Distributed reactor coordination control method and device

Abstract

The invention discloses a distributed reactor coordination control method and device, and relates to the technical field of electric power, and the method comprises the steps: power equipment is used as a network node, a distributed topology is constructed based on an edge connection relation, the node is provided with a unique label, and the edge comprises an entity edge and a virtual body edge. A distributed cluster is divided through community cluster detection, and the cluster at least comprises one reactor node. And constructing an event database, and covering and coordinating event classes and reactor parameter control information. And supervising and training a cascade decision module based on the topology, cluster and event database. Electricity data is collected, cluster greedy decision making and boundary sharing interaction are carried out in combination with a decision making module, a cascade coordination strategy is determined, and coordination control over the distributed reactor is achieved. Therefore, the technical effects of improving the control responsiveness and improving the efficiency of the power system are achieved.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a method and device for coordinated control of distributed reactors. Background Art

[0002] With the expansion of the scale of power systems and the rapid development of distributed energy, the number and complexity of power equipment have increased significantly. As one of the important power equipment, reactors play an important role in regulating grid voltage and reactive power. Existing reactor control schemes mostly rely on centralized control systems, which are managed globally by the central control center. This method is suitable for traditional centralized power systems, but for distributed power systems, due to the high real-time requirements between nodes, centralized control is prone to problems such as data transmission delays and slow decision response. In addition, some methods use independent reactor control, which fails to fully utilize the potential of overall coordinated optimization of the system, resulting in low system operation efficiency. In summary, the existing technologies have technical problems of slow control response and low coordination efficiency. Summary of the invention

[0003] The present invention provides a distributed reactor coordinated control method and device to solve the technical problems of slow control response and low coordination efficiency in the prior art, and achieve the technical effects of improving control responsiveness and increasing power system efficiency.

[0004] In a first aspect, the present invention provides a distributed reactor coordinated control method, wherein the method comprises: For distributed power systems, power equipment is used as network nodes, and a distributed topology is constructed based on edge connection relationships. Each network node based on the reactor has a unique label, and the edge connection includes physical edges based on electrical connections and virtual edges based on control relationships.

[0005] Based on the distributed topology, distributed clusters are determined by performing community cluster detection and division, wherein each distributed cluster includes at least one reactor network node.

[0006] An event database is constructed, wherein the event database covers a coordination event class - information pairs based on a predetermined schedule and event status - reactor parameter control.

[0007] Supervise and train a cascade decision module based on the distributed topology, the distributed cluster, and the event database.

[0008] The collection unit acquires the electrical data of the distributed power system, combines with the cascade decision module, performs cluster greedy decision-making and necessary boundary sharing interaction between clusters, and determines the cascade coordination strategy.

[0009] Based on the cascade coordination strategy, coordinated control of distributed reactors is performed.

[0010] In a second aspect, the present invention further provides a distributed reactor coordinated control device, wherein the device comprises: A topological connection component is used for constructing a distributed topology based on edge connection relationships for a distributed power system, with power equipment as network nodes, wherein each network node based on a reactor has a unique label, and the edge connection includes a physical edge based on an electrical connection and a virtual edge based on a control relationship.

[0011] A cluster partitioning component is used to determine distributed clusters based on the distributed topology by performing community cluster detection partitioning, wherein each distributed cluster includes at least one reactor network node.

[0012] An event organization component is used to construct an event database, which covers the coordination event class - information pairs based on a predetermined schedule and event status - reactor parameter control.

[0013] A decision forming component is used to supervise the training of a cascade decision module based on the distributed topology, the distributed cluster and the event database.

[0014] A collection decision component is used for the collection unit to obtain the electrical data of the distributed power system, and in combination with the cascade decision module, performs cluster greedy decision-making and necessary boundary sharing interaction between clusters to determine the cascade coordination strategy.

[0015] A sending execution component is used to perform coordinated control of distributed inductors based on the cascade coordination strategy.

[0016] The present invention discloses a method and device for coordinated control of distributed reactors, including: for a distributed power system, using power equipment as a network node, constructing a distributed topology structure based on an edge connection relationship, wherein each reactor network node has a unique label, and the edge connection relationship includes a physical edge based on an electrical connection and a virtual edge based on a control relationship; through a distributed topology, community cluster detection is performed and distributed clusters are divided, and each cluster contains at least one reactor network node; an event database is established, which covers coordinated event categories, information pairs based on a predetermined schedule and event status, and relevant data on reactor participation and control; according to the distributed topology, distributed clusters and event database, supervised training of a cascade decision module; a collection unit is used to obtain electrical data of a distributed power system, and in combination with a cascade decision module, greedy decisions within the cluster and boundary sharing interactions between clusters are performed to determine a cascade coordination strategy; based on the cascade coordination strategy, coordinated control of distributed reactors is performed. The method and device for coordinated control of distributed reactors disclosed by the present invention solve the technical problems of slow control response and low coordination efficiency, and achieve the technical effects of improving control responsiveness and improving power system efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of the coordinated control method of distributed reactors of the present invention; Figure 2 It is a structural schematic diagram of the distributed inductor coordinated control device of the present invention.

