Distributed cooperative voltage regulation method and device for power distribution network, computer equipment and storage medium

Abstract

The invention relates to a distributed cooperative voltage regulation method and device for a power distribution network, computer equipment and a storage medium, and relates to the technical field of smart power grids, and the method comprises the steps: determining a to-be-regulated node from all nodes according to the monitoring data of each node in the power distribution network, and determining the to-be-regulated node according to the monitoring data, a preset sensitivity nonlinear model and a target voltage value; obtaining a target reactive power utilization rate; and controlling the node to be regulated and controlled to broadcast the target reactive power utilization rate to the adjacent nodes, and performing iterative calculation on the target reactive power utilization rate of each adjacent node by adopting a consistency algorithm model according to the target reactive power utilization rate, the sensitivity nonlinear model and the monitoring data of the adjacent nodes until a preset iteration ending condition is met, and obtaining a target reactive power utilization rate of each node until all nodes of the power distribution network are traversed; and according to the target reactive power utilization rate of each node, generating a reactive power output instruction for each node so as to instruct each node to execute voltage regulation operation. By adopting the method, the adaptability can be improved.

Description

Technical Field

[0001] The present application relates to the field of smart grid technology, and in particular to a method, device, computer equipment and storage medium for distributed coordinated voltage regulation in a distribution network. Background Art

[0002] With the rapid development of distributed photovoltaic power generation, more and more photovoltaic power generation systems are connected to the distribution network. Photovoltaic power generation with high penetration rate generates a large amount of active power during the peak hours of the day. Due to the volatility and intermittent characteristics of photovoltaic power generation, it may cause problems such as voltage fluctuations and difficulty in regulating reactive power in the distribution network. In particular, the reactive power fluctuations output by photovoltaic power generation have a significant impact on the stability of the distribution network voltage, which in turn affects the safe operation of the power system. In related technologies, through distributed collaborative regulation strategies, voltage regulation and reactive power optimization can be achieved through local equipment collaboration without relying on centralized control systems. This is particularly suitable for distribution networks with high penetration photovoltaic power generation.

[0003] However, the distributed voltage regulation method in the related art is difficult to ensure voltage stability and safe operation of the system under the condition of rapidly changing voltage fluctuations, and has the problem of weak adaptability. Summary of the invention

[0004] Based on this, it is necessary to provide a distributed coordinated voltage regulation method, device, computer equipment and computer-readable storage medium for distribution networks that can improve adaptability in response to the above-mentioned technical problems.

[0005] In a first aspect, the present application provides a distributed coordinated voltage regulation method for a distribution network, comprising:

[0006] According to the monitoring data of each node in the distribution network, a node to be regulated is determined from all the nodes; the node is a node equipped with a miniature circuit breaker and a photovoltaic inverter device;

[0007] According to the monitoring data of the node to be regulated, the preset sensitivity nonlinear model and the target voltage value, the target reactive power utilization rate of the node to be regulated is obtained; the sensitivity nonlinear model is obtained by training a preset neural network with historical monitoring data;

[0008] Control the node to be regulated to broadcast the target reactive power utilization rate to adjacent nodes, and iteratively calculate the target reactive power utilization rate of each adjacent node using a consistency algorithm model according to the target reactive power utilization rate, the sensitivity nonlinear model and the monitoring data of the adjacent nodes, until a preset iteration end condition is met, thereby obtaining the target reactive power utilization rate of each adjacent node;

[0009] Control each of the adjacent nodes to broadcast its target reactive power utilization rate to new adjacent nodes in a direction away from the node to be regulated, and iteratively calculate the target reactive power utilization rate of each of the new adjacent nodes using a consistency algorithm model according to the target reactive power utilization rate, the sensitivity nonlinear model and the monitoring data of the new adjacent nodes to obtain the target reactive power utilization rate of each of the new adjacent nodes;

[0010] The new adjacent node is used as the current adjacent node, and the step of controlling each adjacent node to broadcast its target reactive power utilization rate to the new adjacent node in a direction away from the node to be regulated is returned until all the nodes of the distribution network are traversed to obtain the target reactive power utilization rate of each node;

[0011] According to the target reactive power utilization rate of each node, a reactive power output instruction for each node is generated to instruct each node to perform a voltage regulation operation.

[0012] In one embodiment, the monitoring data includes a voltage value, and determining the node to be regulated from all the nodes according to the monitoring data of each node in the distribution network includes:

[0013] Determine a comparison result between a voltage value of each of the nodes in the distribution network and a preset voltage threshold range; and according to the comparison result, determine the node whose voltage value exceeds the voltage threshold range as a node to be regulated.

[0014] In one embodiment, the preset iteration end condition includes any one of the following: the voltage values ​​of all the nodes are within the voltage threshold range, the difference between the target reactive power utilization rates of two adjacent updates is lower than a preset threshold, and the number of iterations reaches a preset iteration number threshold.

[0015] In one embodiment, obtaining the target reactive power utilization rate of the node to be regulated according to the monitoring data of the node to be regulated, a preset sensitivity nonlinear model and a target voltage value includes:

[0016] According to the voltage value of the node to be regulated and the target voltage value, the voltage change of the node to be regulated is obtained; the voltage change of the node to be regulated and other monitoring data except the voltage value are input into the inverse function of the sensitivity nonlinear model to obtain the target reactive power utilization rate of the node to be regulated.

[0017] In one embodiment, the target reactive power utilization rate of each adjacent node is iteratively calculated using a consistency algorithm model according to the target reactive power utilization rate, the sensitivity nonlinear model and the monitoring data of the adjacent nodes until a preset iteration end condition is met to obtain the target reactive power utilization rate of each adjacent node, including:

[0018] Obtain the voltage change of each adjacent node; input the voltage change of the monitoring data of each adjacent node and other monitoring data except the voltage value into the sensitivity nonlinear model to obtain the reactive voltage sensitivity of each adjacent node; input the reactive voltage sensitivity of the adjacent node and the target reactive utilization rate into the consistency algorithm model until the preset iteration end condition is met to obtain the target reactive utilization rate of each adjacent node.

[0019] In one embodiment, the reactive voltage sensitivity of the adjacent nodes and the target reactive utilization rate are input into the consistency algorithm model until a preset iteration end condition is met to obtain the target reactive utilization rate of each adjacent node, including:

[0020] For each of the adjacent nodes, the reactive voltage sensitivity of the adjacent node and the target reactive utilization rate are respectively input into the model of the consistency algorithm to obtain a candidate reactive utilization rate; when the candidate reactive utilization rate satisfies the preset inverter constraint, the candidate reactive utilization rate is used as the target reactive utilization rate after this round of iterative update; when the candidate reactive utilization rate does not satisfy the preset inverter constraint, the candidate reactive utilization rate is projected according to the inverter constraint to obtain the target reactive utilization rate after this round of iterative update; return to the step of inputting the reactive voltage sensitivity of the adjacent node and the target reactive utilization rate into the consistency algorithm model until the preset iteration end condition is met to obtain the target reactive utilization rate of each of the adjacent nodes.

[0021] In one embodiment, the expression corresponding to the consistency algorithm model includes:

[0022]

[0023] Where k represents the number of iterations, represents the target reactive power utilization, r and Respectively represent the learning rate and training parameters of the sensitivity nonlinear model, Q i represents the reactive power of the ith node, is the Chebyshev filter acceleration operator, W represents the state transfer matrix composed of the state transfer weights of each communication link in the distribution network, represents the reactive voltage sensitivity of the node i, represents the sensitivity weighting matrix, represents the step length, represents the set of adjacent nodes of node i, and j represents the jth adjacent node.

