide a comprehensive perspective of the operation of the power grid. The data of different time scales reflect the state changes of the power grid in instantaneous, short-term and long-term operation, and help analyze the voltage behavior and characteristics of the power grid in different time periods. The short-term voltage sag value analysis of the second-level operation status data can identify the instantaneous change of the voltage of the power grid under instantaneous load fluctuations or faults. The medium-term voltage deviation fluctuation analysis of the graded operation status data can evaluate the stability and fluctuation trend of the voltage of the power grid over a long period of time. The long-term voltage stability margin analysis of the time-level operation status data can reveal the voltage stability capability and margin of the power grid in long-term operation. By identifying the voltage characteristic boundaries at different time scales, such as the voltage sag range, voltage deviation range and voltage stability margin range at the load end, it is actually setting specific performance indicators and safety limits for the reactive power / voltage control of the power grid. It helps to optimize the configuration of reactive compensation resources, reduce the impact of voltage sag on sensitive loads, and ensure the long-term stability of the overall voltage level. By performing multi-scale constraint fusion on voltage quantization boundary condition data through multi-scale operating status data, the voltage constraints at different time scales can be comprehensively considered, so that the influence of each time scale can be taken into account when formulating the voltage control strategy, thereby achieving more efficient voltage regulation and reactive power coordination.

[0027] Preferably, step S2 comprises the following steps:

[0028] Step S21: extracting voltage stability control elements from multi-scale voltage stability domain data using distribution network topology data to generate voltage control variable data;

[0029] Step S22: marking the multi-scale voltage stability domain data with a stability domain state based on the multi-scale voltage constraint data, and performing hierarchical mapping to obtain multi-scale hierarchical stability domain data, wherein the multi-scale hierarchical stability domain data includes a safe operation state area, a warning operation state area, and a dangerous operation state area;

[0030] Step S23: performing regional sensitivity difference analysis on the multi-scale layered stable domain data to generate regional sensitivity difference data;

[0031] Step S24: performing a layered stability domain disturbance analysis on the multi-scale layered stability domain data through the voltage control variable data based on the regional sensitivity difference data to generate control sensitivity matrix data;

[0032] Step S25: Perform multi-scale matrix decoupling according to the control sensitivity matrix data to obtain multi-scale control sensitivity data.

[0033] The present invention uses the distribution network topology data to extract the voltage stability control elements from the multi-scale voltage stability domain data, and can identify the key factors affecting voltage stability. It is helpful to determine the role of different voltage control components (such as reactive compensation equipment, transformer voltage regulation, etc.) in maintaining voltage stability. Based on the multi-scale voltage constraint data, the multi-scale voltage stability domain data is marked with the stable domain state, which can clearly divide the safety of the power grid under different operating conditions. The regional sensitivity difference analysis of the multi-scale hierarchical stable domain data can reveal the differences in the voltage control response of each safety area, and reveal which areas have more critical or more sensitive voltage control, so that the control requirements of these areas can be given priority, and the overall control efficiency can be improved. The hierarchical stable domain disturbance analysis is performed using the regional sensitivity difference data and the voltage control variable data, and the quantitative relationship between the control variable and the stable domain state change of the power grid is established. According to the control sensitivity matrix data, multi-scale matrix decoupling can simplify the complex control relationship into multiple independent control actions, which can not only cope with instantaneous disturbances but also maintain a long-term stable control scheme, realize the multi-dimensional coordination and optimization of the control strategy, and enhance the dynamic adaptability and global stability of the distribution network.

[0034] Preferably, step S24 includes the following steps:

[0035] Step S241: performing control variable disturbance processing on the voltage control variable data based on the regional sensitivity difference data to generate disturbance control variable data;

[0036] Step S242: performing a stable domain state variation analysis on the multi-scale layered stable domain data by using the disturbance control variable data to generate stable domain disturbance response curve data;

[0037] Step S243: Calculate the sensitivity coefficient of the multi-scale layered stable domain data using the disturbance control variable data and the stable domain disturbance response curve data to generate a control sensitivity coefficient;

[0038] Step S244: extracting regional sensitivity features from the multi-scale layered stable domain data by controlling the sensitivity coefficient, and performing control point feature space mapping on the voltage control variable data to generate control point feature space data;

[0039] Step S245: Calculating the regional correlation degree according to the control point feature space data to generate a control region correlation matrix;

[0040] Step S246: Perform multi-region weighted processing on the control sensitivity coefficients through the control region correlation matrix, and construct a sensitivity matrix to generate control sensitivity matrix data.

[0041] The present invention performs disturbance processing on voltage control variable data and generates disturbance control variable data, applies small changes to different control variables, and identifies the contribution of different control variables to the stability of the power grid. By analyzing the change amount of the stable domain state of the multi-scale layered stable domain data through the disturbance control variable data, the change trend of the stable domain state of the power grid under the disturbance of different control variables can be described. The sensitivity coefficient of the multi-scale layered stable domain data is calculated using the disturbance control variable data and the stable domain disturbance response curve data, and the influence of each control variable on the change of the stable domain state can be quantified, ensuring that when implementing voltage management, resources can be concentrated on the control measures that have the greatest impact on the stability of the power grid, and the overall control efficiency is improved. The regional sensitivity feature extraction of the multi-scale layered stable domain data is performed by controlling the sensitivity coefficient, and the sensitivity feature data of each control area is separated, and the key nodes and features of the voltage control are clarified. According to the control point feature space data, the regional correlation degree is calculated, and the mutual influence relationship between different voltage control areas can be revealed, so that when implementing voltage and reactive power control, the interaction between regions can be considered, a more coordinated control strategy can be achieved, and potential operation risks can be reduced. By performing multi-region weighted processing on the control sensitivity coefficient through the control area correlation matrix, the sensitivity information of each region can be integrated to ensure that the control measures can be adjusted efficiently and flexibly during power grid operation to achieve the coordinated control goals of static concentration and dynamic distribution of reactive power / voltage in the distribution network.

[0042] Preferably, step S3 comprises the following steps:

[0043] Step S31: constructing a multi-objective optimization function according to preset static voltage optimization target data, and performing multi-dimensional mapping of the global voltage multi-objective function based on multi-scale control sensitivity data to obtain a global voltage optimization model;

[0044] Step S32: setting distribution network constraints according to the distribution network topology data to generate distribution network constraint data, wherein the distribution network constraint data includes equipment operation constraint data, safety constraint data and power grid operation standard constraint data;

[0045] Step S33: constructing a multi-objective optimization problem for the distribution network constraint condition data through a global voltage optimization model to generate voltage control optimization problem data;

[0046] Step S34: solving the voltage control optimization problem data for static voltage control values ​​using a preset multi-objective optimization algorithm based on the global voltage optimization model, and performing static centralized control strategy processing to generate a static centralized control strategy;

[0047] Step S35: Perform dynamic event triggering control processing on the multi-scale voltage stability domain data through the historical fault data of the power grid to generate a dynamic distributed control strategy.

[0048] The present invention constructs a multi-objective optimization function according to preset static voltage optimization target data, aiming to balance and optimize multiple mutually constrained voltage control targets (such as voltage stability, power quality, economy, etc.). The distribution network constraint conditions are set according to the distribution network topology data to ensure that the safety standards and operating specifications of the power grid can be followed when optimizing voltage control, and to prevent equipment damage or failure caused by improper control measures. The multi-objective optimization problem is constructed for the distribution network constraint condition data through the global voltage optimization model, and the complex voltage control requirements can be converted into a processable mathematical model. Based on the global voltage optimization model, the static voltage control value of the voltage control optimization problem data is solved using a preset multi-objective optimization algorithm, with the aim of finding the optimal reactive power / voltage control configuration under all constraints, focusing on long-term system optimization and overall performance improvement, and ensuring the high efficiency and stability of the power grid under normal operation. The multi-scale voltage stability domain data is subjected to dynamic event triggering control processing through the historical fault data of the power grid, which can quickly respond and adjust the voltage control strategy in the face of emergencies or dynamic changes, and can enhance the stability and reliability of the power grid under uncertainty and variability conditions, and ensure the safe operation of the power grid.

[0049] Preferably, step S35 includes the following steps:

[0050] Step S351: extracting fault event features based on historical fault data of the power grid to generate historical event feature data;

[0051] Step S352: performing event impact assessment on historical event feature data using multi-scale voltage stability domain data to obtain fault event impact assessment data;

[0052] Step S353: Distributed control resources are identified according to the fault event impact assessment data, and a mapping relationship between events and control resources is established to obtain event-resource mapping data;

[0053] Step S354: dividing the event impact range according to the event-resource mapping data, and sorting the event response priorities to generate event response priority list data;

[0054] Step S355: evaluating the control resource response capability of the event response priority list data through the event-resource mapping data, and setting the control resource priority to obtain event control resource priority data;

[0055] Step S356: Dynamically matching the event response priority list data and the event control resource priority data through a preset power grid dynamic control rule library to obtain event-resource-control rule data;

[0056] Step S357: Perform dynamic distributed control strategy processing according to the event-resource-control strategy mapping data to obtain a dynamic distributed control strategy.