[0018] Explanation of reference numerals: topology connection component 11 , cluster division component 12 , event organization component 13 , decision formation component 14 , collection and decision component 15 , and dispatch execution component 16 . DETAILED DESCRIPTION

[0019] The technical solution provided in the embodiments of the present invention is to solve the technical problems of slow control response and low coordination efficiency in the prior art. The overall idea adopted is as follows: Firstly, the power equipment is taken as the network node, and a distributed topology is constructed based on the edge connection relationship, wherein each network node of the reactor has a unique label, and the edge connection includes a physical edge based on the electrical connection and a virtual edge based on the control relationship; then, based on the distributed topology, community cluster detection and division are performed to determine the distributed clusters, and each distributed cluster contains at least one reactor network node; then, an event database is constructed, covering coordination event classes, information pairs based on a predetermined schedule and event status, and reactor parameter control; then, in combination with the distributed topology, distributed clusters and event database, a cascade decision module is supervised and trained; then, the acquisition unit obtains the electrical data of the distributed power system, and performs cluster greedy decision-making and necessary boundary sharing interaction between clusters through the cascade decision module to determine the cascade coordination strategy; finally, based on the cascade coordination strategy, the distributed reactors are coordinated and controlled.

[0020] The above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods of the specification to better understand the above technical solution. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments used only to explain the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, it should be noted that, for the convenience of description, only the parts related to the present invention are shown in the drawings, rather than all of them.

[0021] Embodiment 1 Figure 1 The figure is a flow chart of a coordinated control method of a distributed reactor according to the present invention, wherein the method comprises: For distributed power systems, power equipment is used as network nodes, and a distributed topology is constructed based on edge connection relationships. Each network node based on the reactor has a unique label, and the edge connection includes physical edges based on electrical connections and virtual edges based on control relationships.

[0022] Specifically, a distributed topology of the target distributed power system is first established. The distributed topology is used to characterize the connection relationship between multiple power devices in the target distributed power system, where the connection relationship includes a physical connection relationship (based on electrical connection) and a logical connection relationship (based on control relationship). The physical edges and virtual edges in the distributed topology together constitute a complete system connection model, which helps to accurately understand and analyze the physical and control relationships in the target distributed power system.

[0023] Specifically, various types of power equipment in a distributed power system (such as generators, transformers, reactors, loads, etc.) are abstracted as network nodes, which are the basic units of operation and scheduling in the system. These nodes represent the role and position of power equipment in the system, and form a distributed topology through various connection relationships (such as electrical connections and control relationships).

[0024] Specifically, in a distributed topology, multiple network nodes are traversed for unique labeling according to the layout of the reactor. In other words, the unique label is the reactor label, which is used to identify the specific location of the node, the device type, and its role in the system.

[0025] Specifically, physical edges represent the physical connection between power devices, which are usually directly connected through transmission lines, cables, etc. These physical connections form the current and voltage transmission paths of the power system and are the basis for ensuring the normal operation of the power system. In the topological model, physical edges need to consider parameters such as voltage level, current capacity, and impedance between devices. The actual operating parameters of the power system, such as current flow and load distribution, all depend on the electrical characteristics of the physical edge connections.

[0026] Specifically, virtual edges represent the control relationship between nodes and are mainly used for dispatching, monitoring, and protection in power systems. Through virtual edges, remote monitoring equipment, intelligent control equipment, and system dispatch centers can be connected to physical nodes. Unlike physical edges, virtual edges do not involve physical electrical transmission, but manage the operating status of power equipment through information and signal transmission. For example, through virtual edges, the dispatch center can adjust the reactance to control the current and voltage changes of the transmission line to ensure the stability of the system.

[0027] Specifically, the reactor can be regarded as a regulating element on the physical side, which controls the current and voltage changes of the transmission line by adjusting the reactance to ensure the stability of the system.

[0028] In some embodiments, building a distributed topology includes: The network nodes are traversed to determine the distributed nodes based on the reactor as the topological subject, and uniquely label each node; the topological subject is identified to contextualize the non-distributed subject topological part; the distributed topology is traversed to mark the edge weights based on the control relevance of the connection.

[0029] Specifically, first traverse the nodes in the entire power network, and according to the distribution characteristics of the reactor, determine those nodes with reactors as the main nodes of the distributed topology, and output them as the topological body. Reactors are used to regulate current and voltage in power systems, so the control and management of the main nodes corresponding to the reactors is crucial to the stability of the system. In other words, the topological body and the multiple main nodes it contains are the control target objects.