[0024] In a second aspect, the present application also provides a distributed coordinated voltage regulation device for a distribution network, comprising:

[0025] A node determination module, used to determine the node to be regulated from all the nodes according to the monitoring data of each node in the distribution network; the node is a node equipped with a miniature circuit breaker and a photovoltaic inverter device;

[0026] A reactive power determination module is used to obtain a target reactive power utilization rate of the node to be regulated according to the monitoring data of the node to be regulated, a preset sensitivity nonlinear model and a target voltage value; the sensitivity nonlinear model is obtained by training a preset neural network with historical monitoring data;

[0027] An iterative update module is used to control the node to be regulated to broadcast the target reactive power utilization rate to adjacent nodes, and iteratively calculate the target reactive power utilization rate of each adjacent node using a consistency algorithm model according to the target reactive power utilization rate, the sensitivity nonlinear model and the monitoring data of the adjacent nodes, until a preset iteration end condition is met, thereby obtaining the target reactive power utilization rate of each adjacent node;

[0028] The iterative update module is also used to control each of the adjacent nodes to broadcast its target reactive power utilization rate to new adjacent nodes in a direction away from the node to be regulated, and to iteratively calculate the target reactive power utilization rate of each of the new adjacent nodes using a consistency algorithm model according to the target reactive power utilization rate, the sensitivity nonlinear model and the monitoring data of the new adjacent nodes to obtain the target reactive power utilization rate of each of the new adjacent nodes;

[0029] The iterative update module is further used to take the new adjacent node as the current adjacent node, and return to the step of controlling each adjacent node to broadcast its target reactive power utilization rate to the new adjacent node in a direction away from the node to be regulated, until all the nodes of the distribution network are traversed to obtain the target reactive power utilization rate of each node;

[0030] The instruction output module is used to generate a reactive power output instruction for each of the nodes according to the target reactive power utilization rate of each of the nodes, so as to instruct each of the nodes to perform a voltage regulation operation.

[0031] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0032] According to the monitoring data of each node in the distribution network, a node to be regulated is determined from all the nodes; the node is a node equipped with a miniature circuit breaker and a photovoltaic inverter device;

[0033] According to the monitoring data of the node to be regulated, the preset sensitivity nonlinear model and the target voltage value, the target reactive power utilization rate of the node to be regulated is obtained; the sensitivity nonlinear model is obtained by training a preset neural network with historical monitoring data;

[0034] Control the node to be regulated to broadcast the target reactive power utilization rate to adjacent nodes, and according to the target reactive power utilization rate, the sensitivity nonlinear model and the monitoring data of the adjacent nodes, use a consistency algorithm model to iteratively calculate the target reactive power utilization rate of each adjacent node until a preset iteration end condition is met, and obtain the target reactive power utilization rate of each adjacent node; control each adjacent node to broadcast its target reactive power utilization rate to new adjacent nodes in a direction away from the node to be regulated, and according to the target reactive power utilization rate, the sensitivity nonlinear model and the monitoring data of the new adjacent nodes, use a consistency algorithm model to iteratively calculate the target reactive power utilization rate of each new adjacent node, and obtain the target reactive power utilization rate of each new adjacent node;

[0035] The new adjacent node is used as the current adjacent node, and the step of controlling each adjacent node to broadcast its target reactive power utilization rate to the new adjacent node in a direction away from the node to be regulated is returned until all the nodes of the distribution network are traversed to obtain the target reactive power utilization rate of each node;

[0036] According to the target reactive power utilization rate of each node, a reactive power output instruction for each node is generated to instruct each node to perform a voltage regulation operation.

[0037] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0038] According to the monitoring data of each node in the distribution network, a node to be regulated is determined from all the nodes; the node is a node equipped with a miniature circuit breaker and a photovoltaic inverter device;

[0039] According to the monitoring data of the node to be regulated, the preset sensitivity nonlinear model and the target voltage value, the target reactive power utilization rate of the node to be regulated is obtained; the sensitivity nonlinear model is obtained by training a preset neural network with historical monitoring data;

[0040] Control the node to be regulated to broadcast the target reactive power utilization rate to adjacent nodes, and iteratively calculate the target reactive power utilization rate of each adjacent node using a consistency algorithm model according to the target reactive power utilization rate, the sensitivity nonlinear model and the monitoring data of the adjacent nodes, until a preset iteration end condition is met, thereby obtaining the target reactive power utilization rate of each adjacent node;

[0041] Control each of the adjacent nodes to broadcast its target reactive power utilization rate to new adjacent nodes in a direction away from the node to be regulated, and iteratively calculate the target reactive power utilization rate of each of the new adjacent nodes using a consistency algorithm model according to the target reactive power utilization rate, the sensitivity nonlinear model and the monitoring data of the new adjacent nodes to obtain the target reactive power utilization rate of each of the new adjacent nodes;

[0042] The new adjacent node is used as the current adjacent node, and the step of controlling each adjacent node to broadcast its target reactive power utilization rate to the new adjacent node in a direction away from the node to be regulated is returned until all the nodes of the distribution network are traversed to obtain the target reactive power utilization rate of each node;

[0043] According to the target reactive power utilization rate of each node, a reactive power output instruction for each node is generated to instruct each node to perform a voltage regulation operation.

[0044] The above-mentioned distributed collaborative voltage regulation method, device, computer equipment and computer-readable storage medium of the distribution network, the method determines the node to be regulated from all nodes according to the monitoring data of each node in the distribution network, wherein the node is a node equipped with a miniature circuit breaker and a photovoltaic inverter device, so as to grasp the operating status of each node in real time and obtain data support for subsequent voltage regulation decisions. Further, according to the monitoring data of the node to be regulated, the preset sensitivity nonlinear model and the target voltage value, the target reactive power utilization rate of the node to be regulated is obtained; the sensitivity nonlinear model is obtained by training the preset neural network with historical monitoring data; and the node to be regulated is controlled to broadcast the target reactive power utilization rate to adjacent nodes, and according to the target reactive power utilization rate, the sensitivity nonlinear model and the monitoring data of the adjacent nodes, the target reactive power utilization rate of each adjacent node is iteratively calculated using a consistency algorithm model until the preset iteration end condition is met to obtain the target reactive power utilization rate of each adjacent node; in addition, each adjacent node is controlled to broadcast its target reactive power utilization rate to a new adjacent node in a direction away from the node to be regulated. Node, according to the target reactive power utilization, sensitivity nonlinear model and monitoring data of new adjacent nodes, the target reactive power utilization of each new adjacent node is iteratively calculated using the consistency algorithm model to obtain the target reactive power utilization of each new adjacent node; the new adjacent node is used as the current adjacent node, and the step of controlling each adjacent node to broadcast its target reactive power utilization to the new adjacent node in the direction away from the node to be regulated is returned until all nodes of the distribution network are traversed to obtain the target reactive power utilization of each node, and the information exchange between each node is realized. According to the target reactive power utilization of each node, a reactive output instruction for each node is generated to instruct each node to perform voltage regulation. The sensitivity nonlinear model can dynamically track the nonlinear relationship between node voltage and reactive power, providing a more accurate decision-making basis for regulation, and the use of the consistency algorithm model can efficiently realize the coordinated regulation between nodes. Through information exchange and sensitivity weighting between nodes, it ensures that the system can achieve coordinated voltage regulation of the entire network or local area when facing load fluctuations and photovoltaic power generation fluctuations, thereby improving the overall adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0046] Figure 1 This is an application environment diagram of a distributed coordinated voltage regulation method for a distribution network in an embodiment;

[0047] Figure 2 It is a schematic diagram of a flow chart of a distributed coordinated voltage regulation method for a distribution network in one embodiment;

[0048] Figure 3 A schematic flow chart of the iterative calculation steps of the target reactive power utilization rate in one embodiment;

[0049] Figure 4 It is a flowchart of a distributed autonomous coordinated voltage regulation method for a distribution network based on an intelligent miniature circuit breaker in one embodiment;

[0050] Figure 5 It is a structural block diagram of a distributed coordinated voltage regulation device for a distribution network in one embodiment;