[0057] The present invention extracts fault event features based on historical fault data of the power grid, and can summarize typical modes and key indicators of fault occurrence, providing a benchmark and basis for quickly identifying and responding to similar events in the future. : The multi-scale voltage stability domain data is used to evaluate the impact of historical event feature data, and the obtained fault event impact evaluation data can quantify the impact of specific faults on the stability of different areas of the power grid, which helps to accurately determine which areas are most susceptible to faults. Distributed control resources are identified and event-resource mapping relationships are established based on the fault event impact evaluation data, ensuring that when a fault occurs, available control resources (such as distributed power generation, energy storage equipment, reactive power compensation devices, etc.) near the affected area can be quickly located, providing resource preparation for rapid response and recovery. By dividing the event impact range and sorting the response priority, it is helpful for the dispatching center or the automatic control system to quickly determine which area or which fault is most urgent to handle, ensuring the orderliness and efficiency of resource allocation and control actions. The control resource response capability is evaluated for the event response priority list data, and the control resource priority is set. This operation refines the control resource usage strategy, ensuring that under limited resource conditions, the resources that can most effectively respond to the current fault are preferentially called, and the flexibility and effectiveness of the control strategy are improved. By dynamically matching the event response priority list data and event control resource priority data through the preset power grid dynamic control rule library, specific fault events can be effectively connected with corresponding control strategies and resources. Dynamic distributed control strategy processing based on event-resource-control strategy mapping data can achieve real-time monitoring and dynamic adjustment of the power grid, ensuring that various fault events can be flexibly responded to in a complex and changing power grid environment, and voltage and reactive power distribution can be quickly adjusted, thereby ensuring the safe, stable and efficient operation of the distribution network.

[0058] Preferably, step S4 comprises the following steps:

[0059] Step S41: acquiring real-time status monitoring data of the distribution network to obtain real-time operation status monitoring data;

[0060] Step S42: performing monitoring event trigger identification according to the real-time operation status monitoring data, and generating an event warning signal to obtain event warning signal data;

[0061] Step S43: analyzing the load and power distribution characteristics of the real-time operation status monitoring data through the distribution network topology data to generate power grid load and power distribution characteristic data;

[0062] Step S44: Based on the power grid load and power distribution characteristic data, the event warning signal data is used to adaptively divide the distribution network topology data into control areas to obtain adaptive control area data;

[0063] Step S45: using the dynamic distributed control strategy and the static centralized control strategy to perform inter-regional coordinated control processing on the adaptive control regional data to generate real-time distributed coordinated control data;

[0064] Step S46: mapping power grid control variables according to the real-time distributed coordinated control data, and generating control instructions to obtain coordinated control instruction data.

[0065] The present invention acquires real-time state monitoring data of the distribution network, and can reflect the current operation state and load situation of the power grid in real time. According to the real-time operation state monitoring data, monitoring event trigger identification is performed, and potential faults or abnormal events can be identified in time, ensuring that a rapid response can be made when an abnormal situation occurs, and reducing the probability of accidents. Through the distribution network topology data, the load and power distribution characteristics of the real-time operation state monitoring data are analyzed, and the distribution of the load and power supply of each node in the power grid can be deeply understood. The division of the adaptive control area is based on the real-time operation characteristics and early warning signals of the power grid, and the control area is dynamically adjusted to ensure that the control strategy can closely fit the actual operation state and demand of the power grid, which is helpful to quickly isolate and deal with local problems when they occur, and avoid the spread of the impact. It integrates short-term dynamic response control and long-term system stability control, realizes the time and space complementarity and advantage complementarity of the control strategy, ensures the rapid suppression of transient disturbances, and guarantees the long-term stability and economy of the system. According to the real-time distributed coordinated control data, the power grid control variable mapping is performed, and the coordinated control strategy can be converted into specific operation instructions.

[0066] Preferably, step S45 includes the following steps:

[0067] Step S451: Perform dynamic event feature analysis on the event warning signal data to generate dynamic event feature data; perform event dynamic classification on the dynamic event feature data using a preset event response threshold to obtain event dynamic level label data;

[0068] Step S452: extracting static strategy parameters according to the static centralized control strategy to obtain static strategy parameter data;

[0069] Step S453: Based on the static strategy parameter data, the dynamic event feature data is subjected to static control scenario fitting through the event dynamic level label data to obtain a real-time static fitting control value;

[0070] Step S454: activating the dynamic distributed control strategy through the event dynamic level tag data, and adjusting the distributed control variables of the adaptive control area data using the real-time static fitting control value as the dynamic control reference value to obtain dynamic control adjustment data;

[0071] Step S455: performing event situation awareness prediction on the dynamic control adjustment data through a static centralized control strategy to generate event situation prediction data;

[0072] Step S456: Based on the event situation prediction data, the static centralized control strategy is used to perform global static centralized control optimization on the adaptive control area data to obtain global static control optimization data;

[0073] Step S457: Perform collaborative control processing according to the dynamic control adjustment data and the global static control optimization data to generate real-time distributed coordinated control data.

[0074] The present invention extracts the key features of the event by deeply analyzing the early warning signal, and divides it into different levels according to the preset threshold value, so as to ensure that the control measures match the severity of the event. According to the static centralized control strategy, the static strategy parameters are extracted, and the control parameters of the power grid under normal operation can be obtained. By combining the static strategy parameters with the dynamic event feature data, the control level that the static strategy should take under the current event level is simulated, which provides a benchmark reference for dynamic control. The dynamic distributed control strategy is activated by the event dynamic level label data, so that the real-time adjustment of the adaptive control area can be realized, and the control variables can be quickly responded and adjusted, so as to optimize the operation state of the power grid. The dynamic control adjustment data is predicted by the static centralized control strategy to predict the development trend of the event. Based on the event situation prediction data, the adaptive control area is globally optimized and adjusted, ensuring that the voltage control of the entire power grid can still maintain a global optimal or near-optimal state even in a dynamically changing environment. The collaborative control processing ensures the seamless connection between dynamic control and static control, improves the voltage stability and power quality of the distribution network under various operating conditions, and improves the operation efficiency and reliability of the power grid.

[0075] Preferably, step S5 comprises the following steps:

[0076] Step S51: sending the coordinated control instruction data through the communication network, and executing multi-region coordinated control to obtain control execution feedback data;

[0077] Step S52: performing real-time monitoring of multiple indicators according to the control execution feedback data, and generating real-time adjustment monitoring indicator data;

[0078] Step S53: evaluating the voltage stability of the distribution network by adjusting the monitoring index data in real time, and generating grid adjustment voltage evaluation data;

[0079] Step S54: Analyze the reactive power loss of the power grid according to the control adjustment effect index data to obtain the reactive power loss data of the power grid;

[0080] Step S55: evaluating the effect of grid coordination control according to the grid adjustment voltage evaluation data and the grid reactive power loss data, and generating grid coordination control effect data;

[0081] Step S56: Perform voltage over-limit suppression analysis based on the power grid coordinated control effect data, and use the static centralized control strategy to correct the power grid control strategy to generate corrected coordinated control solution data.

[0082] The present invention issues coordinated control instructions through a communication network and executes multi-region collaborative control, thereby realizing real-time interaction between the control center and the on-site equipment of the power grid, and ensuring that the control instructions are executed quickly and accurately. Real-time monitoring of multiple indicators is performed based on the control execution feedback data, which can fully reflect the operating status of the power grid after the implementation of coordinated control. The voltage stability of the distribution network is evaluated by adjusting the monitoring index data in real time, and the effect of the control strategy on improving the voltage stability of the power grid is verified. The reactive power loss analysis of the power grid is performed based on the control adjustment effect index data, and the reactive power loss of the power grid during operation can be quantified. The coordinated control effect of the power grid is evaluated based on the power grid adjustment voltage evaluation data and the power grid reactive power loss data, and the comprehensive benefits of the control strategy are comprehensively analyzed to ensure that potential problems can be discovered and adjusted in time when the control measures are implemented, optimize the control effect of the power grid, and improve the overall operating performance of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 It is a schematic flow chart of the steps of the method for coordinated control of reactive power / voltage of a power distribution network with static concentration and dynamic distribution according to the present invention;

[0084] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0085] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0086] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0087] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0088] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0089] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0090] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for coordinated control of reactive power / voltage of a distribution network with static concentration and dynamic distribution, comprising the following steps:

[0091] Step S1: acquiring basic data of the distribution network; extracting historical fault data of the power grid according to the basic data of the distribution network to obtain historical fault data of the power grid; performing multi-scale voltage constraint analysis according to the basic data of the distribution network to generate multi-scale voltage constraint data; performing multi-scale voltage stability domain analysis on the multi-scale voltage constraint data through the historical fault data of the power grid to generate multi-scale voltage stability domain data;

[0092] Step S2: using the distribution network topology data in the distribution network basic data to perform regional sensitivity difference analysis on the multi-scale voltage stability domain data, and generate multi-scale control sensitivity data;

[0093] Step S3: Perform multi-dimensional mapping of global voltage according to multi-scale control sensitivity data to obtain a global voltage optimization model; perform static centralized control strategy processing according to the global voltage optimization model to generate a static centralized control strategy; perform dynamic event triggering control processing on multi-scale voltage stability domain data through historical fault data of the power grid to generate a dynamic distributed control strategy;

[0094] Step S4: acquiring real-time status monitoring data of the distribution network to obtain real-time operation status monitoring data; performing monitoring event trigger identification based on the real-time operation status monitoring data to obtain event warning signal data; performing inter-regional coordinated control processing on the event warning signal data using a static centralized control strategy and a dynamic distributed control strategy to obtain coordinated control instruction data;

[0095] Step S5: Execute multi-area coordinated control according to the coordinated control instruction data, and evaluate the control effect to generate power grid coordinated control effect data; based on the power grid coordinated control effect data, correct the power grid control strategy through a static centralized control strategy to generate corrected coordinated control scheme data.

