Energy scheduling method, device and equipment for low-voltage distribution network and storage medium

Abstract

The embodiment of the invention relates to the field of low-voltage power distribution, and provides an energy scheduling method and device for a low-voltage distribution network, equipment and a storage medium, and the method comprises the steps: obtaining the operation data of a target low-voltage distribution network; responding to an input instruction for inputting the operation data of the target low-voltage distribution network into the fault diagnosis and recognition model, and obtaining a fault area and a fault type of the target low-voltage distribution network; obtaining a target energy scheduling planning strategy according to the fault region and the fault type; receiving load feedback information of the user load end after the target energy scheduling planning strategy is executed; and optimizing the target energy scheduling planning strategy by taking the power supply feedback information in the strategy optimization function as a target to obtain the optimized target energy scheduling planning strategy, and executing the optimized target energy scheduling planning strategy. Through combination of the fault diagnosis identification model and the operation data of the target low-voltage distribution network, adaptability and response speed of the low-voltage distribution network are improved, and reliable and stable operation of the low-voltage distribution network is guaranteed.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of low-voltage power distribution, and in particular to an energy scheduling method for a low-voltage distribution network, an energy scheduling device for a low-voltage distribution network, a corresponding electronic device, and a corresponding computer-readable storage medium. Background Art

[0002] In the current power supply system, the distribution network in the low-voltage area (referred to as the low-voltage distribution network) is a key link directly facing users. Its stable operation is crucial to ensuring reliable power supply. For example, in the scenario of a new large factory, it is predicted that the load of the distribution transformer near the new large factory will increase significantly. At this time, there is a need to build a new distribution transformer for load cutover. However, the operation of the new distribution transformer is prone to regional failures for the low-voltage distribution network with complex structure, numerous equipment and wide distribution.

[0003] In the related technologies for energy dispatch planning of low-voltage distribution networks with regional faults, a rule-based energy dispatch scheme or an optimization algorithm-based energy dispatch scheme can be adopted. Among them, the rule-based energy dispatch scheme is usually manifested as energy dispatch according to pre-set simple rules; the energy dispatch scheme based on the optimization algorithm is usually manifested as the use of genetic algorithms, particle swarm algorithms, etc. to consider various constraints of the distribution network and seek the optimal dispatch scheme. However, whether it is a rule-based energy dispatch scheme or an optimization algorithm-based energy dispatch scheme, it is difficult to ensure the reliable and stable operation of the low-voltage distribution network due to the uncertainty of the low-voltage distribution network operating environment. Summary of the invention

[0004] The embodiments of the present application provide an energy scheduling method, device, equipment and storage medium for a low-voltage distribution network, which can improve the adaptability and response speed of the low-voltage distribution network and ensure the reliable and stable operation of the low-voltage distribution network.

[0005] In one aspect, an embodiment of the present application provides an energy dispatching method for a low-voltage distribution network, involving an energy dispatching terminal, wherein the energy dispatching terminal has a fault diagnosis and identification model, and the method includes:

[0006] Obtain the operating data of the target low-voltage distribution network;

[0007] In response to an input instruction of inputting the operation data of the target low-voltage distribution network into the fault diagnosis and identification model, the fault area and fault type of the target low-voltage distribution network are obtained; wherein the fault diagnosis and identification model includes a network topology layer, a feature analysis layer and a fault analysis layer, the fault area and fault type are obtained based on a fault analysis of the fault feature data by the fault analysis layer, the fault feature data are obtained based on feature mining of a fault analysis graph by the feature analysis layer, and the fault analysis graph is generated based on a topological analysis of the operation data by the network topology layer;

[0008] Obtaining a target energy scheduling planning strategy according to the fault area and the fault type;

[0009] Receiving load feedback information from a user load end after executing the target energy scheduling planning strategy; the load feedback information is used to construct a strategy optimization function;

[0010] The target energy scheduling planning strategy is optimized with the power supply feedback information in the strategy optimization function as the target, an optimized target energy scheduling planning strategy is obtained and the optimized target energy scheduling planning strategy is executed.

[0011] On the other hand, an embodiment of the present application further provides an energy dispatching device for a low-voltage distribution network, which is applied to an energy dispatching terminal, wherein the energy dispatching terminal has a fault diagnosis and identification model, and the device includes:

[0012] An operation data acquisition module is used to acquire the operation data of the target low-voltage distribution network;

[0013] A fault diagnosis module, for responding to an input instruction of inputting the operation data of the target low-voltage distribution network into the fault diagnosis identification model, to obtain the fault area and fault type of the target low-voltage distribution network; wherein the fault diagnosis identification model comprises a network topology layer, a feature analysis layer and a fault analysis layer, the fault area and fault type are obtained based on a fault analysis of the fault feature data by the fault analysis layer, the fault feature data are obtained based on feature mining of a fault analysis graph by the feature analysis layer, and the fault analysis graph is generated based on a topological analysis of the operation data by the network topology layer;

[0014] A planning strategy generation module, used to obtain a target energy scheduling planning strategy according to the fault area and the fault type;

[0015] A feedback information receiving module, used to receive load feedback information from a user load end after executing the target energy scheduling planning strategy; the load feedback information is used to construct a strategy optimization function;

[0016] The energy scheduling module is used to optimize the target energy scheduling planning strategy with the power supply feedback information in the strategy optimization function as the target, obtain the optimized target energy scheduling planning strategy and execute the optimized target energy scheduling planning strategy.

[0017] On the other hand, an embodiment of the present application further provides an electronic device, comprising: a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein when the computer program is executed by the processor, any one of the energy scheduling methods for the low-voltage distribution network is implemented.

[0018] On the other hand, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, any one of the energy scheduling methods for a low-voltage distribution network is implemented.

[0019] On the other hand, an embodiment of the present application further provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute the energy scheduling method for the low-voltage distribution network described in the above aspects.

[0020] The energy dispatching method, device, equipment and storage medium of the low-voltage distribution network provided in the embodiment of the present application, through the fault diagnosis and identification model combined with the operating data of the target low-voltage distribution network, comprehensively analyzes the real-time status of the low-voltage distribution network layer by layer through the network topology layer, the feature analysis layer and the fault analysis layer, quickly and accurately identifies the fault area and the fault type, and obtains the target energy dispatching planning strategy of the fault area based on the accurately identified fault area and the fault type, and can obtain the corresponding energy dispatching planning strategy in a relatively short time under complex and changeable fault scenarios, thereby improving the adaptability of the low-voltage distribution network and the response speed of the low-voltage distribution network; and, the energy dispatching planning strategy can also be continuously optimized through the load feedback information of the user load end, and the power supply feedback information in the strategy optimization function constructed by the load feedback information is used as the target to optimize the target energy dispatching planning strategy, and execute the optimized target energy dispatching planning strategy, while improving the response speed of the low-voltage distribution network, further improving the adaptability of the low-voltage distribution network, and being more conducive to ensuring the reliable and stable operation of the low-voltage distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic diagram of the architecture of a distribution network planning system provided in an embodiment of the present application;

[0022] Figure 2 It is a flowchart of the steps of an energy dispatching method for a low-voltage distribution network provided in an embodiment of the present application;

[0023] Figure 3It is a flowchart of the steps of another energy dispatching method of a low-voltage distribution network in an embodiment of the present application;

[0024] Figure 4 It is a structural block diagram of an energy dispatching device for a low-voltage distribution network in an embodiment of the present application;

[0025] Figure 5 is a structural block diagram of an electronic device provided in an embodiment of the present application;

[0026] Figure 6 It is a structural block diagram of a computer-readable storage medium provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0028] The reliable and stable operation of the low-voltage distribution network is conducive to ensuring the reliable supply of electricity. Therefore, when a regional fault occurs in the low-voltage distribution network, there is a need to dispatch energy to the low-voltage distribution network to ensure the reliable and stable operation of the low-voltage distribution network, and thus ensure the reliable and stable supply of electricity.