[0030] Specifically, for those topological parts of non-distributed entities (i.e. nodes without reactors), they are backgrounded. This means that these nodes will not be directly controlled, but their data flow status will still be monitored and analyzed to reflect the system's power load, power changes, etc., to help optimize the regulation of reactors. For example, if the load of a non-reactor node suddenly increases, the operating status of the associated reactor node can be adjusted in a targeted manner to avoid system overload or imbalance.

[0031] Furthermore, the constructed distributed topology is traversed, and edge weights are assigned to each connecting edge (whether physical or virtual) according to the control correlation between the nodes. Among them, the control correlation reflects the degree of mutual influence between two nodes in the process of power transmission and regulation. The higher the correlation, the stronger the correlation between the two nodes in the control process, and adjusting one of the nodes may have a greater impact on the other node. For example, the weight of the physical edge reflects the physical electrical relationship between the nodes, such as the intensity of current flow, power distribution, voltage difference, etc.; the weight of the virtual edge reflects the correlation between control signals, such as the priority and scope of influence of the control command.

[0032] By marking the weight of the edge, high-weight node connections can be given priority in the control process. For example, when the control correlation between certain nodes is high, the system can give priority to adjusting these nodes to improve overall stability and power regulation efficiency.

[0033] Based on the distributed topology, distributed clusters are determined by performing community cluster detection and division, wherein each distributed cluster includes at least one reactor network node.

[0034] Specifically, a distributed cluster is a clustering result of multiple reactor network nodes in a distributed topology, and is used to divide multiple distributed discrete reactors into multiple distributed clusters according to topological relationships, wherein the multiple reactor network nodes within each distributed cluster have relatively close associations (such as nodes with strong electrical connections or control correlations).

[0035] Specifically, the distributed cluster contains at least one reactor network node. In other words, an isolated single reactor network node can also be regarded as a distributed cluster and controlled relatively independently.

[0036] Optionally, community cluster detection and division are performed, including implementation paths based on the Louvain algorithm, the Girvan-Newman algorithm, and the spectral clustering algorithm. Among them, cluster division based on the Laplace matrix of the graph is suitable for processing nonlinear cluster structures.

[0037] By identifying each distributed cluster, each cluster can be independently controlled and optimized, reducing the complexity of the whole network control, which helps to improve the reliability, flexibility and control efficiency of the system.

[0038] In some embodiments, performing community cluster detection and division includes: Based on the distributed topology, a Laplace matrix is ​​constructed; the Laplace matrix is ​​traversed to calculate eigenvalues ​​and eigenvectors, wherein the eigenvalues ​​are calculated according to the eigenvalue equation |λE-L|=0, and the eigenvalue vectors are calculated by solving the equation group (L-λE)v=0, wherein λ is the eigenvalue, E is the unit matrix, L is the Laplace matrix, and v is the eigenvalue vector; based on the eigenvalues ​​and the eigenvectors, spectral clustering processing is performed to determine the distributed cluster.

[0039] Specifically, the Laplace matrix is ​​a matrix used to represent a graph (in this case, a distributed power topology), which can effectively describe the connection relationship between nodes and the structural characteristics of the network, laying the foundation for the subsequent eigenvalue and eigenvector calculations. The Laplace matrix is ​​constructed through the adjacency matrix and the degree matrix. It represents the structural characteristics of nodes and edges in the entire distributed power system.

[0040] Specifically, after constructing the Laplace matrix, the eigenvalues ​​and eigenvectors of the Laplace matrix are calculated by eigenvalue decomposition technology. The obtained eigenvalues ​​and eigenvectors are the basis for further clustering. Among them, the eigenvalue is a scalar that describes the properties of the matrix. In spectral clustering, the eigenvalue can reflect the number and compactness of cluster structures in the distributed topology. The eigenvector is a vector related to the eigenvalue, which indicates the direction related to the eigenvalue.

[0041] Specifically, first calculate the eigenvalues ​​of the Laplace matrix by solving the following eigenvalue equation: ; in, is the eigenvalue, E is the identity matrix, and L is the Laplace matrix constructed previously.

[0042] After obtaining the eigenvalues, the corresponding eigenvectors are calculated by solving the following system of equations: ; Among them, v is the eigenvector, which indicates the direction corresponding to each eigenvalue. By traversing each element of the Laplace matrix, the eigenvector is gradually solved.

[0043] Specifically, the centrality indicators such as betweenness centrality and degree centrality are calculated through the eigenvalues ​​and eigenvectors of the Laplace matrix, so as to understand the important nodes and connection methods in the distributed topology.

[0044] Furthermore, the eigenvalues ​​and eigenvectors of the Laplace matrix are subjected to spectral clustering. Spectral clustering projects the original high-dimensional network node data into a low-dimensional space by selecting the eigenvector corresponding to the smallest eigenvalue of the Laplace matrix. The cluster structure can be more easily identified in the low-dimensional space, and then the similarity of each pair of nodes in the low-dimensional space is calculated through the eigenvector. The more similar the nodes are, the more likely they belong to the same cluster. Therefore, these nodes can be classified through k-means or other clustering algorithms. The clustering results reflect the tightly connected node groups in the network.