[0096] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of the method for coordinated control of reactive power / voltage of a distribution network with static concentration and dynamic distribution of the present invention. In this embodiment, the method for coordinated control of reactive power / voltage of a distribution network with static concentration and dynamic distribution of the present invention comprises the following steps:

[0097] Step S1: acquiring basic data of the distribution network; extracting historical fault data of the power grid according to the basic data of the distribution network to obtain historical fault data of the power grid; performing multi-scale voltage constraint analysis according to the basic data of the distribution network to generate multi-scale voltage constraint data; performing multi-scale voltage stability domain analysis on the multi-scale voltage constraint data through the historical fault data of the power grid to generate multi-scale voltage stability domain data;

[0098] In an embodiment of the present invention, the actual basic data of the distribution network in a certain area is obtained, including line parameters, transformer parameters, load data, historical fault records, etc. These data are read and stored using Python, for example, the Pandas library is used to process the tabular data, and the NetworkX library is used to build the distribution network topology. Next, the historical fault data is analyzed to extract information such as the fault type, fault location, fault time, voltage and current changes, etc. For example, voltage sag faults can be identified based on the voltage sag amplitude and duration, and the K-means algorithm can be used to perform cluster analysis on historical faults to identify common fault modes. The power system simulation software is used to simulate the distribution network under different load levels and power output conditions, and the voltage stability is analyzed in combination with the historical fault data. According to the simulation results, the voltage stability domain under different load levels and power output conditions is determined, and multi-scale voltage stability domain data is generated.

[0099] Step S2: using the distribution network topology data in the distribution network basic data to perform regional sensitivity difference analysis on the multi-scale voltage stability domain data, and generate multi-scale control sensitivity data;

[0100] In an embodiment of the present invention, the sensitivity differences of different regions to voltage control measures are analyzed in combination with multi-scale voltage stability domain data. For example, the sensitivity coefficients of different node voltages to reactive compensation, load control and other measures can be calculated using a linearized sensitivity analysis method, and the response speed and impact range of different regions to voltage control can be analyzed in combination with the network topology. The sensitivity differences of different regions to voltage control measures are analyzed in combination with multi-scale voltage stability domain data. For example, the sensitivity coefficients of different node voltages to reactive compensation, load control and other measures can be calculated using a linearized sensitivity analysis method, and the response speed and impact range of different regions to voltage control can be analyzed in combination with the network topology.

[0101] Step S3: Perform multi-dimensional mapping of global voltage according to multi-scale control sensitivity data to obtain a global voltage optimization model; perform static centralized control strategy processing according to the global voltage optimization model to generate a static centralized control strategy; perform dynamic event triggering control processing on multi-scale voltage stability domain data through historical fault data of the power grid to generate a dynamic distributed control strategy;

[0102] In an embodiment of the present invention, the distribution network voltage control problem is mapped into a multidimensional optimization problem. For example, the voltage stability margin, control cost, response speed of control measures and other factors in different regions can be used as optimization objective functions, and voltage constraints, equipment capacity limitations and other constraints can be used as constraints to construct a global voltage optimization model. The optimization model is solved using a static optimization method, for example, linear programming, nonlinear programming and other methods can be used to obtain the optimal reactive power / voltage control strategy for different regions at different time scales, such as determining the switching state of each reactive compensation device in different time periods, load control strategy, etc., and generating a static centralized control strategy. Identify the changing rules of the voltage stability domain when different types of faults occur. For example, historical fault data can be classified according to factors such as fault type, fault location, and fault degree, and the changing trend of the voltage stability margin when different types of faults occur can be analyzed to construct a dynamic event trigger control rule base.

[0103] Step S4: acquiring real-time status monitoring data of the distribution network to obtain real-time operation status monitoring data; performing monitoring event trigger identification based on the real-time operation status monitoring data to obtain event warning signal data; performing inter-regional coordinated control processing on the event warning signal data using a static centralized control strategy and a dynamic distributed control strategy to obtain coordinated control instruction data;

[0104] In an embodiment of the present invention, the distribution network is monitored in real time, for example, data such as line current, voltage, power, etc. are collected, and synchronized phasor data is obtained using PMU and other equipment. The collected data is uploaded to the control center, and the raw data is cleaned, analyzed and processed using data processing technology, such as data smoothing, bad value processing, state estimation, etc., to obtain real-time operation status monitoring data. The real-time operation status monitoring data is analyzed using machine learning and other methods, such as building an event recognition model based on a support vector machine (SVM) to identify events such as abnormal voltage fluctuations, frequency fluctuations, and load mutations, determine whether to trigger an early warning signal, and generate event early warning signal data. According to the event early warning signal data, combined with the static centralized control strategy and the dynamic distributed control strategy, inter-regional coordinated control processing is performed. For example, when a voltage sag event is detected in a certain area, first, it is determined whether the dynamic distributed control strategy needs to be started according to the severity and impact range of the event. If the impact range of the event is small and the system can be restored to stability through static adjustment, a static centralized control strategy is adopted, such as adjusting the output of the reactive power compensation device in the area, or adjusting the load level of the adjacent area to maintain voltage stability. If the event has a large impact and causes system instability, it is necessary to activate the dynamic distributed control strategy. For example, according to pre-set rules, some non-important loads can be quickly removed, or the emergency support function of distributed power sources can be activated to prevent the spread of voltage collapse and buy time for system recovery.

[0105] Step S5: Execute multi-area coordinated control according to the coordinated control instruction data, and evaluate the control effect to generate power grid coordinated control effect data; based on the power grid coordinated control effect data, correct the power grid control strategy through a static centralized control strategy to generate corrected coordinated control scheme data.

[0106] In an embodiment of the present invention, according to the coordinated control instruction data, the control center issues instructions to perform multi-region coordinated control. For example, the switching state of the reactive compensation device is remotely adjusted through the control terminal, or the user load is adjusted through the load management system to achieve real-time control of voltage / reactive power. During the control process, the control effect is monitored in real time, such as collecting data such as voltage, current, power, etc. of each node, and analyzing indicators such as voltage stability margin, control cost, and control response speed, evaluating the control effect, and generating power grid coordinated control effect data. Based on the power grid coordinated control effect data, the static centralized control strategy is corrected. For example, if it is found that some control strategies are not effective, or the voltage control pressure in some areas is large, it is necessary to adjust the static centralized control strategy, such as modifying the switching strategy of the reactive compensation device, adjusting the load control strategy, etc., to generate corrected coordinated control scheme data to improve the robustness and adaptability of the control system.

[0107] Preferably, step S1 comprises the following steps:

[0108] Step S11: Obtain basic data of the distribution network, including distribution network topology data and historical distribution network operation status data;

[0109] Step S12: preprocessing the historical distribution network operation status data to generate clean historical operation status data;

[0110] Step S13: Analyze the historical faults of the power grid according to the historical cleaning operation status data to obtain the historical fault data of the power grid;

[0111] Step S14: performing multi-scale voltage constraint analysis according to the cleaning history operation status data to generate multi-scale voltage constraint data;

[0112] Step S15: Calculate the static voltage stability margin of the distribution network topology data using the multi-scale voltage constraint data to obtain static voltage stability margin data; construct a static voltage stability domain for the distribution network topology data based on the static voltage stability margin data to generate static voltage stability domain data;

[0113] Step S16: Performing a dynamic disturbance scenario response on the static voltage stability domain data through the historical fault data of the power grid to generate dynamic voltage stability margin data; constructing a dynamic voltage stability domain for the distribution network topology data based on the dynamic voltage stability margin data to generate dynamic voltage stability domain data;

[0114] Step S17: performing multi-scale voltage domain fusion according to the static voltage stability domain data and the dynamic voltage stability domain data to generate multi-scale voltage stability domain data.