[0029] In the related technology of energy dispatch planning for low-voltage distribution networks with regional faults, as an example, a rule-based energy dispatch scheme can be adopted. The scheme is usually manifested as energy dispatch according to pre-set simple rules. For example, when a fault occurs in a certain area, it can be switched to a backup power supply or the power supply line can be adjusted according to a fixed priority. However, the aforementioned energy dispatch method based on the set simple rules lacks comprehensive perception and flexible response capabilities to the real-time status of the distribution network, and it is difficult to achieve efficient energy dispatch under complex and changeable fault scenarios. It is easy to cause insufficient power supply in some areas, resulting in low adaptability of the low-voltage distribution network; as another example, an energy dispatch scheme based on an optimization algorithm can be adopted. The scheme is usually manifested as using genetic algorithms, particle swarm algorithms, etc. to consider various constraints of the distribution network and seek the optimal dispatch scheme. However, the algorithm used in the aforementioned scheme has high computational complexity and large requirements for computing resources. In actual applications, due to the uncertainty of the low-voltage distribution network operating environment, the algorithm converges slowly, and it is difficult to give an effective dispatch strategy in a short time, and it is impossible to meet the rapid response when a fault occurs, thereby failing to ensure the reliable and stable operation of the low-voltage distribution network.

[0030] In summary, the above-mentioned related technologies are difficult to ensure the reliable and stable operation of the low-voltage distribution network due to the uncertainty of the low-voltage distribution network operating environment.

[0031] The embodiment of the present application combines the operating data of the target low-voltage distribution network through the fault diagnosis and identification model, and comprehensively analyzes the real-time status of the low-voltage distribution network layer by layer through the network topology layer, the feature analysis layer and the fault analysis layer, quickly and accurately identifies the fault area and the fault type, and obtains the target energy scheduling planning strategy of the fault area based on the accurately identified fault area and the fault type. It can obtain the corresponding energy scheduling planning strategy in a relatively short time under complex and changeable fault scenarios, improve the adaptability of the low-voltage distribution network and the response speed of the low-voltage distribution network, which is conducive to ensuring the reliable and stable operation of the low-voltage distribution network; and, it can also continuously optimize the energy scheduling planning strategy through the load feedback information of the user load end, optimize the target energy scheduling planning strategy with the power supply feedback information in the strategy optimization function constructed by the load feedback information as the target, and execute the optimized target energy scheduling planning strategy, while improving the response speed of the low-voltage distribution network, further improving the adaptability of the low-voltage distribution network, which is more conducive to ensuring the reliable and stable operation of the low-voltage distribution network.

[0032] Reference Figure 1 , showing a schematic diagram of the architecture of a distribution network planning system provided in an embodiment of the present application. The distribution network planning system can monitor the status of the power distribution network in real time. When a fault occurs in the power distribution network, energy can be dispatched for the aforementioned power distribution network according to the load conditions of the power distribution network, thereby optimizing the power distribution network resource configuration and ensuring the reliable and stable operation of the power distribution network.

[0033] like Figure 1 As shown, the distribution network planning system 100 may include an energy dispatching terminal 101 and a user load terminal 102. The energy dispatching terminal 101 may be mainly responsible for the overall dispatching and management of the power distribution network, such as energy dispatching for a power distribution network with a fault, etc. The user load terminal 102 may be mainly responsible for monitoring the power load of power users and feeding back load information to the energy dispatching terminal 101. It should be noted that the specific energy dispatching terminal and the specific user load terminal may be determined based on the actual distribution network planning scenario, and the embodiments of the present application are not limited thereto.

[0034] Exemplarily, when the distribution network planning system 100 is in the operating environment of a low-voltage distribution network, in a scenario where a large factory is newly built, the distribution network planning system 100 predicts that the distribution transformer load near the newly built large factory will increase significantly, and there is a need to build a new distribution transformer for load cutover. The operation of the new distribution transformer is prone to regional failures for low-voltage distribution networks with complex structures, numerous equipment and wide distribution. At this time, the distribution network planning system 100 can perform energy scheduling planning for low-voltage distribution networks with regional failures. Among them, the energy scheduling end 101 can be a highly automated control center, and the user load end 102 can refer to various types of users in the low-voltage distribution network, including but not limited to the power equipment of various industrial, commercial and residential users including the newly built large factory, and the embodiments of the present application are not limited to this.

[0035] In actual applications, the energy dispatching terminal 101 can store a pre-trained fault diagnosis and identification model 1011. The energy dispatching terminal 101 can combine the fault diagnosis and identification model 1011 to execute the energy dispatching method of the low-voltage distribution network provided in the embodiment of the present application to overcome the regional faults that occur in the target low-voltage distribution network when a new distribution transformer is built in the target low-voltage distribution network.

[0036] In some embodiments of the present application, the fault diagnosis and identification model 1011 can be composed of three layers: a network topology layer, a feature analysis layer, and a fault analysis layer. Specifically, the fault diagnosis and identification model 1011 can perform topological analysis on the operation data of the low-voltage distribution network with regional faults based on the network topology layer, generate a fault analysis diagram, and perform feature mining based on the feature analysis layer according to the aforementioned generated fault analysis diagram to obtain fault feature data, and perform fault analysis on the fault feature data based on the fault analysis layer to obtain the fault area and fault type of the low-voltage distribution network, so that the energy dispatching end 101 can obtain the target energy dispatching planning strategy based on the fault area and fault type output by the fault diagnosis and identification model 1011, and then receive the load feedback information of the user load end 102 after executing the aforementioned target energy dispatching planning strategy, optimize the target energy dispatching planning strategy with the power supply feedback information in the strategy optimization function constructed by the load feedback information as the target, and execute the optimized target energy dispatching planning strategy to achieve energy dispatching of the low-voltage distribution network with faults in the aforementioned area.

[0037] In an embodiment of the present application, the distribution network planning system combines the operating data of the target low-voltage distribution network through a fault diagnosis and identification model, and comprehensively analyzes the real-time status of the low-voltage distribution network layer by layer through the network topology layer, the feature analysis layer and the fault analysis layer, and quickly and accurately identifies the fault area and the fault type, and obtains the target energy scheduling planning strategy for the fault area based on the accurately identified fault area and the fault type. It can obtain the corresponding energy scheduling planning strategy in a relatively short time under complex and changeable fault scenarios, improve the adaptability of the low-voltage distribution network and the response speed of the low-voltage distribution network; and, it can also continuously optimize the energy scheduling planning strategy through load feedback information from the user load end, and further improve the adaptability of the low-voltage distribution network while improving the response speed of the low-voltage distribution network, which is more conducive to ensuring the reliable and stable operation of the low-voltage distribution network.

[0038] Reference Figure 2 , shows a flow chart of the steps of an energy dispatching method for a low-voltage distribution network provided in an embodiment of the present application, which may specifically include the following steps:

[0039] Step S201, obtaining operation data of a target low-voltage distribution network;

[0040] When the distribution network planning system performs the operation of building a new distribution transformer in the operating environment of the low-voltage distribution network, the distribution network planning system can carry out energy dispatch planning for the low-voltage distribution network with regional faults.

[0041] In one embodiment of the present application, the operating data of the target low-voltage distribution network can be collected through the energy dispatching terminal to fully grasp the real-time status of the target low-voltage distribution network based on the collected operating data.

[0042] Optionally, the target low-voltage distribution network can be any low-voltage distribution network in the power system. The low-voltage distribution network can include multiple nodes, and each node can be deployed with multiple types of sensors, such as voltage sensors, current sensors, temperature sensors, etc.; Optionally, distributed energy and energy storage equipment can also be connected to the low-voltage distribution network, among which distributed energy such as solar photovoltaic energy, geothermal energy, natural gas energy, etc., and energy storage equipment includes heat storage equipment, flywheel energy storage units, small compressed air energy storage equipment, etc., and the embodiments of the present application are not limited to this.