[0045] The above method steps make the nodes in the cluster have a higher connection density through spectral clustering, while the connections between clusters are relatively few, which is conducive to the modular management of the power system and simplifies the control and optimization of complex networks.

[0046] In some implementations, constructing a Laplace matrix based on the distributed topology includes: The distributed topology is traversed to construct an adjacency matrix, wherein the adjacency matrix is ​​constructed based on nodes of the topology body, and the matrix items represent the weights of the edges between the nodes; the distributed topology is traversed to construct a degree matrix, wherein the degree matrix is ​​a diagonal matrix, and the diagonal matrix items represent the sum of the weights of the edges connecting the nodes; the degree matrix is ​​subtracted from the adjacency matrix to determine the Laplace matrix.

[0047] Specifically, the adjacency matrix is ​​a symmetric matrix that represents the connections between nodes in the topology. If there is an edge between node i and node j, then A i,j =1, otherwise A i,j = 0. For example, for a distributed topology of a power system, the adjacency matrix reflects the electrical or control connection relationship between power devices. Specifically, the degree matrix is ​​a diagonal matrix used to represent the degree of each node (i.e., the sum of the weights connected to it). For example, the diagonal elements of the degree matrix are is equal to the sum of the weights of the edges connecting node i.

[0048] An event database is constructed, wherein the event database covers a coordination event class - information pairs based on a predetermined schedule and event status - reactor parameter control.

[0049] Specifically, the event database includes multiple groups of related coordination event classes, information pairs based on a predetermined schedule and event status, and reactor parameter control. The event database is constructed based on the reactor parameter control history records of the target power grid system or a power grid system similar to the target power grid system. Exemplarily, it includes parsing of historical parameter control records, data cleaning based on data integrity and data quality, data structuring, etc.

[0050] Specifically, the coordination event class includes various operation events related to the reactor, involving key tasks such as operation scheduling, control strategy adjustment, load balancing, etc. of the power system, which is used to achieve efficient control of the reactor. Exemplarily, the coordination event class includes timed scheduling events, event-driven operations, and control adjustment events.

[0051] Specifically, the information pair based on the scheduled schedule and the event status determines when and how the reactor is controlled. In other words, the information pair is the trigger condition of the coordinated event class. By using the information pair based on the scheduled schedule and the event status, it can be ensured that the system triggers the event according to the planned or expected logic and controls the operation of the reactor in the best way.

[0052] The predetermined schedule is a set of time rules set in advance, which defines the operations that the reactor needs to perform at a certain point in time or time period in the future. The schedule can be formulated based on historical data of the power system, load forecasts, or demand in the power market.

[0053] Specifically, when an event is triggered, the reactor is controlled (parameterized) and the reactor parameterized control is the specific control logic and control parameters for participating in the control. The reactor parameterized control includes: adjusting the reactance in power transmission to help balance the system load to reduce voltage fluctuations; controlling the reactive power of the system, optimizing the power factor, and improving the transmission efficiency and stability of the power grid; and adjusting the current flow path through the reactor to reduce system pressure and protect equipment from damage when the system is overloaded or fails.

[0054] By building an event database covering coordinated event classes, precise participation control (parameter control) of reactors can be performed based on information pairs of scheduled schedules and event status, thereby achieving automated dispatching and control of distributed power systems and ensuring stable operation of the system under complex loads and emergencies.

[0055] Supervise and train a cascade decision module based on the distributed topology, the distributed cluster, and the event database.

[0056] Specifically, a cascade decision module is constructed and trained to adjust the inductive and capacitive characteristics of the reactor to ensure efficient operation and control of the reactor in the distributed power system. The cascade decision module is constructed through supervised training by combining information from the distributed topology, distributed clusters, and event database.

[0057] In some embodiments, a cascade decision module is constructed, including: Based on the inductive characteristics of the reactor, a first regulation mode is determined; based on the capacitive characteristics of the reactor, a second regulation mode is determined; based on the first regulation mode, a first cascade decision unit is constructed, and based on the second regulation mode, a second cascade decision unit is constructed, and the first cascade decision unit and the second cascade decision unit are connected in parallel to generate the cascade decision module.

[0058] Specifically, the inductance characteristics of the reactor are mainly used to regulate the current in the system. The basic characteristic of inductance is to hinder the change of current. Especially in alternating current, it can buffer the current and prevent sudden large current shocks. Among them, the inductance characteristics of the reactor are determined by the spiral coil of its conductor. When the current passing through the coil changes, the inductance will generate a reverse current through electromagnetic induction, thereby hindering the rapid change of current.