[0115] In an embodiment of the present invention, the geographic information system (GIS) data of the target distribution network, the SCADA data of the substation and the line, the smart meter data, and the historical fault records are collected. For example, the GIS data of the 10kV distribution network in a certain area is obtained, including information such as line length, conductor model, and tower location; the SCADA data of the substation and the line is obtained, including information such as voltage, current, power, and switch status, with a sampling frequency of 1 minute; the smart meter data is obtained, including information such as user power consumption and voltage, with a sampling frequency of 15 minutes; the historical fault records of the past year are obtained, including information such as fault time, fault location, and fault type. The acquired historical distribution network operation status data is cleaned using data preprocessing technology. For example, linear interpolation can be used to fill in missing values ​​in SCADA data and smart meter data. Analyze the sudden changes in data such as voltage and current, identify events such as voltage sags and short circuit faults, and record information such as the time, location, duration, and impact range of the fault. For example, the K-means algorithm is used to divide historical faults into different types, such as single-phase ground short circuit, two-phase short circuit, three-phase short circuit, etc., and the statistical characteristics such as the frequency and severity of different types of faults are analyzed. Set different load levels and power output conditions. Perform multi-scale voltage constraint analysis, including: node voltage upper and lower limits: set the allowable range of each node voltage to ensure voltage quality and equipment safety. Voltage deviation: set the allowable deviation between each node voltage to ensure voltage balance. Other constraints: such as line flow restrictions, transformer capacity restrictions, etc. The continuous power flow method can be used to gradually increase the load level, calculate the system voltage distribution at each load level, and determine whether it meets the multi-scale voltage constraint requirements set in step S14. When the system voltage exceeds the constraint range, it is considered that the system is unstable, the load level at this time is recorded, and the difference between the load level and the initial load level is calculated as the static voltage stability margin under this condition. The static voltage stability margin under different load levels is plotted into a curve, which is the static voltage stability margin curve. According to the static voltage stability margin curve, the static voltage stability domain under different voltage constraint levels can be constructed. For example, the area where the voltage stability margin is greater than or equal to 0 can be defined as a safe and stable domain, and the area where the voltage stability margin is less than 0 can be defined as an unstable domain. Use power system simulation software, such as PSD-BPA, to simulate and calculate each fault scenario to obtain the dynamic response curves of system voltage, current, power, etc. Analyze the dynamic response curves and calculate the dynamic voltage stability margin under each fault scenario. For example, the critical fault clearing time (CCT) can be used as an indicator of the dynamic voltage stability margin, that is, the maximum fault duration at which the system can remain stable. The area where the CCT is greater than the preset threshold can be defined as a safe and stable domain, and the area where the CCT is less than the preset threshold can be defined as an unstable domain.Based on the multi-scale voltage stability domain, the areas and time periods with weak voltage stability in the distribution network can be identified, and more refined control strategies can be formulated for these areas and time periods, such as increasing reactive power compensation capacity, optimizing load control strategies, etc., to improve the overall voltage stability level of the distribution network.

[0116] Preferably, step S14 comprises the following steps:

[0117] Step S141: performing time multi-scale aggregation processing on the cleaning history operation status data to generate multi-scale operation status data, wherein the multi-scale operation status data includes second-level operation status data, hierarchical operation status data, and hour-level operation status data;

[0118] Step S142: performing a short-term voltage sag value analysis on the second-level operation status data to obtain short-term voltage characteristic data; performing a mid-term voltage deviation fluctuation analysis on the graded operation status data to obtain mid-term voltage characteristic data; performing a long-term voltage stability margin analysis on the hour-level operation status data to obtain long-term voltage characteristic data;

[0119] Step S143: identifying voltage sag boundaries according to short-term voltage characteristic data, and generating load voltage sag range data; identifying voltage fluctuation boundaries according to medium-term voltage characteristic data, and generating load voltage deviation range data; identifying load voltage stability margin range according to long-term voltage characteristic data, and generating load voltage margin range data;

[0120] Step S144: performing boundary constraint condition quantization processing on the load voltage sag range data, the load voltage deviation range data and the load voltage margin range data to generate voltage quantization boundary condition data;

[0121] Step S145: performing multi-scale constraint fusion on the voltage quantization boundary condition data through the multi-scale operating status data to generate multi-scale voltage constraint data.

[0122] In an embodiment of the present invention, a multi-scale time series analysis method is used to process the historical distribution network operation status data after cleaning, and generate second-level, graded and hour-level operation status data. For example, the moving average method is used to set the time window to 1 second, 1 minute and 1 hour respectively, and the original data is subjected to sliding average calculation to obtain operation status data of different time scales. The second-level operation status data is analyzed to identify voltage sag events, and the sag amplitude, duration and other information are recorded. For example, a threshold method can be used to set the voltage sag threshold to 90% of the nominal voltage. When the voltage is lower than the threshold, it is determined that a voltage sag event has occurred. The graded operation status data is analyzed to calculate indicators such as voltage deviation and voltage fluctuation rate. For example, the standard deviation statistical method can be used to calculate the standard deviation of the voltage data per minute as the voltage fluctuation rate of the minute to reflect the severity of the voltage fluctuation. The hour-level operation status data is analyzed to calculate indicators such as the voltage stability margin. For example, the PV curve method can be used to calculate the critical point of system voltage collapse according to the relationship between load level and voltage, and the difference between the voltage corresponding to the critical point and the current voltage is used as the voltage stability margin to reflect the degree of system voltage collapse. According to the short-term voltage characteristic data extracted in step S142, such as the voltage sag amplitude and duration, combined with the actual operation experience and relevant standards of the distribution network, the allowable voltage sag range under different load levels is determined. For example, for important loads, the allowable voltage sag amplitude and duration are smaller than those of general loads. According to the medium-term voltage characteristic data, such as voltage deviation and voltage fluctuation rate, combined with relevant standards, the allowable voltage deviation range under different load levels is determined. For example, the upper limit of voltage deviation under different load levels can be set according to the voltage qualified range specified in the national standard. According to the long-term voltage characteristic data, such as voltage stability margin, combined with the safe operation requirements of the distribution network, the allowable voltage stability margin range under different load levels is determined. The voltage sag range can be expressed as the upper limit value of the voltage sag amplitude and duration, the voltage deviation range can be expressed as the upper limit value of the voltage deviation, and the voltage stability margin range can be expressed as the lower limit value of the voltage stability margin. The second-level operation status data is associated with the load voltage sag range data, the graded operation status data is associated with the load voltage deviation range data, and the hour-level operation status data is associated with the load voltage margin range data. For example, for a specific moment, the corresponding load voltage sag range, load voltage deviation range, and load voltage margin range can be found according to the second-level, graded, and hour-level time periods of the moment, and these ranges are used as multi-scale voltage constraints at the moment.

[0123] As an example of the present invention, refer to Figure 2 As shown, Figure 1 Detailed implementation steps of step S2 in the embodiment are shown in the flowchart. In this embodiment, step S2 includes:

[0124] Step S21: extracting voltage stability control elements from multi-scale voltage stability domain data using distribution network topology data to generate voltage control variable data;

[0125] In an embodiment of the present invention, the distribution network topology data and the multi-scale voltage stability domain data are analyzed to extract the key control factors that affect the voltage stability. These control factors may include: reactive compensation equipment: such as shunt capacitor banks (SCBs) and static VAR compensators (SVCs), etc., which can change the voltage drop of the line by adjusting the reactive power output, thereby affecting the voltage stability. Load: Different types of loads have different sensitivities to voltage changes, and the impact of the system on voltage stability can be changed by adjusting the load level or implementing demand-side response. Distributed power sources: such as photovoltaic power generation and wind power generation, etc., can have a positive or negative impact on voltage stability by adjusting the active and reactive power output, and need to be analyzed according to actual conditions. For example, the switching state of SCB, the active and reactive power of the load, the active and reactive power of the distributed power source, etc., generate voltage control variable data.

[0126] Step S22: marking the multi-scale voltage stability domain data with a stability domain state based on the multi-scale voltage constraint data, and performing hierarchical mapping to obtain multi-scale hierarchical stability domain data, wherein the multi-scale hierarchical stability domain data includes a safe operation state area, a warning operation state area, and a dangerous operation state area;

[0127] In an embodiment of the present invention, based on the multi-scale voltage constraint data, the multi-scale voltage stability domain data is marked in state. For example, safe operation state area: when the system operation point is located in this area, the voltage constraint conditions under all time scales are met, and the system operation is safe and stable. Warning operation state area: when the system operation point is located in this area, the voltage constraint conditions under some time scales are met, but there are certain risks, and it is necessary to pay close attention to the system state and take necessary preventive measures. Dangerous operation state area: when the system operation point is located in this area, the voltage constraint conditions under any time scale are not met, and there is a great risk in the system operation, and emergency control measures need to be taken immediately to prevent voltage collapse. For example, the state where the voltage stability margin is greater than 10% is marked as a safe operation state, the state where the voltage stability margin is between 5% and 10% is marked as a warning operation state, and the state where the voltage stability margin is less than 5% is marked as a dangerous operation state. The multi-dimensional space mapping method is used to map different operation states into a multi-dimensional space to generate multi-scale hierarchical stability domain data.