[0043] In actual applications, the energy dispatching end can obtain the operating data of the target low-voltage distribution network through multiple types of sensors. By way of example, the operating data may include but are not limited to temperature parameters, voltage parameters, current parameters and power parameters of each node in the target low-voltage distribution network, as well as the output power of the distributed energy connected to the target low-voltage distribution network and the status information of the energy storage device connected to the target low-voltage distribution network. The status information mentioned in the embodiments of the present application refers to the charge status information of the energy storage device.

[0044] Step S202, in response to an input instruction of inputting the operation data of the target low-voltage distribution network into the fault diagnosis and identification model, obtaining the fault area and fault type of the target low-voltage distribution network;

[0045] In some embodiments of the present application, the energy dispatching terminal may store a pre-trained fault diagnosis and recognition model, which may be combined with the operating data of the target low-voltage distribution network to comprehensively analyze the real-time status of the low-voltage distribution network layer by layer, and quickly and accurately identify the fault area and fault type. Specifically, the energy dispatching terminal may respond to an input instruction to input the operating data of the target low-voltage distribution network into the fault diagnosis and recognition model, analyze the operating data of the target low-voltage distribution network layer by layer through the fault diagnosis and recognition model, and output the fault area and fault type of the target low-voltage distribution network.

[0046] Exemplarily, the fault diagnosis and identification model involves artificial intelligence training, which can be obtained based on the parameter vector and its corresponding fault label result training, wherein the sample parameter vector may include but is not limited to sample temperature parameters, sample voltage parameters, sample current parameters, sample power parameters, sample output power and sample status information, and the fault label result may include but is not limited to the fault area label result and the fault type label result. It should be noted that the specific training process of the fault diagnosis and identification model is not limited in the embodiments of the present application.

[0047] Optionally, the model structure of the fault diagnosis and identification model may include a network topology layer, a feature analysis layer, and a fault analysis layer, wherein the fault area and fault type output by the fault diagnosis and identification model can be obtained by performing fault analysis on the fault feature data based on the fault analysis layer; the fault feature data used in the fault analysis performed by the fault analysis layer can be obtained by performing feature mining on the fault analysis graph based on the feature analysis layer; the fault analysis graph used in the feature mining performed by the feature analysis layer can be generated by performing topological analysis on the operation data based on the network topology layer. That is, through the top-down network topology layer, feature analysis layer, and fault analysis layer of the fault diagnosis and identification model, combined with the temperature parameters, voltage parameters, current parameters, and power parameters of each node, the output power of distributed energy, and the status information of the energy storage device, the real-time status of the low-voltage distribution network is fully grasped and analyzed layer by layer, the fault area and fault type are quickly and accurately identified, and efficient energy scheduling is achieved under complex and changeable fault scenarios, thereby improving the adaptability of the low-voltage distribution network.

[0048] Step S203, obtaining a target energy scheduling planning strategy according to the fault area and fault type;

[0049] In some embodiments of the present application, after quickly and accurately identifying the fault area and fault type, a target energy scheduling planning strategy for the fault area can be obtained based on the accurately identified fault area and fault type. The corresponding energy scheduling planning strategy can be obtained in a shorter time under complex and changeable fault scenarios, thereby improving the adaptability and response speed of the low-voltage distribution network.

[0050] In practical applications, the fault information of the fault area can be obtained, and the strategy matching is performed in the preset energy scheduling strategy library based on the fault information and the fault type to obtain at least one final candidate energy scheduling planning strategy. Then, the evaluation index of the target low-voltage distribution network for energy scheduling can be obtained, and the strategy matching is performed in at least one final candidate energy scheduling planning strategy based on the evaluation index to obtain the target energy scheduling planning strategy.

[0051] The preset energy scheduling strategy library can store a collection of various energy scheduling strategies for dealing with different situations. For example, first, relevant information about the fault in the fault area can be collected, such as but not limited to the fault size, fault level and fault range, etc., and then, based on the aforementioned fault information and fault type, strategy matching can be performed in the preset energy scheduling strategy library to obtain at least one optional energy scheduling planning strategy, that is, the final candidate energy scheduling planning strategy; then, the evaluation indicators of the target low-voltage distribution network for energy scheduling can be obtained, such as but not limited to power supply recovery time, energy utilization efficiency, scheduling cost and equipment performance parameters, etc., and then according to the aforementioned evaluation indicators, matching and screening are performed again in the at least one final candidate energy scheduling planning strategy obtained above, so as to determine a target energy scheduling planning strategy that meets the requirements. The target energy scheduling planning strategy can be an energy scheduling planning strategy that matches the fault area. At this time, the aforementioned target energy scheduling planning strategy can be executed as the current distribution network plan.

[0052] Step S204, receiving load feedback information from the user load end after executing the target energy scheduling planning strategy;

[0053] In some embodiments of the present application, the energy dispatching end executes a target energy dispatching planning strategy, and transmits energy to user load cells connected to the fault area according to the aforementioned strategy. In order to further optimize the energy dispatching planning strategy and further improve the adaptability of the low-voltage distribution network, the energy dispatching end can receive load feedback information fed back by the user load end of the user load cell, so as to optimize the executed target energy dispatching planning strategy based on the fed-back load feedback information.

[0054] Exemplarily, load feedback information may include actual load information, load change trend information, and power supply feedback information, among which the actual load information may refer to the specific load value of the user's load end in the actual operating state after executing the target energy scheduling planning strategy, which can reflect the actual load quantity; load change trend information may refer to information about the load's rising, falling or stable changing trends over time and other factors, which is helpful to predict future load conditions; power supply feedback information may be feedback content related to power supply, such as whether the power supply is stable, whether the power supply is sufficient, etc., and the embodiments of the present application are not limited to this.

[0055] Step S205 , optimizing the target energy scheduling planning strategy with the power supply feedback information in the strategy optimization function as the target, obtaining the optimized target energy scheduling planning strategy and executing the optimized target energy scheduling planning strategy.

[0056] Optionally, the embodiment of the present application can construct a strategy optimization function based on load feedback information, that is, construct a strategy optimization function based on the actual load information, load change trend information and power supply feedback information in the load feedback information, and then optimize the target energy scheduling planning strategy based on the constructed strategy optimization function, so as to further improve the adaptability of the low-voltage distribution network while improving the response speed of the low-voltage distribution network, which is more conducive to ensuring the reliable and stable operation of the low-voltage distribution network.

[0057] In one embodiment of the present application, the target energy scheduling planning strategy can be optimized with the power supply feedback information in the strategy optimization function as the target, and the adaptability of the low-voltage distribution network can be further improved while improving the response speed of the low-voltage distribution network. It should be noted that the implementation of this application does not limit the specific optimization process of the target energy scheduling planning strategy.

[0058] Among them, the optimized target energy scheduling planning strategy may refer to the optimal energy scheduling planning strategy. Optionally, after obtaining the optimal energy scheduling planning strategy, the energy scheduling end may execute the optimal energy scheduling planning strategy to implement energy scheduling for the target low-voltage distribution network, and overcome regional faults that occur in the target low-voltage distribution network when a new distribution transformer is built for the target low-voltage distribution network.

[0059] In an embodiment of the present application, a fault diagnosis and identification model is combined with the operating data of the target low-voltage distribution network, and the real-time status of the low-voltage distribution network is comprehensively analyzed layer by layer through the network topology layer, the feature analysis layer and the fault analysis layer, and the fault area and fault type are quickly and accurately identified. Based on the accurately identified fault area and fault type, the target energy scheduling planning strategy of the fault area is obtained, and the corresponding energy scheduling planning strategy can be obtained in a shorter time under complex and changeable fault scenarios, thereby improving the adaptability of the low-voltage distribution network and the response speed of the low-voltage distribution network; and, the energy scheduling planning strategy can also be continuously optimized through the load feedback information of the user load end, and the power supply feedback information in the strategy optimization function constructed by the load feedback information is used as the target, and the target energy scheduling planning strategy is optimized, and the optimized target energy scheduling planning strategy is executed. While improving the response speed of the low-voltage distribution network, the adaptability of the low-voltage distribution network is further improved, which is more conducive to ensuring the reliable and stable operation of the low-voltage distribution network.