[0051] Figure 6 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0053] As described in the background technology, the distributed coordinated voltage regulation method of the distribution network of the related technology has the problem of low adaptability. The inventor has found that the reason for this problem is that with the rapid development of distributed photovoltaic power generation, more and more photovoltaic power generation systems are connected to the distribution network, especially in the low-voltage distribution network. The high penetration rate of photovoltaics makes the power system face unprecedented challenges. Photovoltaic power generation with high penetration rate generates a large amount of active power during the peak period of the day, and due to the volatility and intermittent characteristics of photovoltaic power generation, it may cause problems such as voltage fluctuation and reactive power regulation difficulty in the distribution network. In particular, the reactive power fluctuation output by photovoltaic power generation has a significant impact on the stability of the distribution network voltage, and thus affects the safe operation of the power system. In order to effectively cope with these challenges, the traditional distribution network voltage regulation method has been difficult to meet the needs of high-penetration photovoltaic power generation. Most of the existing voltage regulation methods rely on traditional centralized control or static regulation methods, which often cannot respond to the rapidly changing loads and photovoltaic power generation power in the distribution network in real time. In addition, the traditional method usually needs to rely on a detailed power flow analysis model, which is complex to calculate and difficult to cope with the rapidly changing power grid state, resulting in hysteresis and inaccuracy in the regulation process. In order to solve the above problems, in recent years, distributed control methods have gradually become a new research direction. This method uses a distributed collaborative regulation strategy to achieve voltage regulation and reactive power optimization through local equipment collaboration without relying on a centralized control system. It is particularly suitable for distribution networks with high penetration of photovoltaic power generation. However, there are still some technical difficulties in the existing distributed voltage regulation methods, which are mainly manifested in the following aspects: insufficient voltage regulation accuracy. Under the condition of high penetration of photovoltaic power generation, some existing distributed control methods have poor voltage regulation accuracy and stability, especially under the condition of rapidly changing voltage fluctuations, it is difficult to ensure voltage stability and safe operation of the system; reactive power regulation is difficult. Due to the volatility and intermittency of the reactive power output of distributed photovoltaic systems, it is difficult to regulate reactive power in the distribution network. The existing regulation methods have a lag in responding to reactive power fluctuations and are difficult to adapt to system changes in real time; the level of intelligence is low. Although some existing regulation methods have attempted to introduce distributed collaborative algorithms, most methods rely on fixed models or static control strategies, lack intelligent regulation capabilities for different operating conditions and equipment states, and are difficult to adapt to dynamically changing power grid environments; the system is highly complex and difficult to deploy. Existing control methods usually require complex hardware facilities and algorithm support, and face high costs and technical difficulties in actual deployment, especially during on-site installation and commissioning, which requires secondary debugging and configuration, increasing the system's operation and maintenance costs.

[0054] Based on the above reasons, the present application provides a distributed coordinated voltage regulation method for a distribution network, aiming to improve the adaptability to fluctuations of the distribution network system.

[0055] The distributed coordinated voltage regulation method for distribution network provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, the application environment includes: a control system 102, a node 104 to be regulated in the distribution network, and an adjacent node 106 in the distribution network. The node 104 to be regulated and the adjacent node 106 both include a miniature circuit breaker and a photovoltaic inverter. The control system 102 communicates with the miniature circuit breakers in each node by wireless communication. The control system 102 can be integrated on a server, or placed on a cloud or other network server. The control system 102 determines the node 104 to be regulated from all nodes according to the monitoring data of each node in the distribution network; the node is a node equipped with a miniature circuit breaker and a photovoltaic inverter device; the control center 102 obtains the target reactive power utilization rate of the node 104 to be regulated according to the monitoring data of the node 104 to be regulated, a preset sensitivity nonlinear model and a target voltage value; the control system 102 controls the node 104 to be regulated to broadcast the target reactive power utilization rate to the adjacent nodes 106, and according to the target reactive power utilization rate, the sensitivity nonlinear model and the monitoring data of the adjacent nodes 106, uses the consistency algorithm model to iteratively calculate the target reactive power utilization rate of each adjacent node 106 until the preset iteration end condition is met, and obtains the target reactive power utilization rate of each adjacent node 106; the sensitivity nonlinear model is obtained by training the preset neural network with historical monitoring data; the control system 102 controls each adjacent node 106 to broadcast its target reactive power utilization to new adjacent nodes 106 in a direction away from the node to be regulated. According to the target reactive power utilization, the sensitivity nonlinear model and the monitoring data of the new adjacent nodes 106, the target reactive power utilization of each new adjacent node 106 is iteratively calculated using a consistency algorithm model to obtain the target reactive power utilization of each new adjacent node; the control system 102 uses the new adjacent node as the current adjacent node 106, and returns to the step of controlling each adjacent node 106 to broadcast its target reactive power utilization to new adjacent nodes 106 in a direction away from the node to be regulated 104, until all nodes of the distribution network are traversed to obtain the target reactive power utilization of each node; the control center 102 generates a reactive power output instruction for each node according to the target reactive power utilization of each node to instruct each node to perform a voltage regulation operation.

[0056] In an exemplary embodiment, Figure 2 As shown, a distributed coordinated voltage regulation method for a distribution network is provided. Figure 1 The control center 102 in FIG. 1 is taken as an example to illustrate the method, which includes the following steps S202 to S212. Among them:

[0057] Step S202: determining the nodes to be regulated from all the nodes according to the monitoring data of each node in the distribution network.

[0058] Among them, the distribution network is the part of the power system that transmits electrical energy from the transmission network or power plant to end users (such as households, commercial users, and industrial users). It is mainly responsible for reducing high-voltage electricity to low-voltage electricity so that civil and industrial users can use it safely. The distribution network usually includes substations, distribution lines, distribution transformers, and power supply equipment.

[0059] Among them, the node is a node equipped with a miniature circuit breaker and a photovoltaic inverter device, which is usually the access point of a distributed photovoltaic power generation system. Among them, the miniature circuit breaker is used for overload and short-circuit protection, isolating the photovoltaic system from the power grid (such as the inverter output or the user's distribution box); among them, the photovoltaic inverter converts the direct current of the photovoltaic array into alternating current and synchronizes with the power grid.

[0060] Among them, the monitoring data include voltage value, current value, active power, reactive power and power factor.

[0061] Optionally, the control system periodically collects monitoring data of each node in the distribution network, and determines whether there is a node with voltage exceeding the limit based on the monitoring data of each node, and determines the node with voltage exceeding the limit as the node to be regulated. It is understandable that after determining the node to be regulated, an alarm message can be automatically generated to feedback to the technician that there is an abnormality in the node to be regulated.

[0062] Step S204, obtaining the target reactive power utilization rate of the node to be regulated according to the monitoring data of the node to be regulated, the preset sensitivity nonlinear model and the target voltage value.

[0063] Among them, the sensitivity nonlinear model is obtained by training the preset neural network with historical monitoring data. The sensitivity nonlinear model represents the nonlinear relationship between the voltage and reactive power of the node. The output of this model is reactive voltage sensitivity, that is, the response degree of voltage change to reactive power regulation.

[0064] The target voltage value may be a pre-set safe voltage value at which each node can operate normally.

[0065] The reactive power utilization rate may be the reactive power output, which refers to the ratio of the reactive power output by the photovoltaic inverter to the reactive capacity it provides.

[0066] Optionally, the control system inputs the monitoring data and target voltage value of the node to be regulated into a preset sensitivity nonlinear model to obtain the reactive voltage sensitivity of the output node to be regulated, and further obtains the target reactive power utilization rate of the node to be regulated based on the reactive voltage sensitivity.

[0067] Step S206, control the node to be regulated to broadcast the target reactive power utilization to the adjacent nodes, and use the consistency algorithm model to iteratively calculate the target reactive power utilization of each adjacent node according to the target reactive power utilization, the sensitivity nonlinear model and the monitoring data of the adjacent nodes, until the preset iteration end condition is met, and the target reactive power utilization of each adjacent node is obtained.