[0059] Specifically, the first regulation mode is used to adjust the smoothness of current flow and reduce high-frequency fluctuations. This mode controls the current by controlling the value of inductance (such as by adjusting the solenoid structure or material of the reactor). For example, in the case of a sudden increase or decrease in load, the system adjusts the inductance value of the reactor by enabling the first regulation mode to reduce the impact of current fluctuations on the power grid system.

[0060] Specifically, the capacitance characteristics of the reactor are mainly used to regulate voltage. Capacitors can regulate voltage fluctuations by storing charge, especially in power systems, where the capacitance characteristics of the reactor can help balance reactive power and optimize voltage stability.

[0061] Specifically, the second regulation mode is determined based on the capacitance characteristics, and the second regulation mode is used for voltage regulation and reactive power management. For example, according to the voltage change in the power system, the capacitance value of the reactor is dynamically adjusted to ensure voltage stability. For example, when the grid voltage is unstable or fluctuates frequently, the second regulation mode is enabled to adjust the capacitance value of the reactor to stabilize the voltage and optimize the power factor.

[0062] Specifically, two cascade decision units, namely the first cascade decision unit and the second cascade decision unit, are constructed based on the inductance and capacitance characteristics of the reactor. The two decision units form a cascade decision module through parallel processing, thereby realizing control decisions for voltage and current at the same time, and providing a more comprehensive reactor control strategy.

[0063] The collection unit acquires the electrical data of the distributed power system, combines with the cascade decision module, performs cluster greedy decision-making and necessary boundary sharing interaction between clusters, and determines the cascade coordination strategy.

[0064] Specifically, the acquisition unit is responsible for real-time monitoring and acquisition of electrical data such as current, voltage, power and load in the distributed power system. These data will be used in subsequent decision-making and control processes. The acquisition unit is composed of sensors (including current sensors, voltage sensors, temperature sensors, etc.) arranged in the power system.

[0065] Specifically, the collected data is input into the cascade decision module, which performs cluster greedy decision making, performs optimization operations within each cluster, and finds the best reactor control scheme for each distributed cluster in the current state. Through the greedy decision algorithm, the control parameters of the current reactor can be gradually optimized, responding quickly when the system load fluctuates greatly, reducing response delays and ensuring the stability of local clusters.

[0066] Specifically, greedy decision-making only searches for the optimal solution in a local range and may not be able to find the global optimal solution. Therefore, the overall system performance is further optimized through boundary sharing interactions between clusters. Among them, necessary boundary sharing interactions include sharing key data such as power and load between clusters to ensure cross-cluster optimization. Exemplarily, after obtaining the data of the boundary nodes, a comprehensive evaluation of the power flow and load of adjacent clusters will be performed to ensure coordinated operation between clusters. For example, when a cluster is overloaded, part of the load is transferred to the adjacent cluster through the boundary sharing mechanism to avoid local overload.

[0067] Optionally, in the first cascade decision unit, the inductance of the reactor is dynamically adjusted by processing the operation data of the reactor step by step to ensure the accuracy of current control. For example, the basic parameters of the reactor are first adjusted according to the scheduling plan in the event database, and then fine-tuned according to the real-time data.

[0068] Optionally, the second cascade decision unit makes multi-level adjustments to the capacitance of the reactor through hierarchical decision making to ensure that the voltage fluctuation is within a reasonable range. For example, a preliminary adjustment is first made according to the voltage dispatch plan in the event database, and then the reactor is more finely controlled through real-time voltage data.

[0069] The electric data in the distributed power system is acquired through the acquisition unit, and these data are combined with the string decision module to perform cluster greedy decision and boundary sharing interaction between clusters, and finally determine the optimized string coordination strategy to ensure that each cluster in the distributed power system can work together efficiently. The string coordination strategy combines local optimization and global optimization to ensure that the system's load, current and voltage are balanced among different clusters.

[0070] In some embodiments, before determining the cascade coordination strategy, the process includes: Determine the switching position and reactor capacity, and coordinate the cluster strategy. The hardware structure is regulated based on the series-parallel relationship of the reactors and the installation and idleness of the reactors. Predict the transient impact at the switching moment. If the transient impact exceeds the limit, perform a multi-step strategy conversion with the preset impact as a constraint.

[0071] Specifically, the switching position and capacity of the reactor determine its regulation effect and regulation capacity in the power system. Therefore, it is necessary to determine the switching point of the reactor and the required regulation capacity according to the current power demand, load conditions and voltage level in order to achieve stable operation of the power system. Among them, the switching position refers to the specific location where the reactor is installed and works in the power system. Usually, the reactor is installed at a node with high load, prone to current fluctuations or voltage instability. The capacity of the reactor determines the range of current or voltage that it can regulate, and the reactor capacity is dynamically adjusted according to the load conditions and system requirements. For example, if the load is large and fluctuates frequently, the capacity of the reactor can be increased to provide stronger current or voltage regulation capabilities; on the contrary, if the system is in a low load state, the capacity of the reactor can be appropriately reduced to save costs.