[0060] Reference Figure 3 , shows a flowchart of another method for dispatching energy in a low-voltage distribution network provided in an embodiment of the present application, which may specifically include the following steps:

[0061] Step S301, in response to an input instruction of inputting the operation data of the target low-voltage distribution network into the fault diagnosis and identification model, obtaining the fault area and fault type of the target low-voltage distribution network;

[0062] In an embodiment of the present application, the energy dispatching end can store a pre-trained fault diagnosis and identification model. The fault diagnosis and identification model can be combined with the operating data of the target low-voltage distribution network to comprehensively analyze the real-time status of the low-voltage distribution network layer by layer, and quickly and accurately identify the fault area and fault type.

[0063] In some embodiments of the present application, the energy dispatching end can respond to an input instruction to input the operating data of the target low-voltage distribution network into the fault diagnosis and identification model, which is specifically manifested by inputting the temperature parameters, voltage parameters, current parameters and power parameters of each node, as well as the output power and status information into the fault diagnosis and identification model, and then using the fault diagnosis and identification model to analyze the operating data of the target low-voltage distribution network layer by layer, and output the fault area and fault type of the target low-voltage distribution network.

[0064] Optionally, the model structure of the fault diagnosis and identification model may include a network topology layer, a feature analysis layer and a fault analysis layer, wherein the fault area and fault type output by the fault diagnosis and identification model can be obtained by fault analysis of the fault feature data based on the fault analysis layer; the fault feature data used in the fault analysis performed by the fault analysis layer can be obtained by feature mining of the fault analysis graph based on the feature analysis layer; the fault analysis graph used for feature mining by the feature analysis layer can be generated by topological analysis of the operation data based on the network topology layer.

[0065] In practical applications, the operating data of the target low-voltage distribution network can be identified through the fault diagnosis model from top to bottom, namely the network topology layer, feature analysis layer and fault analysis layer. Based on the network topology layer, topological analysis is performed on the operating data of the target low-voltage distribution network to generate a fault analysis diagram. Then, based on the feature analysis layer, feature mining is performed on the fault analysis diagram to obtain fault feature data. Finally, based on the fault analysis layer, fault analysis is performed on the fault feature data to obtain the fault area and fault type of the target low-voltage distribution network.

[0066] Specifically, after the temperature parameters, voltage parameters, current parameters and power parameters, as well as the output power and status information of each node are input into the fault diagnosis and identification model, the temperature parameters, voltage parameters, current parameters and power parameters, as well as the output power and status information of each node can be topologically analyzed in time series based on the network topology layer to generate a fault analysis diagram.

[0067] Topological analysis mainly focuses on processing these data from the perspective of network structure and generating fault analysis diagrams that can reflect the relationship between network structure and operating status.

[0068] Optionally, the generation process of the fault analysis diagram can be expressed as fusing the temperature parameters, voltage parameters, current parameters and power parameters of each node to obtain the parameter vector of each node, and then calculating the topological connection strength between any two different nodes based on the parameter vector between any two different nodes, as well as the output power and status information; constructing a topological relationship matrix with the topological connection strength between any two nodes as the matrix element, and then updating the value of each matrix element in the topological relationship matrix according to the time series to obtain the topological connection strength between any two nodes at a certain point in time, and constructing a three-dimensional topological matrix based on the topological connection strength between any two nodes at a certain point in time, and then representing each node as a vertex of the graph, using the non-zero elements in the three-dimensional topological matrix as the weight of the edge, and generating the fault analysis diagram through the minimum spanning tree of the topological relationship strength.

[0069] For example, assume that the temperature parameter of each node is T n , the voltage parameter of each node is V n , the current parameter of each node is I n, the power parameter of each node is W n , the output power is P D , the status information is S E The network topology layer calculates the temperature parameter T of each node. n 、Voltage parameter V n , current parameter I n And power parameter W n Fusion is performed to obtain the parameter vector of each node, where the parameter vector of the nth node can be expressed as ; Furthermore, the network topology layer can be based on the parameter vector between any two different nodes and the output power P D and status information S E , calculate the topological connection strength between any two different nodes. The specific formula is as follows:

[0070]

[0071] Among them, M ij It can refer to the topological connection strength between node i and node j; T i It can refer to the temperature parameter of node i, T j It can refer to the temperature parameter of node j, V i It can refer to the voltage parameter of node i, V j It can refer to the voltage parameter of node j, I i It can refer to the current parameter of node i, I j can refer to the current parameter of node j, W i It can refer to the power parameter of node i, W j It can refer to the power parameter of node j; d ij It may refer to the distance between node i and node j, which may be calculated based on a distance formula; α and β may refer to preset adjustment parameters; It can refer to an exponential function. As an example, if the state information S E If it is normal, As another example, if the state information S E If it is an abnormal state, .

[0072] Optionally, the network topology layer constructs an n*n dimensional topological relationship matrix with the topological connection strength between any two nodes as the matrix element, wherein in the topological relationship matrix, when i=j, the matrix element M ij =1; for a time series consisting of m time points, at each time point t=1,2,...m, the network topology layer updates the value of each matrix element in the topological relationship matrix according to the time series, and obtains the topological connection strength A of any two nodes at time point titj , where A itj Represents the strength of the topological relationship between node i and node j at time point t.

[0073] Optionally, the network topology layer can be based on the topological connection strength A of any two nodes at time point t. itj A three-dimensional topological matrix A is constructed, each node is represented as a vertex of the graph, the non-zero elements in the three-dimensional topological matrix A are used as the weight of the edge, and the fault analysis graph is generated through the minimum spanning tree of the topological relationship strength.

[0074] Exemplarily, the algorithm analysis for the fault analysis diagram is as follows:

[0075]

[0076]

[0077] Among them, Minimize represents the minimization operation, and subject to represents the constraint condition; It means that for all nodes i, ; It means that for all nodes j, ;x ij Used to determine whether nodes i and j are connected by an edge. For example, if nodes i and j are connected by an edge, then x ij = 1. As another example, if nodes i and j are not connected by an edge, then x ij =0.

[0078] The generated fault analysis diagram can be used to indicate the specific location of the fault and the scope of the fault's impact on the low-voltage distribution network. By analyzing the nodes and connection relationships in the diagram, the feeder, distribution equipment or user terminal where the fault is located can be quickly located; and, through the connection relationships in the diagram and operating data such as current and voltage, information such as the power outage area caused by the fault and the number of affected users can be evaluated.

[0079] In some embodiments of the present application, after a fault analysis graph is generated based on a network topology layer, feature mining may be performed on the fault analysis graph generated above based on a feature analysis layer to obtain fault feature data.

[0080] Optionally, the fault feature data may include parameter association features and parameter change patterns between different parameters of each node, as well as parameter frequent item sets of each node. Among them, the parameter association feature may refer to the interconnected characteristics between different parameters of each node. For example, for the power system, there may be a certain association between the mining power parameter and the output power of a node, or there may be a certain association between the voltage parameter and the status information of a node. It can help to judge whether the power system is operating normally by analyzing the aforementioned parameter association features. The parameter association feature in the embodiment of the present application may refer to the fault association feature; the parameter change pattern may refer to the law or style presented by the parameters of each node changing over time or other factors, such as the current parameter change pattern, the temperature parameter change pattern, etc. The aforementioned change pattern can usually reflect the change in the operating state of the power system; the parameter frequent item set may refer to the set of items that frequently appear in the data set. For the parameters of each node, the parameter frequent item set may be expressed as a combination of parameters that appear at the same time. For example, in the records of multiple faults, several specific parameters of certain nodes always appear together. These parameter combinations can constitute parameter frequent item sets. The analysis of parameter frequent item sets helps to quickly locate the key factor combination that may cause the fault.

[0081] For example, the mining of parameter association features can be specifically performed as follows:

[0082] The feature analysis layer can mine the first correlation feature between the power parameters and the output power according to the power parameters of each node in the fault analysis diagram and the output power of the distributed energy. The specific formula can be as follows:

[0083]

[0084] in, may refer to a first correlation feature between a power parameter and output power, can refer to the power parameter of node i, It can refer to the average value of the power parameters of all nodes. It can refer to the average output power of distributed energy over a period of time.