[0068] Among them, in the topological structure of the distribution network, the adjacent node may be a node that is directly connected to the node to be regulated.

[0069] The consistency algorithm model may be an algorithm that coordinates the status of each node through information exchange between nodes to ensure that a consistent goal is achieved among multiple nodes.

[0070] Optionally, the control system controls the node to be regulated to broadcast the calculated target reactive power utilization along the topological connection to the adjacent nodes directly connected to the node to be regulated, and uses the consistency algorithm model to iteratively calculate the target reactive power utilization of each adjacent node according to the target reactive power utilization, the sensitivity nonlinear model and the monitoring data of the adjacent nodes until the preset iteration end conditions are met, so as to obtain the target reactive power utilization of each adjacent node adjacent to the node to be regulated.

[0071] Step S208, control each adjacent node to broadcast its target reactive power utilization to new adjacent nodes in a direction away from the node to be regulated, and use a consistency algorithm model to iteratively calculate the target reactive power utilization of each new adjacent node based on the target reactive power utilization, the sensitivity nonlinear model and the monitoring data of the new adjacent nodes to obtain the target reactive power utilization of each new adjacent node.

[0072] It is understandable that in the topological structure of the distribution network, there are different connection relationships between various nodes. The node to be regulated has directly connected adjacent nodes, and the adjacent node also has other adjacent nodes directly connected to it.

[0073] Optionally, the control system controls the adjacent nodes to broadcast their target reactive power utilization to the new adjacent nodes in a direction away from the node to be regulated. It should be noted that when the adjacent nodes are broadcasting to the new adjacent nodes, the nodes that have iteratively calculated their own target reactive power utilization do not need to broadcast. The control system uses a consistency algorithm model to iteratively calculate the target reactive power utilization of each new adjacent node based on the target reactive power sensitivity nonlinear model and the monitoring data of the new adjacent nodes to obtain the target reactive power utilization of each new adjacent node.

[0074] Step S210, taking the new adjacent node as the current adjacent node, and returning to the step of controlling each adjacent node to broadcast its target reactive power utilization to the new adjacent node in a direction away from the node to be regulated, until all nodes in the distribution network are traversed to obtain the target reactive power utilization of each node.

[0075] Among them, traversal can refer to systematically accessing or checking all elements (or nodes) in a structure according to a certain rule or order to ensure that each element is processed and not repeated. In this embodiment, each node in the distribution network is traversed according to the rule of broadcasting the target reactive power utilization rate and the rule of iteratively calculating the target reactive power utilization rate of the node.

[0076] Optionally, the control system takes the new adjacent node as the current adjacent node, and returns to the step of controlling each adjacent node to broadcast its target reactive power utilization to the new adjacent node in a direction away from the node to be regulated, until all nodes of the distribution network are traversed according to the rules for broadcasting the target reactive power utilization and the rules for iteratively calculating the target reactive power utilization of the node, and the target reactive power utilization of each node is obtained.

[0077] Step S212: generating a reactive power output instruction for each node according to the target reactive power utilization rate of each node, so as to instruct each node to perform a voltage regulation operation.

[0078] Among them, the reactive power output instruction is used to instruct the photovoltaic inverter of each node to adjust the reactive power output according to the target reactive power utilization rate, thereby adjusting the voltage of each node.

[0079] Optionally, the control system generates a reactive power output command for each node according to the target reactive power utilization rate of each node, and sends it to the reactive devices such as photovoltaic inverters of each node, thereby completing the voltage regulation operation of each node by adjusting the reactive power output of reactive devices such as photovoltaic inverters.

[0080] In the above-mentioned distributed collaborative voltage regulation method for distribution network, the method determines the node to be regulated from all nodes according to the monitoring data of each node in the distribution network, wherein the node is a node equipped with a miniature circuit breaker and a photovoltaic inverter device, so as to grasp the operating status of each node in real time and obtain data support for subsequent voltage regulation decisions. Further, based on the monitoring data of the node to be regulated, the preset sensitivity nonlinear model and the target voltage value, the target reactive power utilization rate of the node to be regulated is obtained; the sensitivity nonlinear model is obtained by training the preset neural network with historical monitoring data; and the node to be regulated is controlled to broadcast the target reactive power utilization rate to adjacent nodes, and according to the target reactive power utilization rate, the sensitivity nonlinear model and the monitoring data of the adjacent nodes, the target reactive power utilization rate of each adjacent node is iteratively calculated using a consistency algorithm model until the preset iteration end condition is met to obtain the target reactive power utilization rate of each adjacent node; in addition, each adjacent node is controlled to broadcast its target reactive power utilization rate to a new adjacent node in a direction away from the node to be regulated. Node, according to the target reactive power utilization, sensitivity nonlinear model and monitoring data of new adjacent nodes, the target reactive power utilization of each new adjacent node is iteratively calculated using the consistency algorithm model to obtain the target reactive power utilization of each new adjacent node; the new adjacent node is used as the current adjacent node, and the step of controlling each adjacent node to broadcast its target reactive power utilization to the new adjacent node in the direction away from the node to be regulated is returned until all nodes of the distribution network are traversed to obtain the target reactive power utilization of each node, and the information exchange between each node is realized. According to the target reactive power utilization of each node, a reactive output instruction for each node is generated to instruct each node to perform voltage regulation. The sensitivity nonlinear model can dynamically track the nonlinear relationship between node voltage and reactive power, providing a more accurate decision-making basis for regulation, and the use of the consistency algorithm model can efficiently realize the coordinated regulation between nodes. Through information exchange and sensitivity weighting between nodes, it ensures that the system can achieve coordinated voltage regulation of the entire network or local area when facing load fluctuations and photovoltaic power generation fluctuations, thereby improving the overall adaptability.

[0081] In an exemplary embodiment, the monitoring data includes a voltage value, and step S202 determines the node to be regulated from all nodes according to the monitoring data of each node in the distribution network, including:

[0082] Determine the comparison result between the voltage value of each node in the distribution network and the preset voltage threshold range; according to the comparison result, determine the node whose voltage value exceeds the voltage threshold range as the node to be regulated.

[0083] The comparison result includes that the voltage value is within a preset voltage threshold range, or that the voltage value exceeds a preset voltage threshold range. The preset voltage threshold range may be a pre-set voltage value range that can ensure normal operation of each node.

[0084] Optionally, the control system compares the voltage values ​​in the monitoring data of each node in the distribution network with a preset voltage threshold range to obtain a comparison result for each node. Based on the comparison result of each node, the node whose comparison result is that the voltage value exceeds the voltage threshold range is determined as the node to be regulated.

[0085] In this embodiment, by monitoring the voltage value of each node in the distribution network in real time and comparing it with a preset voltage threshold range, it is determined whether there is an abnormality in the voltage value of each node. This allows the abnormal situation of the distribution network to be grasped in a timely manner, and the nodes with abnormalities are used as nodes to be regulated, and as information sources to participate in synergy in subsequent distributed regulation.

[0086] In an exemplary embodiment, step S204 obtains the target reactive power utilization rate of the node to be regulated according to the monitoring data of the node to be regulated, the preset sensitivity nonlinear model and the target voltage value, including:

[0087] According to the voltage value of the node to be regulated and the target voltage value, the voltage change of the node to be regulated is obtained; the voltage change of the node to be regulated and other monitoring data except the voltage value are input into the inverse function of the sensitivity nonlinear model to obtain the target reactive power utilization rate of the node to be regulated.

[0088] The voltage variation may be the difference between the voltage value and the target voltage value. The inverse function may be the reverse operation of a function.