[0128] Step S23: performing regional sensitivity difference analysis on the multi-scale layered stable domain data to generate regional sensitivity difference data;

[0129] In the embodiment of the present invention, an analytical method or a numerical method is used to calculate the sensitivity coefficients of the voltage in different regions to various control variables, such as the sensitivity of the voltage to the reactive compensation capacity, the sensitivity of the voltage to the load power, etc. A small disturbance is applied to each control variable, the change of the voltage in different regions is observed, and the sensitivity of the region to the control variable is judged according to the voltage change amplitude. For example, the area close to the main grid usually has a higher voltage stability and a lower sensitivity to control measures; while the area far away from the main grid has a higher sensitivity to control measures due to the larger line impedance and poor voltage stability.

[0130] Step S24: performing a layered stability domain disturbance analysis on the multi-scale layered stability domain data through the voltage control variable data based on the regional sensitivity difference data to generate control sensitivity matrix data;

[0131] In an embodiment of the present invention, a layered stable domain disturbance analysis is performed on multi-scale layered stable domain data. For example, a certain disturbance is applied to each control variable, the change of voltage stability margin in different regions at different time scales is observed, and the relationship between the disturbance amount and the change amount of voltage stability margin is recorded. The disturbance analysis results of all control variables are summarized to construct a control sensitivity matrix. The rows of the matrix represent different control variables, the columns represent different regions and time scales, and the matrix elements represent the degree of influence of the corresponding control variable on the voltage stability margin of the corresponding region and time scale.

[0132] Step S25: Perform multi-scale matrix decoupling according to the control sensitivity matrix data to obtain multi-scale control sensitivity data.

[0133] In the embodiment of the present invention, a matrix decomposition technology such as singular value decomposition (SVD) can be used to decompose the control sensitivity matrix into multiple sub-matrices, each of which corresponds to a time scale and reflects the degree of influence of the control variables on the voltage stability of different regions under the time scale. For example, the control sensitivity matrix can be decomposed into a second-level control sensitivity matrix, a hierarchical control sensitivity matrix, and an hour-level control sensitivity matrix, which are used to guide the formulation of short-term, medium-term, and long-term voltage control strategies, respectively.

[0134] Preferably, step S24 includes the following steps:

[0135] Step S241: performing control variable disturbance processing on the voltage control variable data based on the regional sensitivity difference data to generate disturbance control variable data;

[0136] Step S242: performing a stable domain state variation analysis on the multi-scale layered stable domain data by using the disturbance control variable data to generate stable domain disturbance response curve data;

[0137] Step S243: Calculate the sensitivity coefficient of the multi-scale layered stable domain data using the disturbance control variable data and the stable domain disturbance response curve data to generate a control sensitivity coefficient;

[0138] Step S244: extracting regional sensitivity features from the multi-scale layered stable domain data by controlling the sensitivity coefficient, and performing control point feature space mapping on the voltage control variable data to generate control point feature space data;

[0139] Step S245: Calculating the regional correlation degree according to the control point feature space data to generate a control region correlation matrix;

[0140] Step S246: Perform multi-region weighted processing on the control sensitivity coefficients through the control region correlation matrix, and construct a sensitivity matrix to generate control sensitivity matrix data.

[0141] In an embodiment of the present invention, for example, a certain control variable is selected, and a small disturbance is superimposed on its reference value. The disturbance can be a fixed value or a random value. For example, assuming that the reactive compensation capacity of a certain area is 10 MVar, it can be disturbed to 10.5 MVar or 9.5 MVar, and the influence of increasing and decreasing the reactive compensation capacity on the voltage stability of the area is observed respectively. The disturbance control variable data are respectively substituted into the power system simulation model, such as OpenDSS, to perform flow calculation, and obtain the change of multi-scale layered stable domain data under different disturbance conditions. For example, the boundary changes of the safe operation state area, the warning operation state area and the dangerous operation state area corresponding to each disturbance control variable value are recorded. Using the disturbance control variable data and the stable domain disturbance response curve data, the sensitivity coefficient of each control variable to the stable domain state of each area and time scale is calculated. For example, the difference method can be used to calculate the ratio of the stable domain state change to the disturbance control variable change as the sensitivity coefficient of the control variable to the area and time scale. Regional sensitivity feature extraction is performed on the multi-scale layered stable domain data. For example, the sensitivity coefficient of each region at different time scales can be used as the feature vector of the region. Then, the voltage control variable data is mapped to the feature space, for example, the sensitivity coefficient of each control variable is used as the coordinate of the control variable in the feature space. Finally, the control point feature space data is generated. The distance or similarity of different regions in the feature space can be calculated by using methods such as Euclidean distance, Manhattan distance or cosine similarity, and converted into correlation. The closer the distance or the higher the similarity, the greater the correlation. The sensitivity coefficient of each control variable to a certain region is multiplied by the correlation between the region and other regions to obtain the weighted sensitivity coefficient. The weighted sensitivity coefficient is constructed into a matrix form, which is the control sensitivity matrix data. The matrix comprehensively considers the impact of the control variable on different regions and the correlation between different regions, and can more accurately reflect the impact of the control variable on the voltage stability of the entire distribution network.

[0142] As an example of the present invention, refer to Figure 2 As shown, Figure 1 Detailed implementation steps of step S3 in the flowchart, in this example, step S3 includes:

[0143] Step S31: constructing a multi-objective optimization function according to preset static voltage optimization target data, and performing multi-dimensional mapping of the global voltage multi-objective function based on multi-scale control sensitivity data to obtain a global voltage optimization model;

[0144] In an embodiment of the present invention, first, a multi-objective optimization function is constructed according to preset static voltage optimization objectives, such as minimizing voltage deviation, minimizing network loss, maximizing voltage stability margin, etc. The multi-objective optimization function is a corresponding conventional mathematical calculation function. For example, the voltage deviation can be defined as the sum of the squares of the differences between the voltages of each node and the nominal voltage, the network loss can be defined as the active power loss on the line, and the voltage stability margin can be defined as the maximum load disturbance that the system can withstand. Then, the multi-objective optimization function is mapped to the value space of the voltage control variable using multi-scale control sensitivity data. For example, the sensitivity coefficient of each control variable can be used as a weight and multiplied by the corresponding objective function to obtain the contribution of the control variable to the objective function. The global voltage multi-objective function can be obtained by weighting and summing the contributions of all control variables.

[0145] Step S32: setting distribution network constraints according to the distribution network topology data to generate distribution network constraint data, wherein the distribution network constraint data includes equipment operation constraint data, safety constraint data and power grid operation standard constraint data;

[0146] In an embodiment of the present invention, distribution network constraints are set according to distribution network topology data and actual operation experience, including: equipment operation constraints: for example, capacity limits of reactive power compensation equipment, operation times limits of switchgear, output limits of distributed power sources, etc. Safety constraints: for example, current thermal capacity limits of lines and transformers, permissible ranges of node voltages, permissible ranges of system frequencies, etc. Grid operation standard constraints: for example, limits on indicators such as voltage qualification rate, voltage fluctuation rate, and harmonic content specified in the Technical Specifications for Voltage and Reactive Power Control of Distribution Networks issued by the State Grid Corporation of China. The above constraints are converted into mathematical expressions, such as equality constraints or inequality constraints, to generate distribution network constraint data.

[0147] Step S33: constructing a multi-objective optimization problem for the distribution network constraint condition data through a global voltage optimization model to generate voltage control optimization problem data;

[0148] In an embodiment of the present invention, the global voltage optimization model and the distribution network constraint data are integrated using data analysis software to construct voltage control optimization problem data. The problem includes the objective function and constraint conditions of the global voltage optimization model, as well as the distribution network constraint condition data. The optimization variables of the voltage control optimization problem include the adjustment amount of each reactive compensation device, as well as other adjustable parameters.

[0149] Step S34: solving the voltage control optimization problem data for static voltage control values ​​using a preset multi-objective optimization algorithm based on the global voltage optimization model, and performing static centralized control strategy processing to generate a static centralized control strategy;

[0150] In the embodiment of the present invention, a suitable preset multi-objective optimization algorithm is selected, such as a genetic algorithm, a particle swarm algorithm, an ant colony algorithm, etc., to solve the constructed voltage control optimization problem and obtain a Pareto optimal solution set, which contains the optimal voltage control variable value scheme under the trade-off of different objective functions. According to actual needs, an optimal solution is selected from the Pareto optimal solution set and converted into specific control instructions, such as adjusting the switching state of reactive compensation equipment, adjusting the load level, controlling the output of distributed power sources, etc., to generate a static centralized control strategy.

[0151] Step S35: Perform dynamic event triggering control processing on the multi-scale voltage stability domain data through the historical fault data of the power grid to generate a dynamic distributed control strategy.

[0152] In an embodiment of the present invention, historical fault data of the power grid and multi-scale voltage stability domain data are analyzed to identify typical fault events that cause voltage instability, such as sudden access of large loads, line faults, generator tripping, etc. A corresponding dynamic distributed control strategy is formulated for each typical fault event. For example, a load control strategy can be pre-set. When a large load access is detected, some non-important loads are automatically cut off to reduce load shock and maintain voltage stability. A fault isolation strategy can be pre-set. When a line fault is detected, the faulty line is quickly isolated to prevent the fault from spreading, and the backup line or distributed power source is started to restore power supply. A backup power supply startup strategy can be pre-set. When a generator trip is detected, a backup power supply, such as an energy storage system, a gas turbine, etc., is quickly started to make up for the lack of generator output and maintain voltage stability.