[0085] The feature analysis layer mines the second correlation feature between the voltage parameters and the status information based on the voltage parameters of each node in the fault analysis diagram and the status information of the energy storage device. The specific formula can be as follows:

[0086]

[0087] in, may refer to the second association feature between the voltage parameter and the state information, It represents the status information S of the energy storage device E The entropy of It represents the entropy of all node voltage parameters; Can represent the status information S of the energy storage device E The joint entropy between the voltage parameters of all nodes is used to measure the uncertainty.

[0088] Optional, for entropy The formula can be as follows:

[0089]

[0090] Among them, k represents the state information S E The state dimension, p1 represents the state information S E The probability of being in the first state. Similarly, the entropy can be calculated .

[0091] For entropy The calculation formula is as follows:

[0092]

[0093] Among them, if it satisfies , for the state information S of the first state E ,That .

[0094] That is, the feature analysis layer mining can obtain the first correlation feature between power parameters and output power , and the second correlation feature between voltage parameters and state information .

[0095] Exemplarily, for the mining of parameter change patterns, the feature analysis layer determines the second-order difference in the time series based on the current parameters and temperature parameters of each node in the fault analysis diagram to determine the parameter change pattern. Optionally, the mined parameter change pattern may include the current parameter change pattern and the temperature parameter change pattern, and the specific formula may be as follows:

[0096]

[0097]

[0098] in, Indicates the temperature parameter change mode, Indicates the current parameter change mode, represents the time series, T i represents the temperature parameter of node i, I i represents the current parameter of node i, Indicates the preset interval time.

[0099] Exemplarily, the mining of parameter frequent itemsets may be performed by calculating weighted support based on the feature analysis layer, so as to determine frequent itemsets based on the weighted support.

[0100] Optionally, for each parameter, its importance can be measured by its weight coefficient. In this case, the weighted support of the parameter set can be calculated. Assume that the parameter set of the embodiment of the present application is , for parameter g, its weight coefficient is w g , and satisfies ; At this time, for an item set , its weighted support The calculation formula can be as follows:

[0101]

[0102] Indicator(g,i) represents an indicator function. As an example, if there is valid data of parameter g at node i, then Indicator(g,i)=1; as another example, if there is no valid data of parameter g at the node, then Indicator(g,i)=0.

[0103] Optionally, the existence of valid data of parameter g at node i can be understood as that when the data of parameter g meets all the conditions of data integrity, data accuracy, data validity range, and data timeliness, it is considered that valid data of the parameter exists at the node. As an example, if the data of parameter g at the node has no missing values, it can be considered that it meets the data integrity. For example, for the temperature parameter of the node, if the measuring device works normally and can accurately record and transmit the temperature value of the node, and there is no situation where the temperature data is empty or unrecorded, it can be considered that there is valid data of the temperature parameter at the node. If the temperature measurement device fails and causes the temperature data to be missing at a certain moment, it cannot be considered that there is valid data at that moment. As another example, if the acquired data is accurate and reliable and is not affected by interference, erroneous measurement or other factors that cause data distortion, it can be considered that it meets the data accuracy. For example, when measuring the voltage parameter of the node, the measuring instrument is not affected by electromagnetic interference, and the measurement result can truly reflect the actual voltage situation of the node. The aforementioned voltage data is valid data. If the measuring instrument itself fails or the surrounding environment interferes with the measurement, the measurement is not affected. The voltage value deviates greatly from the actual value, and the data cannot be considered as valid data. As another example, if the data value of parameter g is within a reasonable and meaningful range, it can be considered to meet the validity range of the data. Taking the output power of distributed energy as an example, in actual operation, its output power cannot be negative in certain specific models. If the output power of distributed energy recorded at the node is negative, it means that the data is not within the valid range and cannot be considered as valid data. Or, for the status information of the energy storage device, there is a specific set of values ​​(such as normal, faulty, charging, discharging, etc.). If the recorded status information is not in this set, it is also invalid data. As another example, for some parameters that change with time, if the data is the latest and can reflect the current node status within its specific time requirements, it can be considered to meet the timeliness of the data. For example, in real-time fault diagnosis, the current parameter I of node i i It needs to be updated in time to reflect the current current situation. If the current data obtained is from a long time ago, since the system operating status may have changed, the data cannot meet the current fault diagnosis needs and cannot be regarded as valid data.

[0104] Further, as an example, when When , the item set o can be regarded as a frequent item set; when , item set o is not considered as a frequent item set. It should be noted that According to the actual setting, the embodiments of the present application are not limited to this.

[0105] In one embodiment of the present application, after the fault feature data is obtained by mining based on the feature analysis layer, a fault analysis can be performed on the fault feature data based on the fault analysis layer to obtain the fault area and fault type of the target low-voltage distribution network. Specifically, the fault area analysis can be performed based on the fault analysis layer according to the fault association relationship characteristics and parameter frequent item sets, and the fault area of ​​the target low-voltage distribution network can be output. Then, the fault type analysis can be performed based on the fault analysis layer according to the parameter change pattern, and the fault type of the target low-voltage distribution network can be output.

[0106] Exemplarily, for the analysis process of the fault area, the fault analysis layer can perform weighted calculation on the sum of the first association features, the second association features and the weighted support of all frequent itemsets of all nodes in each area of ​​the target low-voltage distribution network to obtain the fault tendency score of each area in the target low-voltage distribution network. Optionally, if it is determined that the fault tendency score of a certain area is greater than or equal to a preset score, the fault analysis layer can determine the area as the fault area of ​​the target low-voltage distribution network.

[0107] For the fault type analysis process, the fault analysis layer is based on the first parameter change mode in the parameter change mode. and the second parameter change mode , perform fault type analysis with the help of the fault type table, and output the fault type of the target low-voltage distribution network. In one embodiment, the fault type table may refer to an association matching table established according to the parameter change mode and its corresponding fault type, for example, as shown in Table 1:

[0108] Table 1 Fault type table

[0109]

[0110] The embodiment of the present application uses a top-down network topology layer, feature analysis layer and fault analysis layer of the fault diagnosis identification model, combined with the temperature parameters, voltage parameters, current parameters and power parameters of each node, the output power of distributed energy and the status information of energy storage equipment, to comprehensively grasp the real-time status of the low-voltage distribution network and perform layer-by-layer analysis, quickly and accurately identify the fault area and fault type, realize efficient energy scheduling under complex and changeable fault scenarios, and thus improve the adaptability of the low-voltage distribution network.

[0111] Step S302, obtaining fault information of the fault area, performing strategy matching in a preset energy scheduling strategy library based on the fault information and the fault type, and obtaining at least one final candidate energy scheduling planning strategy;

[0112] In some embodiments of the present application, after quickly and accurately identifying the fault area and fault type, a target energy scheduling planning strategy for the fault area can be obtained based on the accurately identified fault area and fault type. The corresponding energy scheduling planning strategy can be obtained in a shorter time under complex and changeable fault scenarios, thereby improving the adaptability and response speed of the low-voltage distribution network.

[0113] In practical applications, fault information of the fault area can be obtained, and strategy matching can be performed in a preset energy scheduling strategy library based on the fault information and fault type to obtain at least one final candidate energy scheduling planning strategy, so as to screen out a target energy scheduling planning strategy from at least one final candidate energy scheduling planning strategy.

[0114] Exemplarily, the fault information may include, but is not limited to, the fault size, fault level, and fault range, etc. Among them, the fault size is the fault area where the fault occurs; the fault level can be used to characterize the importance of the fault area in the target low-voltage distribution network, which can usually be reflected by the distance between the fault area and the load end. The closer the distance, the higher the level. Exemplarily, assuming that the fault level includes 3 levels, the distance between the fault area and the load end is less than or equal to the first preset distance, then the fault level is level 3, the distance between the fault area and the load end is greater than the first preset distance and less than or equal to the second preset distance, then the fault level is level 2, and the distance between the fault area and the load end is greater than the second preset distance, then the fault level is level 1; the fault range can be used to characterize the number of user load cells connected to the fault area in the target low-voltage distribution network.