[0072] Furthermore, after determining the switching position and capacity of the reactor, the cluster strategy is coordinated and constrained to ensure that the reactor regulation within or between clusters can be coordinated to avoid problems such as system instability or equipment overload. Preferably, the coordination constraint includes the adjustment of the series-parallel relationship of the reactor, and the regulation of the installation and idle state. By adjusting the series-parallel relationship, installation or idle state of the reactor, the hardware structure of the power regulation is optimized to ensure that the capacity and position of the reactor can meet the power system requirements when the load fluctuates.

[0073] Optionally, after optimizing the hardware structure of power regulation, it also includes updating the operating parameter (such as inductance and capacitance value) constraints (coordination constraints) of each reactor according to the optimized hardware structure to ensure that the power system is controlled as expected.

[0074] Specifically, when controlling the reactor, it is necessary to predict the transient impact at the moment of switching. If the impact exceeds the limit, a multi-step conversion strategy is required to avoid equipment damage or grid instability through multiple, small-scale adjustments. Among them, transient impact is used to quantify the pressure caused by the control on the reactor, such as the instantaneous sharp fluctuation of current or voltage.

[0075] For example, the first step size adjustment is performed according to the initial parameters of the reactor, and the current and voltage changes in the power system are monitored in real time. The transient impact intensity after the initial adjustment is analyzed to decide whether to make subsequent adjustments. After the initial adjustment, if the transient impact is still large, the step size is further reduced and a second or multiple adjustments are made. By making small adjustments one by one, power fluctuations can be effectively alleviated to avoid large impacts on equipment and systems.

[0076] Furthermore, the cascade coordination strategy includes an inner-loop strategy and an outer-loop strategy, wherein the inner-loop strategy is a direct parameter control of the reactor, and the outer-loop strategy is a linear regulation relationship based on control parameters, and the outer-loop strategy is used to perform feedback regulation to map the response of the inner-loop strategy.

[0077] Specifically, the cascade coordination strategy is divided into an inner loop strategy and an outer loop strategy to form a hierarchical cascade control structure, so that more precise regulation and response feedback can be achieved when executing reactor regulation.

[0078] Among them, the inner loop strategy is a direct control of the reactor to adjust its key parameters (such as inductance, capacitance, switching position, capacity, etc.). These direct control operations ensure that the power system can respond quickly in a short period of time to adjust load, voltage or current fluctuations.

[0079] Among them, the outer loop strategy monitors the control results of the inner loop strategy, analyzes the response deviation (i.e. the difference between the actual output and the expected output), and performs feedback adjustment based on the linear control relationship. The outer loop strategy is used to ensure that the response deviation can be corrected in time, thereby improving the stability and accuracy of the overall control.

[0080] Based on the cascade coordination strategy, coordinated control of distributed reactors is performed.

[0081] Exemplarily, the inner loop strategy first directly controls the inductance, capacitance or switching operation of the reactor according to the actual load and power fluctuation of the power system to quickly respond to changes in the power system. Then, the outer loop strategy monitors the control results of the inner loop strategy and evaluates the difference between the actual output and the expected output (response deviation). If a deviation occurs, the outer loop strategy calculates the compensation parameters through a linear control relationship and gradually adjusts the input of the inner loop to reduce the deviation. Finally, the inner and outer loop strategies work in a cycle, with the inner loop responding quickly and the outer loop performing long-term optimization to ensure that the power system operates stably in the short term while maintaining accuracy and efficiency in the long term.

[0082] In summary, the distributed reactor coordinated control method provided by the present invention has the following technical effects: By targeting the distributed power system, using power equipment as network nodes, and constructing a distributed topology based on edge connection relationships, each reactor network node has a unique label, and the edge connection relationship includes physical edges based on electrical connections and virtual edges based on control relationships; through the distributed topology, community cluster detection is performed and distributed clusters are divided, each cluster contains at least one reactor network node; an event database is established, which covers coordination event categories, information pairs based on predetermined schedules and event states, and relevant data on reactor participation and control; according to the distributed topology, distributed clusters and event databases, supervised training of the cascade decision module; the acquisition unit is used to obtain the electrical data of the distributed power system, and combined with the cascade decision module, greedy decisions within the cluster and boundary sharing interactions between clusters are performed to determine the cascade coordination strategy; based on the cascade coordination strategy, the distributed reactors are coordinated and controlled. Thus, the technical effect of improving control responsiveness and improving power system efficiency is achieved.

[0083] Embodiment 2 Figure 2 Schematic diagram of the structure of the distributed reactor coordination control device of the present invention. Figure 1 The flow chart of the coordinated control method of the distributed reactor of the present invention can be shown as follows: Figure 2 The structure shown is implemented.