[0153] Preferably, step S35 includes the following steps:

[0154] Step S351: extracting fault event features based on historical fault data of the power grid to generate historical event feature data;

[0155] Step S352: performing event impact assessment on historical event feature data using multi-scale voltage stability domain data to obtain fault event impact assessment data;

[0156] Step S353: Distributed control resources are identified according to the fault event impact assessment data, and a mapping relationship between events and control resources is established to obtain event-resource mapping data;

[0157] Step S354: dividing the event impact range according to the event-resource mapping data, and sorting the event response priorities to generate event response priority list data;

[0158] Step S355: evaluating the control resource response capability of the event response priority list data through the event-resource mapping data, and setting the control resource priority to obtain event control resource priority data;

[0159] Step S356: Dynamically matching the event response priority list data and the event control resource priority data through a preset power grid dynamic control rule library to obtain event-resource-control rule data;

[0160] Step S357: Perform dynamic distributed control strategy processing according to the event-resource-control strategy mapping data to obtain a dynamic distributed control strategy.

[0161] In an embodiment of the present invention, data mining technology can be used to identify different types of fault events from historical fault records, such as single-phase ground short circuit, three-phase short circuit, voltage sag, voltage swell, etc., and extract characteristic information of each fault event to build a historical event feature database. According to the position of the system operating point in the multi-scale voltage stability domain when the fault occurs, it is determined whether the fault event will cause the system to enter a warning state or a dangerous state, and the decrease in the system voltage stability margin is calculated as a quantitative indicator of the impact of the fault event. According to the actual situation of the distribution network, resources that can be used for dynamic distributed control, such as controllable loads, energy storage systems, distributed power sources, etc., are identified, and the technical characteristics and control capabilities of each control resource, such as response speed, adjustment range, control accuracy, etc., are analyzed. According to the fault event impact assessment data obtained in step S352, a mapping relationship between fault events and control resources is established. For example, it is possible to determine which control resources can effectively respond to such fault events based on factors such as fault type, fault location, and impact range, and store this information in an event-resource mapping database. According to the fault event impact assessment data obtained in step S352, the impact scope of different fault events is divided, for example, an event whose impact scope is limited to a single load point is defined as a small event, and an event whose impact scope covers multiple load points or the entire line is defined as a large event. Then, the response priority of different events is sorted according to the impact scope and severity of the event. For example, events with a large impact scope and a high severity can be set as high priority events and processed first. According to the event-resource mapping data, the response capabilities of different control resources to different types of fault events are analyzed, such as response speed, adjustment accuracy, control cost, etc. The priorities of different control resources are set according to the response capability and cost of the control resources. For example, control resources with fast response speed, high control accuracy and low cost can be set as high priority resources and called first. A power grid dynamic control rule base is established in advance, and the rule base contains preset control strategies for different types of fault events, such as load control strategy, voltage control strategy, frequency control strategy, etc. For example, for high-priority large fault events, the control strategy of rapid load shedding can be used first to quickly suppress voltage drops and prevent system crashes; for low-priority small fault events, the control strategy of adjusting reactive compensation equipment can be used to fine-tune the voltage and maintain system stability. Generate the final dynamic distributed control strategy. This strategy contains specific control instructions for different types of fault events, such as which loads to control, which reactive compensation equipment to adjust, and which backup power sources to start.

[0162] Preferably, step S4 comprises the following steps:

[0163] Step S41: acquiring real-time status monitoring data of the distribution network to obtain real-time operation status monitoring data;

[0164] Step S42: performing monitoring event trigger identification according to the real-time operation status monitoring data, and generating an event warning signal to obtain event warning signal data;

[0165] Step S43: analyzing the load and power distribution characteristics of the real-time operation status monitoring data through the distribution network topology data to generate power grid load and power distribution characteristic data;

[0166] Step S44: Based on the power grid load and power distribution characteristic data, the event warning signal data is used to adaptively divide the distribution network topology data into control areas to obtain adaptive control area data;

[0167] Step S45: using the dynamic distributed control strategy and the static centralized control strategy to perform inter-regional coordinated control processing on the adaptive control regional data to generate real-time distributed coordinated control data;

[0168] Step S46: mapping power grid control variables according to the real-time distributed coordinated control data, and generating control instructions to obtain coordinated control instruction data.

[0169] In the embodiment of the present invention, sensors, smart meters and other data acquisition devices are deployed at key nodes of the distribution network to collect the operation status data of the distribution network in real time, including information such as voltage, current, power, frequency, and switch status. The data collected by the monitoring system and the data acquisition device are integrated to form real-time operation status monitoring data, which contains the real-time operation status information of each node and device of the distribution network. The acquired real-time operation status monitoring data is analyzed by data analysis to identify abnormal events that affect voltage stability and generate corresponding early warning signals. For example, thresholds of indicators such as voltage, current, and frequency can be set. When the monitoring data exceeds the preset threshold, an early warning signal is triggered, and the severity of the event is judged according to the over-limit amplitude and duration, and early warning information of different levels is generated. The distribution network topology data and the real-time operation status monitoring data are used to analyze the geographical distribution characteristics of the load and power supply in the distribution network, such as load density, power supply type, power supply output, etc. For example, the distribution network topology data can be associated with the geographical information by using the geographic information system (GIS) technology, and the load and power supply data monitored in real time can be superimposed on the GIS platform to intuitively display the geographical distribution of the load and power supply. According to the impact range of the fault event, load density, power distribution and other factors, the distribution network is divided into multiple control areas, and the load and power in each area are kept balanced as much as possible to achieve autonomous control in the area. For example, for the area where a large fault occurs, it can be divided into an independent control area, and the control resources in the area are preferentially called for processing to reduce the impact on other areas; for the area where no fault occurs, the control area boundary can be dynamically adjusted according to the distribution of load and power to optimize resource allocation and improve system operation efficiency. For example, for the control area where the fault occurs, the dynamic distributed control strategy is preferentially adopted to quickly respond to the fault event and suppress voltage fluctuations; for the control area where no fault occurs, the static centralized control strategy is adopted to optimize the system operation state and improve the voltage stability margin. The control instructions are sent to the corresponding control equipment, such as controlling the switching of reactive compensation equipment through the remote terminal unit (RTU), controlling the removal of controllable loads through the load management system, and controlling the output of distributed power sources through the distributed energy management system.

[0170] Preferably, step S45 includes the following steps:

[0171] Step S451: Perform dynamic event feature analysis on the event warning signal data to generate dynamic event feature data; perform event dynamic classification on the dynamic event feature data using a preset event response threshold to obtain event dynamic level label data;

[0172] Step S452: extracting static strategy parameters according to the static centralized control strategy to obtain static strategy parameter data;

[0173] Step S453: Based on the static strategy parameter data, the dynamic event feature data is subjected to static control scenario fitting through the event dynamic level label data to obtain a real-time static fitting control value;

[0174] Step S454: activating the dynamic distributed control strategy through the event dynamic level tag data, and adjusting the distributed control variables of the adaptive control area data using the real-time static fitting control value as the dynamic control reference value to obtain dynamic control adjustment data;

[0175] Step S455: performing event situation awareness prediction on the dynamic control adjustment data through a static centralized control strategy to generate event situation prediction data;

[0176] Step S456: Based on the event situation prediction data, the static centralized control strategy is used to perform global static centralized control optimization on the adaptive control area data to obtain global static control optimization data;

[0177] Step S457: Perform collaborative control processing according to the dynamic control adjustment data and the global static control optimization data to generate real-time distributed coordinated control data.

[0178] In an embodiment of the present invention, a cluster analysis method can be used to classify different types of events according to the similarity of dynamic event feature data, such as voltage sag events, voltage swell events, frequency fluctuation events, etc. Then, event response thresholds are pre-set, such as voltage deviation thresholds, frequency deviation thresholds, load impact thresholds, etc., and events are dynamically classified according to whether the dynamic event feature data exceeds the preset threshold, such as first-level events, second-level events, third-level events, etc., to generate event dynamic level label data. The static centralized control strategy can be represented as a series of rules, each rule containing trigger conditions and control actions, such as when the voltage in a certain area is lower than 0.95 nominal voltage, the reactive power compensation equipment in the area is triggered to be put into operation. The extracted dynamic event feature data is used as input, and the selected static strategy parameter data is used to calculate the real-time static fitting control value under the current event scenario, such as the switching instruction of the reactive power compensation equipment, the execution instruction of the load control, etc. For example, the fuzzy control method can be used to determine the weights of different control parameters according to the event dynamic level label data, and the fuzzy membership function is calculated according to the dynamic event feature data, and finally the real-time static fitting control value is obtained. Then, the obtained real-time static fitting control value is used as a reference value, combined with the adaptive control area data, to adjust the control variables in the dynamic distribution control strategy, for example, according to the real-time static fitting control value, adjust the removal ratio of the controllable load, the charging and discharging power of the energy storage system, etc., to generate dynamic control adjustment data. For example, the power system simulation software can be used to simulate the dynamic response of the system under different control strategies, predict the changing trend of indicators such as voltage, frequency, and power flow, and the security risks that arise. According to the event situation prediction data, it is judged whether the current static centralized control strategy can effectively respond to the development of the event. If the prediction results show that the current strategy cannot meet the needs of safe and stable operation of the system, it is necessary to optimize and adjust the static centralized control strategy. For example, the model predictive control (MPC) method can be used to rollingly optimize the static centralized control strategy for a period of time in the future according to the event situation prediction data, such as adjusting the switching plan of the reactive compensation equipment and the execution order of the load control, to generate global static control optimization data. The dynamic control adjustment data and the global static control optimization data are integrated to generate a comprehensive control strategy, which can not only quickly respond to fault events and suppress voltage fluctuations, but also optimize the overall operation state of the system and improve the voltage stability margin.