[0115] The process of strategy matching based on fault information and fault type can be specifically performed as follows: first, strategy matching is performed in a preset energy scheduling strategy library based on the fault type to obtain multiple initial candidate energy scheduling planning strategies; then, a first fault severity coefficient can be determined based on the fault impact coefficient, fault level and fault range, so as to screen out at least one target candidate energy scheduling planning strategy from multiple initial candidate energy scheduling planning strategies based on the first fault severity coefficient.

[0116] The preset energy scheduling strategy library can store a collection of various energy scheduling strategies for dealing with different situations. In practical applications, the applicable fault type can be pre-marked for each energy scheduling planning strategy in the preset energy scheduling strategy library. The energy scheduling end can match strategies in the preset energy scheduling strategy library according to the fault type, obtain all energy scheduling planning strategies applicable to the fault type in the preset energy scheduling strategy library, and obtain multiple initial candidate energy scheduling planning strategies. Exemplarily, assuming that the preset energy scheduling strategy library includes {energy scheduling planning strategy 1, short circuit}, {energy scheduling planning strategy 2, open circuit}, {energy scheduling planning strategy 3, short circuit}, {energy scheduling planning strategy 4, overload}, etc., when the fault type is short circuit, the energy scheduling planning strategies matched in the preset energy scheduling strategy library can be energy scheduling planning strategy 1 and energy scheduling planning strategy 3.

[0117] Optionally, the energy dispatching end can calculate the first fault severity coefficient based on the fault size, fault level and fault range, wherein there is a mapping relationship between the fault size and the fault impact coefficient, the fault level can characterize the importance of the fault area in the target low-voltage distribution network, and the fault range can characterize the number of user load cells connected to the fault area in the target low-voltage distribution network. Specifically, the first fault severity coefficient can be determined based on the fault impact coefficient, the importance of the fault area in the target low-voltage distribution network and the number of user load cells connected to the fault area in the target low-voltage distribution network.

[0118] Among them, the mapping relationship between the fault size and the fault impact coefficient can be reflected based on a preset mapping table. The preset mapping table is an association table constructed in advance according to the mapping relationship between the fault size and its corresponding fault impact coefficient. The energy dispatching end can first match it in the preset mapping table according to the fault size, that is, obtain the fault impact coefficient corresponding to the fault size according to the range of the fault size, and then use the fault impact coefficient obtained by the mapped match to determine the first fault severity coefficient.

[0119] For example, assuming that the preset mapping table is {fault size is greater than 10, fault impact coefficient is 10}, {fault size is (5,10], fault impact coefficient is 8}, {fault size is (1,5], fault impact coefficient is 5}, {fault size is less than 1, fault impact coefficient is 1}, when the fault size is 4, the fault impact coefficient matched in the preset mapping table can be the fault impact coefficient 5.

[0120] Optionally, the calculation formula of the first fault severity coefficient may be as follows:

[0121]

[0122] in, represents the first fault severity coefficient, represents the fault influence coefficient, Indicates the fault level; Indicates the fault range, that is, the number of user load cells connected to the fault area.

[0123] It should be noted that each energy scheduling planning strategy in the embodiment of the present application is set with its applicable fault severity coefficient. The energy scheduling end can screen among multiple initial candidate energy scheduling planning strategies according to the first fault severity coefficient, and screen out the energy scheduling planning strategy whose fault severity coefficient is greater than or equal to the first fault severity coefficient as the target candidate energy scheduling planning strategy.

[0124] In some embodiments of the present application, the at least one target candidate energy scheduling planning strategy obtained by screening may include a first energy scheduling planning strategy having the same fault severity coefficient as the first fault severity system, and at least one final candidate energy scheduling planning strategy may be obtained based on the first energy scheduling planning strategy. Optionally, at least one target candidate energy scheduling planning strategy may also include a second energy scheduling planning strategy, and the second energy scheduling planning strategy may refer to a strategy other than the first energy scheduling planning strategy in at least one target candidate energy scheduling planning strategy.

[0125] In an embodiment of the present application, the first energy scheduling planning strategy can be taken as the center to determine the strategy similarity and / or strategy correlation between the first energy scheduling planning strategy and each second energy scheduling planning strategy, and then based on the strategy similarity and / or strategy correlation, at least one final candidate energy scheduling planning strategy among the target candidate energy scheduling planning strategies can be determined.

[0126] Optionally, the calculation of policy similarity and / or policy relevance may be implemented through vector calculation.

[0127] For example, assuming that the first strategy vector of the first energy scheduling planning strategy is , the second strategy vector of the second energy scheduling planning strategy is , where N represents the vector dimension, and the specific calculation formula of strategy similarity can be shown as follows:

[0128]

[0129] in, Represents the first energy scheduling planning strategy T1 and the jth second energy scheduling planning strategy Z 2j The strategy similarity between ,when When , it means the first energy scheduling planning strategy T1 and the jth second energy scheduling planning strategy Z 2j Completely similar; at that time , represents the first energy scheduling planning strategy T1 and the jth second energy scheduling planning strategy Z 2j Quite the opposite; when When , it means the first energy scheduling planning strategy T1 and the jth second energy scheduling planning strategy Z 2j There is no obvious similarity. Represents the first energy scheduling planning strategy T1 and the jth second energy scheduling planning strategy Z 2j The energy interaction between i Represents the first strategy vector The i-th policy vector in z ji Represents the second strategy vector The i-th policy vector in .

[0130] The specific calculation formula of strategy relevance can be shown as follows:

[0131]

[0132]

[0133]

[0134] in, Represents the first energy scheduling planning strategy T1 and the jth second energy scheduling planning strategy Z 2j The strategic relevance between , The larger the value, the better the first energy scheduling planning strategy T1 and the jth second energy scheduling planning strategy Z 2j The stronger the correlation between them. Represents the first energy scheduling planning strategy T1 and the jth second energy scheduling planning strategy Z 2j The vector projection between Represents the first strategy vector In the second strategy vector The projection vector on Represents a modulo operation.

[0135] In some embodiments of the present application, after calculating the strategy similarity and strategy correlation between the first energy scheduling planning strategy and each second energy scheduling planning strategy, the final correlation coefficient between the first energy scheduling planning strategy and each second energy scheduling planning strategy can be calculated to screen at least one final candidate energy scheduling planning strategy from the target candidate energy scheduling planning strategy based on the final correlation coefficient.

[0136] For example, the specific calculation formula for the final correlation coefficient can be as follows:

[0137]

[0138] in, Represents the first energy scheduling planning strategy T1 and the jth second energy scheduling planning strategy Z 2j The final correlation coefficient between Represents the preset coefficient, which is generally set to .

[0139] Optionally, after obtaining the final correlation coefficient between the first energy scheduling planning strategy and each second energy scheduling planning strategy, the energy scheduling end can traverse the final correlation coefficient between the first energy scheduling planning strategy and each second energy scheduling planning strategy, and determine the second energy scheduling planning strategy and the first energy scheduling planning strategy whose final correlation coefficient is greater than or equal to the preset correlation coefficient as multiple final candidate energy scheduling planning strategies in the target candidate energy scheduling planning strategy. It should be noted that the preset correlation coefficient can be set based on actual needs, and the embodiment of the present application is not limited to this.

[0140] Step S303, obtaining an evaluation index of the target low-voltage distribution network, and performing strategy matching in at least one final candidate energy scheduling planning strategy based on the evaluation index to obtain a target energy scheduling planning strategy;

[0141] In some embodiments of the present application, after screening and obtaining at least one final candidate energy scheduling planning strategy, a target energy scheduling planning strategy to be executed by the energy scheduling end can be matched from the at least one final candidate energy scheduling planning strategy based on an evaluation indicator.