[0084] Based on the same concept as the distributed reactor coordinated control method in the above embodiment, the present invention also provides a distributed reactor coordinated control device comprising: The topological connection component 11 is used to construct a distributed topology based on edge connection relationships for a distributed power system with power equipment as network nodes, wherein each network node based on a reactor has a unique label, and the edge connection includes a physical edge based on an electrical connection and a virtual edge based on a control relationship.

[0085] The cluster division component 12 is used to determine distributed clusters by performing community cluster detection division based on the distributed topology, wherein each distributed cluster includes at least one reactor network node.

[0086] The event organization component 13 is used to construct an event database, which covers the coordination event class - information pairs based on a predetermined schedule and event status - reactor parameter control.

[0087] The decision shaping component 14 is used to supervise the training of the cascade decision module according to the distributed topology, the distributed cluster and the event database.

[0088] The acquisition decision component 15 is used for the acquisition unit to obtain the electrical data of the distributed power system, and in combination with the string-level decision module, performs cluster greedy decision and necessary boundary sharing interaction between clusters to determine the string-level coordination strategy.

[0089] The execution component 16 is sent down to perform coordinated control of the distributed reactor based on the cascade coordination strategy.

[0090] Wherein, the topology connection component 11 includes: The distributed node determination unit is used to traverse the network nodes, determine the distributed nodes based on the reactor as the topology subject, and perform unique label identification on each node.

[0091] The topology subject identification unit is used to identify the topology subject and contextualize the non-distributed subject topology part.

[0092] The edge weight marking unit is used to traverse the distributed topology and mark the edge weights based on the control relevance of the connections.

[0093] In some embodiments, the cluster partitioning component 12 includes: The Laplace matrix construction unit is used to construct a Laplace matrix based on the distributed topology.

[0094] The eigenvalue and eigenvector calculation unit is used to traverse the Laplace matrix to calculate the eigenvalue and eigenvector, wherein the eigenvalue is calculated according to the eigenvalue equation |λE-L|=0, and the eigenvalue vector is calculated by solving the equation group (L-λE)v=0, wherein λ is the eigenvalue, E is the unit matrix, L is the Laplace matrix, and v is the eigenvalue vector.

[0095] A spectral clustering processing unit is used to perform spectral clustering processing based on the eigenvalue and the eigenvector to determine the distributed cluster.

[0096] In some implementations, the Laplacian matrix construction unit in the cluster partitioning component 12 is further configured to: The distributed topology is traversed to construct an adjacency matrix, wherein the adjacency matrix is ​​constructed based on nodes of the topology subject, and matrix items represent weights of edges between nodes.

[0097] The distributed topology is traversed to construct a degree matrix, wherein the degree matrix is ​​a diagonal matrix, and the diagonal matrix items represent the weight sums of the edges connecting the nodes.

[0098] The degree matrix is ​​subtracted from the adjacency matrix to determine a Laplacian matrix.

[0099] In some embodiments, the decision making component 14 includes: The inductance characteristic adjustment mode determining unit is used to determine a first adjustment mode based on the inductance characteristic of the reactor.

[0100] The capacitance characteristic adjustment mode determining unit is used to determine the second adjustment mode based on the capacitance characteristic of the reactor.

[0101] The first cascade decision unit construction unit is used to construct a first cascade decision unit based on the first adjustment mode.

[0102] The second cascade decision unit construction unit is used to construct a second cascade decision unit based on the second adjustment mode.

[0103] The cascade decision module generating unit is used to connect the first cascade decision unit and the second cascade decision unit in parallel to generate the cascade decision module.

[0104] In some implementations, the acquisition decision component 15 further includes: The coordination constraint unit is used to determine the switching position and the reactor capacity, and coordinate the cluster strategy. Among them, the hardware structure is controlled based on the series and parallel relationship of the reactors, and the installation and idleness of the reactors.

[0105] The impact constraint unit is used to predict the transient impact at the switching moment. If the transient impact exceeds the limit, a multi-step strategy conversion is performed, wherein the preset impact is used as a constraint.

[0106] Furthermore, the cascade coordination strategy includes an inner-loop strategy and an outer-loop strategy, wherein the inner-loop strategy is a direct parameter control of the reactor, and the outer-loop strategy is a linear regulation relationship based on control parameters, and the outer-loop strategy is used to perform feedback regulation to map the response of the inner-loop strategy.

[0107] It should be understood that the embodiments mentioned in this specification focus on their differences from other embodiments. The specific embodiments in the aforementioned embodiment one are also applicable to the distributed inductor coordination control device described in embodiment two. For the sake of brevity of the specification, they will not be further elaborated here.