[0089] Optionally, the control system calculates the difference between the voltage value of the node to be regulated and the target voltage value to obtain the voltage change of the node to be regulated, and inputs the voltage change of the node to be regulated and other monitoring data except the voltage value into the inverse function of the sensitivity nonlinear model to obtain the target reactive power utilization rate of the node to be regulated. The corresponding calculation method is:

[0090]

[0091] Where k is the number of iterations, r is the learning rate of the sensitivity nonlinear model, and ΔV i is the voltage change between the current voltage of node i and the target voltage, It is the inverse function of the sensitivity nonlinear model, which means that the required reactive power regulation is calculated based on the voltage deviation. It should be noted that the number of iterations of k here represents the number of voltage regulation of the node to be regulated. Before the voltage regulation operation is performed, the voltage change between the voltage value in the current monitoring data and the target voltage is used to calculate the target reactive power utilization rate.

[0092] It should be noted that the method for determining the sensitivity nonlinear model is: determine the neural network and input and output of the elements and, at each miniature circuit breaker, build a lightweight neural network based on the locally collected voltage, current, active power and reactive power data. This neural network is used to capture the nonlinear relationship between voltage change and reactive power regulation. The input of the sensitivity nonlinear model is the voltage change, reactive power, active power and power factor of the node, and the output is the reactive voltage sensitivity of the node, that is, the response degree of voltage change to reactive power regulation. Specifically, the structure of the network model is: Input layer: input vector ; Hidden layer: shallow multilayer perceptron with ReLU activation function, with 3 hidden layers; Output layer: reactive voltage sensitivity model output, indicating the responsiveness of voltage to reactive regulation , that is, reactive voltage sensitivity. Among them, ΔV i is the voltage change at node i, I i is the current at node i, P i is the active power of node i, Q i is the reactive power of node i, PF i is the power factor of node i. The expressions of the initial sensitivity nonlinear model include:

[0093]

[0094] in, represents the training parameters of the neural network, Represents the voltage change, is the reactive power, is the active power, is the power factor. Further, the training process of the sensitivity nonlinear model is to optimize the model parameters by minimizing the prediction error. , that is, to minimize the difference between the reactive voltage sensitivity predicted by the model and the actual observed value. The training objectives are:

[0095]

[0096] in, is the actual reactive voltage sensitivity obtained from historical data, and T is the total amount of training data. In order to cope with the dynamic changes in node electrical states and load fluctuations, the training of the initial sensitivity nonlinear model adopts an online recursive training method, that is, the model parameters are updated in real time when each new data point arrives. This training method does not require the storage of a large amount of historical data or offline training, but dynamically optimizes the model through incremental updates (or recursive updates). The core of the online recursive training method is to use the current samples to adjust the model parameters without traversing all historical data. This embodiment uses recursive least squares to achieve this goal. The update process of this method can be expressed by the following formula:

[0097]

[0098] In the formula, is the network parameter of node i after the tth iteration, is the covariance matrix, used to adjust the speed of the learning process, is the learning rate, which controls the step size of the model update. is a regularization factor used to balance the weight of historical information and current data. is the prediction error, which reflects the deviation between the predicted value of the current model and the actual value. When each new sampling period t+1 comes, the node will calculate the current voltage change and reactive power Parameters such as the reactive power voltage sensitivity are input into the neural network to obtain a new reactive power voltage sensitivity prediction value. Then, the node compares the prediction value with the actual observation value. Compare and calculate the error . Using the recursive least squares method, the error Adjusting network weights , so that the model can make a more accurate prediction of the new voltage change and reactive power regulation relationship.

[0099] In this embodiment, the sensitivity nonlinear model realizes local self-modeling of the distribution network, avoids dependence on traditional complex power flow calculation models, realizes dynamic tracking of the relationship between node voltage and reactive power changes, and reversely solves the target reactive power utilization rate of the node to be regulated through the inverse function of the sensitivity nonlinear model, thereby improving the calculation efficiency and further improving the adaptability of the control system.

[0100] In an exemplary embodiment, Figure 3 As shown, in step S206, according to the target reactive power utilization rate, the sensitivity nonlinear model and the monitoring data of the adjacent nodes, the target reactive power utilization rate of each adjacent node is iteratively calculated using the consistency algorithm model until the preset iteration end condition is met, and the content of the target reactive power utilization rate of each adjacent node is obtained, including:

[0101] Step S302, obtaining the voltage change of each adjacent node.

[0102] Optionally, the control system obtains a target voltage value of each adjacent node, and determines the difference between a current voltage value in the monitoring data of each adjacent node and the target voltage value as a voltage variation of each adjacent node.

[0103] Step S304: input the voltage variation of the monitoring data of each adjacent node and other monitoring data except the voltage value into the sensitivity nonlinear model to obtain the reactive voltage sensitivity of each adjacent node.

[0104] Optionally, the control system inputs the voltage change of the monitoring data of the adjacent nodes, and other monitoring data except the voltage value including: current, active power, reactive power and power factor, into the sensitivity nonlinear model to obtain the reactive voltage sensitivity of each adjacent node.

[0105] Step S306, inputting the reactive voltage sensitivity and target reactive utilization rate of the adjacent nodes into the consistency algorithm model until the preset iteration end condition is met, thereby obtaining the target reactive utilization rate of each adjacent node.

[0106] Among them, the expressions corresponding to the consistency algorithm model include:

[0107]

[0108] Where k represents the number of iterations, represents the target reactive power utilization, r and They represent the learning rate and training parameters of the sensitivity nonlinear model, Q i represents the reactive power of the ith node, is the Chebyshev filter acceleration operator, W represents the state transfer matrix composed of the state transfer weights of each communication link in the distribution network, represents the reactive voltage sensitivity of node i, represents the sensitivity weighting matrix, represents the step length, represents the set of adjacent nodes of node i, and j represents the jth adjacent node.

[0109] The preset iteration end condition includes any one of the following: the voltage values ​​of all nodes are within the voltage threshold range, the difference between two consecutive updated target reactive power utilization rates is lower than the preset threshold, and the number of iterations reaches the preset iteration number threshold.

[0110] Optionally, the control system inputs the reactive voltage sensitivity and target reactive utilization of adjacent nodes into a consistency algorithm model and performs iterative calculations until the preset iteration end conditions are met, namely, the voltage values ​​of all nodes are within the voltage threshold range, the difference between two adjacent updated target reactive utilizations is lower than a preset threshold, and the number of iterations reaches any one of the preset iteration number thresholds, to obtain the target reactive utilization of each adjacent node.

[0111] In this embodiment, in order to accelerate the convergence speed of the consistency algorithm model, especially in application scenarios where the system requires high precision and fast response, a Chebyshev polynomial filter is introduced. The filter can accelerate the convergence process of the consistency algorithm and avoid excessive oscillation problems caused by high-frequency fluctuations or network disturbances. The Chebyshev filter controls the step size of each state update by designing a suitable filter function, making the iterative process smoother, thereby improving the efficiency of voltage regulation of the distribution network.

[0112] In an exemplary embodiment, step S306 inputs the reactive voltage sensitivity and target reactive utilization of the adjacent nodes into the consistency algorithm model until a preset iteration end condition is met to obtain the target reactive utilization of each adjacent node, including:

[0113] For each adjacent node, the reactive voltage sensitivity and target reactive utilization of the adjacent node are respectively input into the model of the consistency algorithm to obtain a candidate reactive utilization; when the candidate reactive utilization satisfies the preset inverter constraint, the candidate reactive utilization is used as the target reactive utilization after this round of iterative update; when the candidate reactive utilization does not satisfy the preset inverter constraint, the candidate reactive utilization is projected according to the inverter constraint to obtain the target reactive utilization after this round of iterative update; return to the step of inputting the reactive voltage sensitivity and target reactive utilization of the adjacent nodes into the consistency algorithm model until the preset iteration end condition is met to obtain the target reactive utilization of each adjacent node.

[0114] The candidate reactive power utilization is a reactive power utilization obtained by iterative calculation without determining whether it complies with the inverter constraint.