[0142] Exemplarily, the energy scheduling end can obtain the historical energy scheduling data of each final candidate energy scheduling planning strategy based on the evaluation index, and then perform energy scheduling prediction for each final candidate energy scheduling planning strategy based on the preset target optimization function and the historical energy scheduling data to obtain the comprehensive scheduling coefficient of each final candidate energy scheduling planning strategy, and then based on each comprehensive scheduling coefficient, perform strategy matching in at least one final candidate energy scheduling planning strategy to obtain the target energy scheduling planning strategy.

[0143] Among them, the evaluation indicators may include but are not limited to power supply restoration time, energy utilization efficiency, scheduling cost and equipment performance parameters. The historical energy scheduling data obtained based on the evaluation indicators may include the historical recovery time, historical utilization efficiency, equipment operation and maintenance cost and energy conversion equipment performance parameters of each final candidate energy scheduling planning strategy. The historical recovery time is in hours, and the energy conversion equipment performance parameters are determined according to the operating status of the energy conversion equipment, and the values ​​are 1 to 100.

[0144] Optionally, the specific calculation formula of the comprehensive scheduling coefficient can be as follows:

[0145]

[0146] in, represents the comprehensive scheduling coefficient of the i-th final candidate energy scheduling planning strategy, represents the historical utilization efficiency of the i-th final candidate energy scheduling planning strategy, represents the historical recovery time of the i-th final candidate energy scheduling planning strategy, represents the equipment operation and maintenance cost of the i-th final candidate energy scheduling planning strategy, Represents the performance parameters of the energy conversion equipment of the i-th final candidate energy scheduling planning strategy.

[0147] In practical applications, the energy dispatching end can select at least one initial energy dispatching planning strategy from multiple final candidate energy dispatching planning strategies based on the comprehensive dispatching coefficient. Specifically, it can be manifested as traversing the comprehensive dispatching coefficients of each final candidate energy dispatching planning strategy, and determining the energy dispatching planning strategy whose comprehensive dispatching coefficient is greater than or equal to the preset dispatching coefficient among the multiple final candidate energy dispatching planning strategies as at least one initial energy dispatching planning strategy; then, the predicted load of the user load cell connected to the fault area at the current time can be obtained, and the target energy dispatching planning strategy can be determined based on the predicted load and the available capacity of the energy supply source of each initial energy dispatching planning strategy.

[0148] The predicted load amount can be predicted based on the historical load curve of the user load cell connected to the fault area, which can be specifically expressed as obtaining the historical load amount of the user load cell connected to the fault area at the current time as the predicted load amount of the user load cell connected to the fault area at the current time. In some embodiments of the present application, the energy dispatching end can determine the initial energy dispatching planning strategy in which the available capacity of the energy supply source is greater than the predicted load amount and the available capacity of the energy supply source is the smallest as the target energy dispatching planning strategy.

[0149] In one embodiment, it is assumed that there are multiple initial energy scheduling planning strategies, for example, including {initial energy scheduling planning strategy 1, energy supply source available capacity 100}, {initial energy scheduling planning strategy 2, energy supply source available capacity 110}, {initial energy scheduling planning strategy 3, energy supply source available capacity 150}, and {initial energy scheduling planning strategy 4, energy supply source available capacity 80}. Assuming that the current predicted load is 100, at this time, filtering can be performed according to the predicted load 100 to obtain initial energy scheduling planning strategy 2 and initial energy scheduling planning strategy 3. The energy supply source available capacity of initial energy scheduling planning strategy 2 is the smallest. Therefore, initial energy scheduling planning strategy 2 can be determined as the target energy scheduling planning strategy.

[0150] The embodiment of the present application matches the target energy scheduling planning strategy for the fault area according to the historical recovery time, historical utilization efficiency, equipment operation and maintenance cost and performance parameters of the energy conversion equipment of the energy scheduling planning strategy. The scheduling planning strategy can be matched in a relatively short time, reducing the amount of calculation and calculation time, and improving the response speed of the low-voltage distribution network, thereby ensuring the reliable and stable operation of the low-voltage distribution network.

[0151] Step S304, receiving load feedback information from the user load end after executing the target energy scheduling planning strategy;

[0152] Step S305, constructing a strategy optimization function based on actual load information;

[0153] In some embodiments of the present application, the energy dispatching end executes a target energy dispatching planning strategy, and transmits energy to user load cells connected to the fault area according to the aforementioned strategy. In order to further optimize the energy dispatching planning strategy and further improve the adaptability of the low-voltage distribution network, the energy dispatching end can receive load feedback information fed back by the user load end of the user load cell, so as to optimize the executed target energy dispatching planning strategy based on the fed-back load feedback information.

[0154] Optimizing the executed target energy scheduling planning strategy depends on the strategy optimization function. In practical applications, the strategy optimization function can be constructed based on load feedback information, so as to achieve strategy optimization based on the constructed strategy optimization function.

[0155] Optionally, the load feedback information may include actual load information, load change trend information, power supply feedback information, etc. Specifically, it can be manifested as determining the energy supply deviation value based on the actual load information and the available capacity of the energy supply source of the target energy scheduling planning strategy, and determining the degree of difference in the energy change trend based on the load change trend information and the energy change trend information of the target energy scheduling planning strategy, and then constructing a strategy optimization function with the energy supply deviation value, the degree of difference in the energy change trend and the power supply feedback information.

[0156] For example, assuming that the actual load information is , where M represents the information dimension, represents the actual load value at different times; the available capacity of the energy supply source of the target energy scheduling planning strategy is , where c i Represents the available capacity of the energy supply source at the corresponding moment. The specific calculation formula for the energy supply deviation value can be shown as follows:

[0157]

[0158] in, Indicates the energy supply deviation value.

[0159] Energy change trend information represents the energy supply per unit time. Assume that the load change trend information is expressed as ,in, represents the change of load in the i-th time interval; the energy change trend information of the target energy scheduling planning strategy is expressed as ,in, It represents the change of the corresponding energy supply in the i-th time interval. The specific formula for the difference in energy change trend can be shown as follows:

[0160]

[0161] in, Indicates the degree of difference in energy change trends.

[0162] In the process of constructing the strategy optimization function, it is assumed that the power supply feedback information includes the first frequency of voltage instability , the second frequency of current frequency fluctuation occurs , the first amplitude of voltage sag , the second amplitude of voltage rise and harmonic content , based on the energy supply deviation value , Differences in energy trends , first frequency , second frequency , first amplitude , the second amplitude and harmonic content Constructed strategy optimization function It can be expressed as: .

[0163] In the above strategy optimization function, various factors can be combined together through different nonlinear functions. For example, the energy supply deviation value can be Taking the square, the difference in energy change trend Take the cube, so that the energy supply deviation value Differences in energy trends The contribution to the optimization function is more nonlinear; for voltage-related parameters, the second amplitude of the voltage swell can be converted to The first magnitude of the voltage sag For the second frequency of the current frequency fluctuation , and the energy supply deviation value The natural logarithm of The inverse tangent function can be and The sum of is related to reflect its comprehensive relationship with energy supply deviation and trend difference. This embodiment of the present application is not limited to this.

[0164] Step S306, optimizing the target energy scheduling planning strategy with the power supply feedback information in the strategy optimization function as the target, and obtaining an optimized target energy scheduling planning strategy;

[0165] In some embodiments of the present application, the optimization of the target energy scheduling planning strategy can be expressed as optimizing the strategy parameters in the target energy scheduling planning strategy with the power supply feedback information in the minimization strategy optimization function as the goal, and obtaining the optimized target energy scheduling planning strategy. Specifically, it can be expressed as adjusting the energy supply per unit time in the target energy scheduling planning strategy, as well as the energy supply, current frequency and harmonic content in the target energy scheduling planning strategy with the first frequency, the second frequency, the first amplitude, the second amplitude and the harmonic content in the minimization strategy optimization function as the goal, and obtaining the optimized target energy scheduling planning strategy, that is, the optimal energy scheduling planning strategy.