[0108] It should be understood that the embodiments disclosed in the present invention and the above description can enable those skilled in the art to use the present invention to implement the present invention. At the same time, the present invention is not limited to the above-mentioned embodiments. It should be understood that those skilled in the art can still modify the technical solutions recorded in the above-mentioned embodiments, or replace some of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A coordinated control method for distributed reactors, characterized in that: The method comprises: For distributed power systems, power equipment is used as network nodes, and a distributed topology is constructed based on edge connection relationships. Each network node based on the reactor has a unique label, and the edge connection includes physical edges based on electrical connections and virtual edges based on control relationships. Based on the distributed topology, a distributed cluster is determined by performing community cluster detection and division, wherein each distributed cluster includes at least one reactor network node; Constructing an event database, the event database covers coordination event classes - information pairs based on a predetermined schedule and event status - reactor parameter control; Constructing a cascade decision module, and supervising training the cascade decision module according to the distributed topology, the distributed cluster and the event database; The collection unit acquires the electrical data of the distributed power system, combines with the cascade decision module, performs cluster greedy decision and necessary boundary sharing interaction between clusters, and determines the cascade coordination strategy; Based on the cascade coordination strategy, coordinated control of distributed reactors is performed.

2. The coordinated control method of distributed reactors according to claim 1, characterized in that: The constructing of a distributed topology includes: Traversing the network nodes, determining distributed nodes based on reactors as topological entities, and uniquely labeling each node; identifying the topological subject and contextualizing the non-distributed subject topological portion; The distributed topology is traversed, and edge weights are marked based on the control relevance of the connections.

3. The coordinated control method of distributed reactors according to claim 1, characterized in that: The community cluster detection and division includes: Based on the distributed topology, construct a Laplace matrix; Traversing the Laplace matrix, calculating eigenvalues ​​and eigenvectors, wherein the eigenvalues ​​are calculated according to the eigenvalue equation |λE-L|=0, and the eigenvalue vectors are calculated by solving the equation system (L-λE)v=0, wherein λ is the eigenvalue, E is the identity matrix, L is the Laplace matrix, and v is the eigenvalue vector; A spectral clustering process is performed based on the eigenvalues ​​and the eigenvectors to determine the distributed clusters.

4. The coordinated control method for distributed reactors according to claim 3, characterized in that: Based on the distributed topology, a Laplace matrix is ​​constructed, including: Traversing the distributed topology to construct an adjacency matrix, wherein the adjacency matrix is ​​constructed based on nodes of the topology subject, and matrix items represent weights of edges between nodes; Traversing the distributed topology to construct a degree matrix, wherein the degree matrix is ​​a diagonal matrix, and the diagonal matrix items represent the weights and edges connecting the nodes; The degree matrix is ​​subtracted from the adjacency matrix to determine a Laplacian matrix.

5. The distributed reactor coordinated control method according to claim 1, characterized in that: Construct a cascade decision-making module, including: Determining a first regulation mode based on the inductance characteristic of the reactor; Determining a second regulation mode based on the capacitance characteristics of the reactor; Based on the first regulating mode, a first cascade decision unit is constructed, based on the second regulating mode, a second cascade decision unit is constructed, and the first cascade decision unit and the second cascade decision unit are connected in parallel to generate the cascade decision module.

6. The coordinated control method for distributed reactors according to claim 1, characterized in that: Before determining the cascade coordination strategy, include: Determine the switching position and reactor capacity, and coordinate the cluster strategy. In particular, the hardware structure is regulated based on the series and parallel relationship of the reactors, and the installation and idleness of the reactors. Predict the transient impact at the switching moment. If the transient impact exceeds the limit, perform a multi-step strategy conversion, in which the preset impact is used as a constraint.

7. The coordinated control method for distributed reactors according to claim 1, characterized in that: The cascade coordination strategy includes an inner loop strategy and an outer loop strategy, wherein the inner loop strategy is a direct parameter control of the reactor, and the outer loop strategy is a linear regulation relationship based on control parameters, and the outer loop strategy is used to perform feedback regulation to map the response of the inner loop strategy.

8. A distributed reactor coordination control device, characterized in that: The device comprises: A topology connection component, wherein the topology connection component is used for constructing a distributed topology based on an edge connection relationship for a distributed power system, with power equipment as network nodes, wherein each network node based on a reactor has a unique label, and the edge connection includes a physical edge based on an electrical connection and a virtual edge based on a control relationship; A cluster partitioning component, the cluster partitioning component is used to determine distributed clusters by performing community cluster detection partitioning based on the distributed topology, wherein each distributed cluster includes at least one reactor network node; An event organization component, the event organization component is used to build an event database, the event database covers the coordination event class - information pairs based on a predetermined schedule and event status - reactor parameter control; A decision forming component, the decision forming component is used to construct a cascade decision module, and supervise the training of the cascade decision module according to the distributed topology, the distributed cluster and the event database; A collection decision component, wherein the collection decision component is used for a collection unit to obtain electrical data of the distributed power system, and in combination with the cascade decision module, performs cluster greedy decision making and necessary boundary sharing interaction between clusters to determine a cascade coordination strategy; A sending execution component is used to perform coordinated control of distributed inductors based on the cascade coordination strategy.

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