[0115] The inverter constraints may be physical constraints of the photovoltaic inverter, including:

[0116]

[0117]

[0118] Among them, Q i (k) represents the reactive power obtained by the i-th node in the k-th iteration, Q min Indicates the minimum reactive capacity of the PV inverter, Q maxIndicates the maximum reactive capacity of the photovoltaic inverter, cosφ i Represents the power factor of the i-th node.

[0119] Optionally, the control system inputs the reactive voltage sensitivity and target reactive utilization rate of each adjacent node into the model of the consistency algorithm to obtain a candidate reactive utilization rate; updates the current reactive power and power factor with the candidate reactive utilization rate, and compares and analyzes the updated reactive power and updated power factor with the preset inverter constraints. When the candidate reactive utilization rate satisfies the preset inverter constraints, the control system uses the candidate reactive utilization rate as the target reactive utilization rate after this round of iterative updates; when the candidate reactive utilization rate does not satisfy the preset inverter constraints, the candidate reactive utilization rate is projected according to the inverter constraints to obtain the target reactive utilization rate after this round of iterative updates. The corresponding projection operations include:

[0120]

[0121]

[0122] The reactive power and reactive power utilization corresponding to the power factor projected into the inverter constraint range are used as the updated target reactive power utilization. The control system returns to the step of inputting the reactive voltage sensitivity and target reactive power utilization of the adjacent nodes into the consistency algorithm model until the preset iteration end condition is met to obtain the target reactive power utilization of each adjacent node.

[0123] In this embodiment, after each round of iteration, the feasibility of the calculated candidate reactive power utilization is checked, and the updated state is corrected by considering the local sensitivity model and the inverter constraints. When updating the state, each node avoids over-adjustment when the voltage is close to the target range, thereby improving safety and stability.

[0124] In an exemplary embodiment, Figure 4 As shown, a distributed autonomous coordinated voltage regulation method for a distribution network based on an intelligent miniature circuit breaker is provided, comprising:

[0125] Step 1: Self-detection of node status of intelligent miniature circuit breakers. Use intelligent miniature circuit breakers to periodically collect data (monitoring data) such as voltage, current, active power, reactive power and power factor of each node, monitor the node voltage status in real time, determine whether there is a voltage over-limit situation, and automatically generate alarm information, marking abnormal nodes as "nodes to be regulated".

[0126] Exemplarily, in the distributed autonomous collaborative voltage regulation method of this embodiment, voltage state self-detection is the first step, which is mainly responsible for real-time monitoring of the voltage and power state of each node, timely discovering and marking the node with voltage exceeding the limit, so as to start the subsequent regulation process. During the periodic acquisition process, each intelligent miniature circuit breaker will obtain multiple electrical parameters of the node, including voltage (Vi), current (Ii), active power (Pi), reactive power (Qi) and power factor (PFi), etc. Through local sensors and computing modules, these data can be obtained in real time within each sampling period T. At the end of each cycle, the intelligent miniature circuit breaker will analyze the collected voltage data to determine whether the voltage exceeds the preset upper and lower limits of the voltage (voltage threshold range). When it is determined that the voltage of a node exceeds the limit, the intelligent miniature circuit breaker will automatically generate an alarm message and mark the node as a "node to be regulated". This node will become one of the leading nodes in the subsequent regulation process, responsible for initiating regulation operations, or participating in synergy as an information source in subsequent distributed regulation.

[0127] Step 2: Local self-modeling of intelligent miniature circuit breakers. A local "reactive power-voltage" sensitivity nonlinear model based on a lightweight neural network is proposed, and the online recursive training method is used to update the parameters in real time to achieve dynamic tracking of the relationship between node voltage and reactive power changes without relying on the distribution network power flow model.

[0128] Step 3: Determine the control target of the dominant node. For the abnormal nodes (nodes to be controlled) identified in step 1, mark them as dominant nodes, and calculate the target reactive power utilization or target reactive power output required for the node voltage to return to a safe range based on the local sensitivity model obtained in step 2, providing an initial reference for subsequent distributed collaborative control.

[0129] Step 4: Construct an improved consistency self-discipline collaborative control model. Based on the traditional consistency algorithm, the Chebyshev polynomial filtering method is introduced to improve the algorithm. By integrating the local reactive power-voltage sensitivity information of the node, an improved consistency iteration equation (consistency algorithm model) with "sensitivity weighting" is constructed to make the distributed iteration more accurate, fast and stable.

[0130] For example, the basic principle of the consensus algorithm is to coordinate the status of each node through information exchange between nodes to ensure that a consistent goal is achieved among multiple nodes. In the present invention, the target reactive power utilization rate of each node i is In each iteration, the target state of its neighbor node (adjacent node) j is updated. The iteration formula of the traditional classical consensus algorithm is as follows:

[0131]

[0132] in, For Node i The target reactive power utilization or output at the kth iteration is, is the step length, is the set of neighbor nodes of node i.

[0133] The traditional consistency algorithm does not consider the nonlinear response characteristics between nodes, while the present invention introduces the reactive power-voltage sensitivity model of each node , so that the state update of each node is not only affected by the state difference of neighboring nodes, but also weighted adjustment based on local sensitivity information. Specifically, the target state update formula of each node i is modified as follows:

[0134]

[0135] in, is the reactive-voltage sensitivity function obtained based on lightweight neural network training in step 2, which dynamically reflects the nonlinear effect of node voltage change on reactive regulation. Nodes with high sensitivity values ​​will be more active in state update during the regulation process, while nodes with low sensitivity will be more conservative in reactive adjustment, thereby avoiding over-regulation of the system.

[0136] In order to accelerate the convergence speed of the consistency algorithm, especially in application scenarios where the system requires high precision and fast response, the present invention introduces a Chebyshev polynomial filter. This filter can accelerate the convergence process of the consistency algorithm and avoid excessive oscillation problems caused by high-frequency fluctuations or network disturbances. The Chebyshev filter controls the step size of each state update by designing a suitable filter function, making the iterative process smoother. Specifically, the form of the Chebyshev filter is as follows:

[0137]

[0138]

[0139] in, It is the core function of the Chebyshev filter. cosh and sech represent the hyperbolic cosine function and the hyperbolic secant function respectively. They can generate appropriate acceleration curves and control the smoothness and acceleration effect in the convergence process by adjusting k and x. is the Chebyshev filter acceleration operator, W represents the state transfer matrix composed of the state transfer weights of each communication link in the distribution network, It is the control parameter of the filter, which determines the accuracy and response speed of the filtering. It can be understood that there is a communication link between each node in the distribution network, and the weight is set in advance for the transmission of control state variables between two nodes.

[0140] The iterative model of the improved consensus algorithm invented is as follows:

[0141]

[0142] in, represents the system state variable vector at the k+1th iteration, The convergence process of the consistency algorithm is accelerated by the Chebyshev filter. is the reactive power-voltage sensitivity function, is a sensitivity weighted matrix, representing the contribution of each node to the state update in an iteration.

[0143] Step 5: Iteration of distributed consistency coordinated voltage control in the distribution network. After the dominant node initiates control, each node exchanges the current target reactive power utilization or output state with its neighboring nodes, and performs distributed iterative updates through the improved consistency algorithm proposed in step 4. When iteratively updating each node, the constraints of the local sensitivity model are considered at the same time to ensure that the iterative process meets the physical constraints of the inverter until the coordinated control converges for the entire network or local nodes.

[0144] For example, to perform state exchange and initial setting, the leading node first calculates the target reactive power utilization rate at the current moment Broadcast to all nodes in its neighborhood, where:

[0145]

[0146] is the weight coefficient in the state transfer matrix, is the target reactive power utilization of node j in the previous iteration. After receiving this information, the neighboring node initializes its own state according to the improved consistency algorithm proposed in step 4, combined with the local sensitivity information and the filter acceleration matrix. At the same time, the maximum number of iterations k is set max , voltage upper and lower limits and The voltage deviation threshold and state change threshold are 1.05pu and 0.95pu respectively.