[0179] Preferably, step S5 comprises the following steps:

[0180] Step S51: sending the coordinated control instruction data through the communication network, and executing multi-region coordinated control to obtain control execution feedback data;

[0181] Step S52: performing real-time monitoring of multiple indicators according to the control execution feedback data, and generating real-time adjustment monitoring indicator data;

[0182] Step S53: evaluating the voltage stability of the distribution network by adjusting the monitoring index data in real time, and generating grid adjustment voltage evaluation data;

[0183] Step S54: Analyze the reactive power loss of the power grid according to the control adjustment effect index data to obtain the reactive power loss data of the power grid;

[0184] Step S55: evaluating the effect of grid coordination control according to the grid adjustment voltage evaluation data and the grid reactive power loss data, and generating grid coordination control effect data;

[0185] Step S56: Perform voltage over-limit suppression analysis based on the power grid coordinated control effect data, and use the static centralized control strategy to correct the power grid control strategy to generate corrected coordinated control solution data.

[0186] In an embodiment of the present invention, the control center sends the coordinated control instruction data generated in step S46 to each control device, such as reactive compensation equipment, load controller, distributed power supply controller, etc., through the distribution automation system, using optical fiber communication, power line carrier communication, etc. After receiving the instruction, each control device performs a corresponding control action, such as adjusting reactive output, removing / restoring load, adjusting active / reactive output, etc. After receiving the instruction, each control device performs a corresponding control action, such as adjusting reactive output, removing / restoring load, adjusting active / reactive output, etc. According to the control execution feedback data and the real-time operation status monitoring data, the key indicators are monitored in real time, such as voltage level, frequency deviation, load power, reactive loss, etc., and compared with the indicators before control, the change amount of the indicators is calculated, and the real-time adjustment monitoring indicator data is generated. The voltage stability of the distribution network is evaluated. For example, indicators such as voltage qualification rate, voltage deviation rate, voltage fluctuation rate, etc. can be calculated, and compared with the indicators before control to evaluate the control effect. For example, the voltage qualification rate can be defined as the ratio of the duration of the voltage within the permissible range to the total monitoring duration, the voltage deviation rate can be defined as the average value of the voltage deviation, and the voltage fluctuation rate can be defined as the standard deviation of the voltage change. The flow calculation method can be used to calculate the active power and reactive power flow on the line according to the voltage and current data monitored in real time, and analyze the distribution of reactive power loss. The overall coordinated control effect is evaluated. For example, a comprehensive evaluation index system can be constructed to weighted average the voltage stability index and the reactive power loss index to obtain a comprehensive evaluation result. For example, the voltage qualification rate, voltage deviation rate, voltage fluctuation rate, reactive power loss rate and other indicators can be standardized, and the weights can be set according to actual needs, and the weighted average value can be calculated as a comprehensive evaluation index. According to the evaluation results of the coordinated control effect of the power grid in step S55, it is judged whether the current control strategy achieves the expected goal. If the control effect is not ideal, for example, the voltage stability index does not reach the expected goal, or the reactive power loss is too high, the control strategy needs to be corrected. For example, the voltage over-limit situation can be analyzed to identify areas with weak voltage control, and the static centralized control strategy can be adjusted for these areas, such as adjusting the switching threshold of reactive compensation equipment, optimizing load control strategy, etc., to generate revised coordinated control scheme data.

[0187] The beneficial effect of the present application is that the voltage characteristics of the distribution network at the second, grade and time scales are extracted by using a multi-scale time series analysis method, and the static and dynamic voltage stability domains are constructed in combination with historical fault data and voltage constraint standards, so as to accurately identify the areas and time periods with weak voltage stability in the distribution network. The combination of multi-scale voltage constraint analysis and multi-scale voltage stability domain analysis enables the voltage stability of the power grid under different operating conditions to be deeply evaluated, ensuring the safe operation boundary of the distribution network. It provides a scientific basis for the formulation of preventive measures and optimization control strategies, and effectively improves the fault defense and emergency response capabilities of the power grid. Through regional sensitivity difference analysis, the degree of influence of different regions on voltage stability is identified. The scheme uses power system simulation software to simulate and analyze the impact of voltage changes in different regions on system stability, and combines data analysis software to identify the voltage sensitivity differences in different regions, and uses multi-scale control sensitivity data to construct a global voltage optimization model, and formulates static centralized control strategies and dynamic distributed control strategies. The scheme uses mathematical programming software to construct a multi-objective optimization function based on the preset static voltage optimization target data, and based on the multi-scale control sensitivity data, performs multi-dimensional mapping of the global voltage multi-objective function to obtain a global voltage optimization model. The static centralized control strategy is based on the global voltage optimization model and pre-formulates the optimal reactive power / voltage control scheme for different operating scenarios, such as determining the switching status of each reactive compensation device and the load control strategy in different time periods. The dynamic distributed control strategy formulates a fast-response control strategy for specific fault events, such as sudden access of large loads and line faults, such as quickly removing some non-important loads and starting the emergency support function of distributed power sources, so as to prevent the spread of voltage collapse and buy time for system recovery, effectively suppress the propagation of faults, enhance the dynamic adaptability and self-healing ability of the system, and ensure the continuity and reliability of power supply. The dynamic distributed control strategy and the static centralized control strategy are used to carry out inter-regional coordinated control and realize real-time control of reactive power / voltage of the distribution network. It effectively solves the problem that reactive power compensation and voltage regulation are inefficient in dealing with complex fault scenarios and access of distributed power sources, and it is difficult to meet the fast and dynamic power demand. According to the actual operating status of the distribution network, the control strategy is adaptively adjusted to improve the control efficiency and reliability, effectively coordinate static centralized control and dynamic distributed control, make full use of the advantages of the two control methods, and improve the control effect.

[0188] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0189] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be 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 the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for coordinated control of reactive power / voltage in a distribution network with static concentration and dynamic distribution, characterized in that: The following steps are involved: Step S1: acquiring basic data of the distribution network; extracting historical fault data of the power grid according to the basic data of the distribution network to obtain historical fault data of the power grid; performing multi-scale voltage constraint analysis according to the basic data of the distribution network to generate multi-scale voltage constraint data; performing multi-scale voltage stability domain analysis on the multi-scale voltage constraint data through the historical fault data of the power grid to generate multi-scale voltage stability domain data; Step S2: using the distribution network topology data in the distribution network basic data to perform regional sensitivity difference analysis on the multi-scale voltage stability domain data, and generate multi-scale control sensitivity data; Step S3: Perform multi-dimensional mapping of global voltage according to multi-scale control sensitivity data to obtain a global voltage optimization model; perform static centralized control strategy processing according to the global voltage optimization model to generate a static centralized control strategy; perform dynamic event triggering control processing on multi-scale voltage stability domain data through historical fault data of the power grid to generate a dynamic distributed control strategy; Step S4: acquiring real-time status monitoring data of the distribution network to obtain real-time operation status monitoring data; According to the real-time operation status monitoring data, monitoring event trigger identification is performed to obtain event warning signal data; static centralized control strategy and dynamic distributed control strategy are used to perform inter-regional coordinated control processing on the event warning signal data to obtain coordinated control instruction data; Step S5: Execute multi-area coordinated control according to the coordinated control instruction data, and evaluate the control effect to generate power grid coordinated control effect data; based on the power grid coordinated control effect data, correct the power grid control strategy through a static centralized control strategy to generate corrected coordinated control scheme data.