[0147] Furthermore, distributed consistency is iterated and updated. In each iteration cycle k, each node updates the state of its neighboring nodes according to the state of its neighboring nodes. And the local sensitivity model , use the improved consistency algorithm described in step 4 to update the target reactive power utilization or reactive power output. The update formula is as follows:

[0148]

[0149] in, is the Chebyshev filter acceleration operator, is the reactive power-voltage sensitivity function of node i, represents the sensitivity weighting matrix, is the step size. The updated state is corrected by considering the local sensitivity model and the inverter constraints. When updating the state, each node needs to combine the reactive power-voltage nonlinear characteristics reflected in the local sensitivity model to avoid over-regulation when the voltage is close to the target range. To this end, after each iteration, the feasibility of the updated reactive power utilization or reactive power output is checked to ensure that the inverter physical constraints are met.

[0150] Determine the iteration termination conditions and convergence. When the system repeats the above state exchange and local update steps, the target reactive power utilization or output of each node will continue to approach a certain stable point. When any of the following conditions is met, the iteration can be determined to have converged and the update can be terminated: Voltage deviation threshold: The voltage of each node has entered interval, and the voltage fluctuation is less than the preset threshold; state change threshold: the change in reactive power utilization between two adjacent iterations The maximum number of iterations: the maximum number of iterations k set in advance has been reached max .

[0151] Step 6: Reactive control command execution and closed-loop feedback. After the iteration converges, each intelligent micro-breaker sends specific reactive output commands to local photovoltaic inverters and other reactive devices based on the collaborative iteration results to complete the voltage regulation action, and monitor the voltage status in real time to form a control closed loop to continuously optimize and adjust the voltage regulation effect.

[0152] For example, when the reactive power regulation targets of all nodes converge through the consistency algorithm, the nodes will calculate and determine their own reactive power utilization rates based on the final collaborative iteration results. This target reactive power output is a necessary regulation to ensure that the voltage returns to a safe range and maintain system stability. The intelligent miniature circuit breaker will send specific reactive power control instructions to local photovoltaic inverters and other reactive devices through its control module. These devices will adjust their reactive power output according to the instructions received, thereby adjusting the grid voltage. Once the reactive power control instruction is issued and executed, the intelligent miniature circuit breaker will monitor the voltage status in real time. By periodically collecting voltage data, the system will detect whether the voltage has returned to the target voltage range. and and ensure that no node in the system continues to exceed the limit.

[0153] In this embodiment, accurate and efficient voltage regulation can be achieved. By using the distributed collaborative control method of local self-modeling and sensitivity weighting, the voltage of each node can be accurately adjusted, especially in the case of large fluctuations in photovoltaic power generation, it can respond to voltage changes in real time to ensure that the grid voltage remains within a safe range. The sensitivity model can dynamically track the nonlinear relationship between node voltage and reactive power, provide a more accurate decision-making basis for regulation, and avoid the lag and lack of precision in traditional voltage regulation methods. There is no need to rely on complex power flow models, reduce computational complexity, and avoid dependence on traditional complex power flow calculation models through intelligent micro circuit breakers and local self-modeling. In the distribution network with high penetration photovoltaic power generation, the traditional power flow analysis method has a large amount of calculation and delayed update, while the local self-modeling method provided in this embodiment can dynamically update the model according to field data in real time, reduce the computational burden, and improve the system response speed. Support distributed collaborative control, improve system robustness and flexibility, and introduce a distributed consistency algorithm combined with Chebyshev filter acceleration to efficiently achieve collaborative regulation between nodes. Through information exchange and sensitivity weighting between nodes, it is ensured that the system can achieve coordinated voltage regulation of the entire network or local area when facing load fluctuations and photovoltaic power generation fluctuations. It reduces the on-site debugging and operation and maintenance costs and increases the intelligent adjustment capability. It uses a lightweight neural network to train the reactive-voltage sensitivity model and updates the model parameters in real time through an online recursive training method. In this way, the system can dynamically optimize the adjustment strategy according to the real-time status of the distribution network, improving the system's adaptive ability. It supports plug-and-play and remote operation and maintenance, and does not require secondary debugging after on-site installation, which reduces manual intervention in the system deployment and debugging process and reduces operation and maintenance costs.

[0154] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0155] Based on the same inventive concept, the embodiment of the present application also provides a distribution network distributed coordinated voltage regulation device for implementing the above-mentioned distribution network distributed coordinated voltage regulation method. The implementation solution provided by the device to solve the problem is similar to the implementation solution recorded in the above-mentioned method, so the specific limitations in one or more distribution network distributed coordinated voltage regulation device embodiments provided below can refer to the above-mentioned limitations on the distribution network distributed coordinated voltage regulation method, and will not be repeated here.

[0156] In an exemplary embodiment, Figure 5 As shown, a distributed coordinated voltage regulation device 500 for a distribution network is provided, comprising: a node determination module 501, a reactive power determination module 502, an iterative update module 503 and an instruction output module 504, wherein:

[0157] The node determination module 501 is used to determine the node to be regulated from all nodes according to the monitoring data of each node in the distribution network; the node is a node equipped with a miniature circuit breaker and a photovoltaic inverter device;

[0158] The reactive power determination module 502 is used to obtain the target reactive power utilization rate of the node to be regulated according to the monitoring data of the node to be regulated, the preset sensitivity nonlinear model and the target voltage value. The sensitivity nonlinear model is obtained by training the preset neural network through historical monitoring data.

[0159] The iterative update module 503 is used to control the node to be regulated to broadcast the target reactive power utilization to the adjacent nodes, and according to the target reactive power utilization, the sensitivity nonlinear model and the monitoring data of the adjacent nodes, the target reactive power utilization of each adjacent node is iteratively calculated using the consistency algorithm model until the preset iteration end condition is met, thereby obtaining the target reactive power utilization of each adjacent node.

[0160] The iterative update module 503 is also used to control each adjacent node to broadcast its target reactive power utilization to new adjacent nodes in a direction away from the node to be regulated. According to the target reactive power utilization, the sensitivity nonlinear model and the monitoring data of the new adjacent nodes, the target reactive power utilization of each new adjacent node is iteratively calculated using a consistency algorithm model to obtain the target reactive power utilization of each new adjacent node.

[0161] The iterative update module 503 is also used to take the new adjacent node as the current adjacent node, and return to the step of controlling each adjacent node to broadcast its target reactive power utilization to the new adjacent node in a direction away from the node to be regulated, until all nodes in the distribution network are traversed to obtain the target reactive power utilization of each node.

[0162] The instruction output module 504 is used to generate a reactive power output instruction for each node according to the target reactive power utilization rate of each node, so as to instruct each node to perform a voltage regulation operation.

[0163] Furthermore, in one embodiment, the node determination module 501 is also used to determine the comparison result between the voltage value of each node in the distribution network and the preset voltage threshold range; based on the comparison result, the node whose voltage value exceeds the voltage threshold range is determined as the node to be regulated.

[0164] Furthermore, in one embodiment, the reactive power determination module 502 is also used to obtain the voltage change of the node to be regulated based on the voltage value of the node to be regulated and the target voltage value; the voltage change of the node to be regulated and other monitoring data except the voltage value are input into the inverse function of the sensitivity nonlinear model to obtain the target reactive power utilization rate of the node to be regulated.

[0165] Furthermore, in one embodiment, the iterative update module 503 is also used to obtain the voltage change of each adjacent node; the voltage change of the monitoring data of each adjacent node, as well as other monitoring data except the voltage value, are input into the sensitivity nonlinear model to obtain the reactive voltage sensitivity of each adjacent node; the reactive voltage sensitivity and target reactive utilization rate of the adjacent nodes are input into the consistency algorithm model until the preset iteration end condition is met to obtain the target reactive utilization rate of each adjacent node.

[0166] Furthermore, in one embodiment, the iterative update module 503 is also used to input the reactive voltage sensitivity and target reactive utilization of each adjacent node into the model of the consistency algorithm, respectively, to obtain a candidate reactive utilization; when the candidate reactive utilization satisfies the preset inverter constraint, the candidate reactive utilization is used as the target reactive utilization after this round of iterative update; when the candidate reactive utilization does not satisfy the preset inverter constraint, the candidate reactive utilization is projected according to the inverter constraint to obtain the target reactive utilization after this round of iterative update; and the step of inputting the reactive voltage sensitivity and target reactive utilization of the adjacent nodes into the consistency algorithm model is returned until the preset iteration end condition is met to obtain the target reactive utilization of each adjacent node.

[0167] Each module in the above-mentioned distribution network distributed coordinated voltage regulation device 500 can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0168] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 6 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store monitoring data, target reactive power utilization and other data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a distributed collaborative voltage regulation method for a distribution network is implemented.

[0169] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0170] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0171] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0172] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0173] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0174] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0175] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A distributed coordinated voltage regulation method for a distribution network, characterized in that: The method comprises: According to the monitoring data of each node in the distribution network, a node to be regulated is determined from all the nodes; the node is a node equipped with a miniature circuit breaker and a photovoltaic inverter device; According to the monitoring data of the node to be regulated, the preset sensitivity nonlinear model and the target voltage value, the target reactive power utilization rate of the node to be regulated is obtained; the sensitivity nonlinear model is obtained by training a preset neural network with historical monitoring data; Control the node to be regulated to broadcast the target reactive power utilization rate to adjacent nodes, and iteratively calculate the target reactive power utilization rate of each adjacent node using a consistency algorithm model according to the target reactive power utilization rate, the sensitivity nonlinear model and the monitoring data of the adjacent nodes, until a preset iteration end condition is met, thereby obtaining the target reactive power utilization rate of each adjacent node; Control each of the adjacent nodes to broadcast its target reactive power utilization rate to new adjacent nodes in a direction away from the node to be regulated, and iteratively calculate the target reactive power utilization rate of each of the new adjacent nodes using a consistency algorithm model according to the target reactive power utilization rate, the sensitivity nonlinear model and the monitoring data of the new adjacent nodes to obtain the target reactive power utilization rate of each of the new adjacent nodes; The new adjacent node is used as the current adjacent node, and the step of controlling each adjacent node to broadcast its target reactive power utilization rate to the new adjacent node in a direction away from the node to be regulated is returned until all the nodes of the distribution network are traversed to obtain the target reactive power utilization rate of each node; According to the target reactive power utilization rate of each node, a reactive power output instruction for each node is generated to instruct each node to perform a voltage regulation operation.

2. The method according to claim 1, characterized in that The monitoring data includes voltage values, and the nodes to be regulated are determined from all the nodes according to the monitoring data of each node in the distribution network, including: Determine a comparison result between a voltage value of each of the nodes in the distribution network and a preset voltage threshold range; According to the comparison result, the node whose voltage value exceeds the voltage threshold range is determined as the node to be regulated.

3. The method according to claim 2, characterized in that The preset iteration end condition includes any one of the following: the voltage values ​​of all the nodes are within the voltage threshold range, the difference between the target reactive power utilization rates updated two adjacent times is lower than a preset threshold, and the number of iterations reaches a preset iteration number threshold.

4. The method according to claim 2, characterized in that The step of obtaining a target reactive power utilization rate of the node to be regulated according to the monitoring data of the node to be regulated, a preset sensitivity nonlinear model and a target voltage value includes: Obtaining a voltage change of the node to be regulated according to the voltage value of the node to be regulated and the target voltage value; The voltage variation of the node to be regulated and other monitoring data except the voltage value are input into the inverse function of the sensitivity nonlinear model to obtain the target reactive power utilization rate of the node to be regulated.

5. The method according to claim 1, characterized in that: The target reactive power utilization rate of each of the adjacent nodes is iteratively calculated using a consistency algorithm model according to the target reactive power utilization rate, the sensitivity nonlinear model and the monitoring data of the adjacent nodes until a preset iteration end condition is met to obtain the target reactive power utilization rate of each of the adjacent nodes, including: Obtaining a voltage change of each of the adjacent nodes; Inputting the voltage variation of the monitoring data of each of the adjacent nodes and other monitoring data except the voltage value into the sensitivity nonlinear model to obtain the reactive voltage sensitivity of each of the adjacent nodes; The reactive voltage sensitivity of the adjacent nodes and the target reactive utilization rate are input into the consistency algorithm model until a preset iteration end condition is met, thereby obtaining the target reactive utilization rate of each adjacent node.

6. The method according to claim 5, characterized in that The reactive voltage sensitivity of the adjacent nodes and the target reactive utilization rate are input into the consistency algorithm model until a preset iteration end condition is met to obtain the target reactive utilization rate of each adjacent node, including: For each of the adjacent nodes, the reactive voltage sensitivity of the adjacent node and the target reactive utilization rate are respectively input into a model of a consistency algorithm to obtain a candidate reactive utilization rate; In the case where the candidate reactive power utilization rate satisfies the preset inverter constraint, the candidate reactive power utilization rate is used as the target reactive power utilization rate after the current round of iterative update; When the candidate reactive power utilization rate does not satisfy the preset inverter constraint, a projection operation is performed on the candidate reactive power utilization rate according to the inverter constraint to obtain a target reactive power utilization rate after the current round of iterative update; Return to the step of inputting the reactive voltage sensitivity and the target reactive utilization rate of the adjacent nodes into the consistency algorithm model until a preset iteration end condition is met to obtain the target reactive utilization rate of each adjacent node.

7. The method according to any one of claims 1 to 6, characterized in that: The expressions corresponding to the consistency algorithm model include: Where k represents the number of iterations, represents the target reactive power utilization, r and Respectively represent the learning rate and training parameters of the sensitivity nonlinear model, Q i represents the reactive power of the ith node, is the Chebyshev filter acceleration operator, W represents the state transfer matrix composed of the state transfer weights of each communication link in the distribution network, represents the reactive voltage sensitivity of the node i, represents the sensitivity weighting matrix, represents the step length, represents the set of adjacent nodes of node i, and j represents the jth adjacent node.

8. A distributed coordinated voltage regulation device for a distribution network, characterized in that: The device comprises: A node determination module, used to determine the node to be regulated from all the nodes according to the monitoring data of each node in the distribution network; the node is a node equipped with a miniature circuit breaker and a photovoltaic inverter device; A reactive power determination module is used to obtain a target reactive power utilization rate of the node to be regulated according to the monitoring data of the node to be regulated, a preset sensitivity nonlinear model and a target voltage value; the sensitivity nonlinear model is obtained by training a preset neural network with historical monitoring data; An iterative update module is used to control the node to be regulated to broadcast the target reactive power utilization rate to adjacent nodes, and iteratively calculate the target reactive power utilization rate of each adjacent node using a consistency algorithm model according to the target reactive power utilization rate, the sensitivity nonlinear model and the monitoring data of the adjacent nodes, until a preset iteration end condition is met, thereby obtaining the target reactive power utilization rate of each adjacent node; The iterative update module is also used to control each of the adjacent nodes to broadcast its target reactive power utilization rate to new adjacent nodes in a direction away from the node to be regulated, and to iteratively calculate the target reactive power utilization rate of each of the new adjacent nodes using a consistency algorithm model according to the target reactive power utilization rate, the sensitivity nonlinear model and the monitoring data of the new adjacent nodes to obtain the target reactive power utilization rate of each of the new adjacent nodes; The iterative update module is further used to take the new adjacent node as the current adjacent node, and return to the step of controlling each adjacent node to broadcast its target reactive power utilization rate to the new adjacent node in a direction away from the node to be regulated, until all the nodes of the distribution network are traversed to obtain the target reactive power utilization rate of each node; The instruction output module is used to generate a reactive power output instruction for each of the nodes according to the target reactive power utilization rate of each of the nodes, so as to instruct each of the nodes to perform a voltage regulation operation.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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