2. The method for coordinated control of reactive power / voltage of distribution network by static concentration and dynamic distribution according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Obtain basic data of the distribution network, including distribution network topology data and historical distribution network operation status data; Step S12: preprocessing the historical distribution network operation status data to generate clean historical operation status data; Step S13: Analyze the historical faults of the power grid according to the historical cleaning operation status data to obtain the historical fault data of the power grid; Step S14: performing multi-scale voltage constraint analysis according to the cleaning history operation status data to generate multi-scale voltage constraint data; Step S15: Calculate the static voltage stability margin of the distribution network topology data using the multi-scale voltage constraint data to obtain static voltage stability margin data; construct a static voltage stability domain for the distribution network topology data based on the static voltage stability margin data to generate static voltage stability domain data; Step S16: Performing a dynamic disturbance scenario response on the static voltage stability domain data through the historical fault data of the power grid to generate dynamic voltage stability margin data; constructing a dynamic voltage stability domain for the distribution network topology data based on the dynamic voltage stability margin data to generate dynamic voltage stability domain data; Step S17: performing multi-scale voltage domain fusion according to the static voltage stability domain data and the dynamic voltage stability domain data to generate multi-scale voltage stability domain data.

3. The method for coordinated control of reactive power / voltage of distribution network by static concentration and dynamic distribution according to claim 2, characterized in that: Step S14 includes the following steps: Step S141: performing time multi-scale aggregation processing on the cleaning history operation status data to generate multi-scale operation status data, wherein the multi-scale operation status data includes second-level operation status data, hierarchical operation status data, and hour-level operation status data; Step S142: performing a short-term voltage sag value analysis on the second-level operation status data to obtain short-term voltage characteristic data; performing a mid-term voltage deviation fluctuation analysis on the graded operation status data to obtain mid-term voltage characteristic data; performing a long-term voltage stability margin analysis on the hour-level operation status data to obtain long-term voltage characteristic data; Step S143: identifying voltage sag boundaries according to short-term voltage characteristic data, and generating load voltage sag range data; identifying voltage fluctuation boundaries according to medium-term voltage characteristic data, and generating load voltage deviation range data; identifying load voltage stability margin range according to long-term voltage characteristic data, and generating load voltage margin range data; Step S144: performing boundary constraint condition quantization processing on the load voltage sag range data, the load voltage deviation range data and the load voltage margin range data to generate voltage quantization boundary condition data; Step S145: performing multi-scale constraint fusion on the voltage quantization boundary condition data through the multi-scale operating status data to generate multi-scale voltage constraint data.

4. The method for coordinated control of reactive power / voltage of distribution network by static concentration and dynamic distribution according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: extracting voltage stability control elements from multi-scale voltage stability domain data using distribution network topology data to generate voltage control variable data; Step S22: marking the multi-scale voltage stability domain data with a stability domain state based on the multi-scale voltage constraint data, and performing hierarchical mapping to obtain multi-scale hierarchical stability domain data, wherein the multi-scale hierarchical stability domain data includes a safe operation state area, a warning operation state area, and a dangerous operation state area; Step S23: performing regional sensitivity difference analysis on the multi-scale layered stable domain data to generate regional sensitivity difference data; Step S24: performing a layered stability domain disturbance analysis on the multi-scale layered stability domain data through the voltage control variable data based on the regional sensitivity difference data to generate control sensitivity matrix data; Step S25: Perform multi-scale matrix decoupling according to the control sensitivity matrix data to obtain multi-scale control sensitivity data.

5. The method for coordinated control of reactive power / voltage of distribution network by static concentration and dynamic distribution according to claim 4, characterized in that: Step S24 includes the following steps: Step S241: performing control variable disturbance processing on the voltage control variable data based on the regional sensitivity difference data to generate disturbance control variable data; Step S242: performing a stable domain state variation analysis on the multi-scale layered stable domain data by using the disturbance control variable data to generate stable domain disturbance response curve data; Step S243: Calculate the sensitivity coefficient of the multi-scale layered stable domain data using the disturbance control variable data and the stable domain disturbance response curve data to generate a control sensitivity coefficient; Step S244: extracting regional sensitivity features from the multi-scale layered stable domain data by controlling the sensitivity coefficient, and performing control point feature space mapping on the voltage control variable data to generate control point feature space data; Step S245: Calculating the regional correlation degree according to the control point feature space data to generate a control region correlation matrix; Step S246: Perform multi-region weighted processing on the control sensitivity coefficients through the control region correlation matrix, and construct a sensitivity matrix to generate control sensitivity matrix data.

6. The method for coordinated control of reactive power / voltage of distribution network by static concentration and dynamic distribution according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: constructing a multi-objective optimization function according to preset static voltage optimization target data, and performing multi-dimensional mapping of the global voltage multi-objective function based on multi-scale control sensitivity data to obtain a global voltage optimization model; Step S32: setting distribution network constraints according to the distribution network topology data to generate distribution network constraint data, wherein the distribution network constraint data includes equipment operation constraint data, safety constraint data and power grid operation standard constraint data; Step S33: constructing a multi-objective optimization problem for the distribution network constraint condition data through a global voltage optimization model to generate voltage control optimization problem data; Step S34: solving the voltage control optimization problem data for static voltage control values ​​using a preset multi-objective optimization algorithm based on the global voltage optimization model, and performing static centralized control strategy processing to generate a static centralized control strategy; Step S35: Perform dynamic event triggering control processing on the multi-scale voltage stability domain data through the historical fault data of the power grid to generate a dynamic distributed control strategy.

7. The method for coordinated control of reactive power / voltage of distribution network by static concentration and dynamic distribution according to claim 6, characterized in that: Step S35 includes the following steps: Step S351: extracting fault event features based on historical fault data of the power grid to generate historical event feature data; Step S352: performing event impact assessment on historical event feature data using multi-scale voltage stability domain data to obtain fault event impact assessment data; Step S353: Distributed control resources are identified according to the fault event impact assessment data, and a mapping relationship between events and control resources is established to obtain event-resource mapping data; Step S354: dividing the event impact range according to the event-resource mapping data, and sorting the event response priorities to generate event response priority list data; Step S355: evaluating the control resource response capability of the event response priority list data through the event-resource mapping data, and setting the control resource priority to obtain event control resource priority data; Step S356: Dynamically matching the event response priority list data and the event control resource priority data through a preset power grid dynamic control rule library to obtain event-resource-control rule data; Step S357: Perform dynamic distributed control strategy processing according to the event-resource-control strategy mapping data to obtain a dynamic distributed control strategy.

8. The method for coordinated control of reactive power / voltage of distribution network by static concentration and dynamic distribution according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: acquiring real-time status monitoring data of the distribution network to obtain real-time operation status monitoring data; Step S42: performing monitoring event trigger identification according to the real-time operation status monitoring data, and generating an event warning signal to obtain event warning signal data; Step S43: analyzing the load and power distribution characteristics of the real-time operation status monitoring data through the distribution network topology data to generate power grid load and power distribution characteristic data; Step S44: Based on the power grid load and power distribution characteristic data, the event warning signal data is used to adaptively divide the distribution network topology data into control areas to obtain adaptive control area data; Step S45: using the dynamic distributed control strategy and the static centralized control strategy to perform inter-regional coordinated control processing on the adaptive control regional data to generate real-time distributed coordinated control data; Step S46: mapping power grid control variables according to the real-time distributed coordinated control data, and generating control instructions to obtain coordinated control instruction data.

9. The method for coordinated control of reactive power / voltage of distribution network by static concentration and dynamic distribution according to claim 1, characterized in that: Step S45 includes the following steps: Step S451: Perform dynamic event feature analysis on the event warning signal data to generate dynamic event feature data; perform event dynamic classification on the dynamic event feature data using a preset event response threshold to obtain event dynamic level label data; Step S452: extracting static strategy parameters according to the static centralized control strategy to obtain static strategy parameter data; Step S453: Based on the static strategy parameter data, the dynamic event feature data is subjected to static control scenario fitting through the event dynamic level label data to obtain a real-time static fitting control value; Step S454: activating the dynamic distributed control strategy through the event dynamic level tag data, and adjusting the distributed control variables of the adaptive control area data using the real-time static fitting control value as the dynamic control reference value to obtain dynamic control adjustment data; Step S455: performing event situation awareness prediction on the dynamic control adjustment data through a static centralized control strategy to generate event situation prediction data; Step S456: Based on the event situation prediction data, the static centralized control strategy is used to perform global static centralized control optimization on the adaptive control area data to obtain global static control optimization data; Step S457: Perform collaborative control processing according to the dynamic control adjustment data and the global static control optimization data to generate real-time distributed coordinated control data.

10. The method for coordinated control of reactive power / voltage of distribution network by static concentration and dynamic distribution according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: sending the coordinated control instruction data through the communication network, and executing multi-region coordinated control to obtain control execution feedback data; Step S52: performing real-time monitoring of multiple indicators according to the control execution feedback data, and generating real-time adjustment monitoring indicator data; Step S53: evaluating the voltage stability of the distribution network by adjusting the monitoring index data in real time, and generating grid adjustment voltage evaluation data; Step S54: Analyze the reactive power loss of the power grid according to the control adjustment effect index data to obtain the reactive power loss data of the power grid; Step S55: evaluating the effect of grid coordination control according to the grid adjustment voltage evaluation data and the grid reactive power loss data, and generating grid coordination control effect data; Step S56: Perform voltage over-limit suppression analysis based on the power grid coordinated control effect data, and use the static centralized control strategy to correct the power grid control strategy to generate corrected coordinated control solution data.

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