[0166] Optionally, the simulated annealing algorithm can be combined for analysis. Assume that the energy supply per unit time in the target energy scheduling planning strategy is expressed as , the energy supply is , the current frequency is , the harmonic content is , the analysis process can be expressed as follows: First, step 1 can be expressed as performing initialization operations, such as setting the initial temperature to , the initial solution is , the energy supply is , the current frequency is The harmonic content is ; Then step 2 can be iterated, for the current temperature , yielding a new solution , new energy supply , the new current frequency and new harmonic content , obtained by making a small perturbation on the current solution; at this time, step 3 can calculate the energy difference , step 4 can be expressed as if , then accept the new solution if , then with probability Accept the new solution; step 5 can be represented by lowering the temperature ,in, is a cooling factor, usually between 0 and 1. In this example, steps 2 to 5 can be repeated until the termination condition is met. The termination condition can be, for example, that the temperature is lower than a preset threshold or that the new solution is not accepted for multiple consecutive times. It should be noted that the accepted new solution is the optimized target energy scheduling planning strategy, that is, the optimal energy scheduling planning strategy, which is not limited in the embodiments of the present application.

[0167] Step S307, executing the optimized target energy scheduling planning strategy.

[0168] When the distribution network planning system is in the operating environment of the low-voltage distribution network, after the energy dispatching end has executed the target energy dispatching planning strategy and optimized the executed target energy dispatching planning strategy, the energy dispatching end can execute the optimized target energy dispatching planning strategy to overcome the regional failures that occur in the target low-voltage distribution network when a new distribution transformer is built in the target low-voltage distribution network. While improving the response speed of the low-voltage distribution network, the adaptability of the low-voltage distribution network is further improved, which is more conducive to ensuring the reliable and stable operation of the low-voltage distribution network.

[0169] In an embodiment of the present application, a fault diagnosis and identification model is combined with the operating data of the target low-voltage distribution network, and the real-time status of the low-voltage distribution network is comprehensively analyzed layer by layer through the network topology layer, the feature analysis layer and the fault analysis layer, and the fault area and fault type are quickly and accurately identified. Based on the accurately identified fault area and fault type, the target energy scheduling planning strategy of the fault area is obtained, and the corresponding energy scheduling planning strategy can be obtained in a shorter time under complex and changeable fault scenarios, thereby improving the adaptability of the low-voltage distribution network and the response speed of the low-voltage distribution network; and, the energy scheduling planning strategy can also be continuously optimized through the load feedback information of the user load end, and the power supply feedback information in the strategy optimization function constructed by the load feedback information is used as the target, and the target energy scheduling planning strategy is optimized, and the optimized target energy scheduling planning strategy is executed. While improving the response speed of the low-voltage distribution network, the adaptability of the low-voltage distribution network is further improved, which is more conducive to ensuring the reliable and stable operation of the low-voltage distribution network.

[0170] It should be noted that, for the method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present application are not limited by the described order of actions, because according to the embodiments of the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present application.

[0171] Reference Figure 4, shows a structural block diagram of an energy dispatching device for a low-voltage distribution network provided in an embodiment of the present application, which is applied to an energy dispatching terminal. The energy dispatching terminal has a fault diagnosis and identification model, which may specifically include the following modules:

[0172] An operation data acquisition module 401 is used to acquire the operation data of the target low-voltage distribution network;

[0173] A fault diagnosis module 402 is used to respond to an input instruction of inputting the operation data of the target low-voltage distribution network into the fault diagnosis identification model, and obtain the fault area and fault type of the target low-voltage distribution network; wherein the fault diagnosis identification model includes a network topology layer, a feature analysis layer and a fault analysis layer, the fault area and fault type are obtained based on the fault analysis layer performing a fault analysis on the fault feature data, the fault feature data are obtained based on the feature mining of the fault analysis graph by the feature analysis layer, and the fault analysis graph is generated based on the topological analysis of the operation data by the network topology layer;

[0174] A planning strategy generation module 403 is used to obtain a target energy scheduling planning strategy according to the fault area and the fault type;

[0175] Feedback information receiving module 404, used to receive load feedback information from the user load end after executing the target energy scheduling planning strategy; the load feedback information is used to construct a strategy optimization function;

[0176] The energy scheduling module 405 is used to optimize the target energy scheduling planning strategy with the power supply feedback information in the strategy optimization function as the target, obtain the optimized target energy scheduling planning strategy and execute the optimized target energy scheduling planning strategy.

[0177] In one embodiment of the present application, the planning strategy generation module 403 may include the following submodules:

[0178] The planning strategy generation submodule is used to obtain the fault information of the fault area, perform strategy matching in a preset energy scheduling strategy library based on the fault information and the fault type, and obtain at least one final candidate energy scheduling planning strategy; obtain the evaluation index of the target low-voltage distribution network, perform strategy matching in at least one final candidate energy scheduling planning strategy based on the evaluation index, and obtain the target energy scheduling planning strategy.

[0179] In one embodiment of the present application, the fault information includes fault size, fault level and fault range, the fault level represents the importance of the fault area in the target low-voltage distribution network, and the fault range represents the number of user load cells connected to the fault area in the target low-voltage distribution network;

[0180] The planning strategy generation submodule may include the following units:

[0181] A candidate planning strategy acquisition unit is used to perform strategy matching in the preset energy scheduling strategy library based on the fault type to obtain multiple initial candidate energy scheduling planning strategies; determine a first fault severity coefficient based on the fault size, the importance of the fault area in the target low-voltage distribution network, and the number of user load cells connected to the fault area in the target low-voltage distribution network; based on the first fault severity coefficient, screen out at least one target candidate energy scheduling planning strategy from multiple initial candidate energy scheduling planning strategies; the at least one target candidate energy scheduling planning strategy includes a first energy scheduling planning strategy whose fault severity coefficient is the same as the first fault severity coefficient; based on the first energy scheduling planning strategy, obtain at least one final candidate energy scheduling planning strategy.

[0182] In one embodiment of the present application, the candidate planning strategy acquisition unit may include the following subunits:

[0183] The candidate planning strategy acquisition subunit is used to determine the strategy similarity and / or strategy correlation between the first energy scheduling planning strategy and each second energy scheduling planning strategy with the first energy scheduling planning strategy as the center; the second energy scheduling planning strategy is the strategy of the at least one target candidate energy scheduling planning strategy except the first energy scheduling planning strategy; based on the strategy similarity and / or the strategy correlation, determine at least one final candidate energy scheduling planning strategy among the target candidate energy scheduling planning strategies.

[0184] In one embodiment of the present application, the planning strategy generation submodule may include the following units:

[0185] A planning strategy generation unit is used to obtain the historical energy scheduling data of each final candidate energy scheduling planning strategy based on the evaluation index; perform energy scheduling prediction on each final candidate energy scheduling planning strategy based on a preset target optimization function and the historical energy scheduling data to obtain a comprehensive scheduling coefficient of each final candidate energy scheduling planning strategy; screen out at least one initial energy scheduling planning strategy from multiple final candidate energy scheduling planning strategies based on the comprehensive scheduling coefficient; obtain the predicted load of the user load cell connected to the fault area at the current time; and determine the target energy scheduling planning strategy based on the predicted load and the available capacity of the energy supply source of each initial energy scheduling planning strategy.

[0186] In one embodiment of the present application, the load feedback information includes actual load information, load change trend information and power supply feedback information; after the load feedback information at the user load end, the device provided in the embodiment of the present application may further include the following modules:

[0187] A strategy optimization function construction module is used to determine the energy supply deviation value based on the actual load information and the available capacity of the energy supply source of the target energy scheduling planning strategy; determine the degree of difference in energy change trends based on the load change trend information and the energy change trend information of the target energy scheduling planning strategy; and construct a strategy optimization function with the energy supply deviation value, the degree of difference in energy change trends and the power supply feedback information.

[0188] In one embodiment of the present application, the energy scheduling module 405 may include the following submodules:

[0189] The energy scheduling submodule is used to optimize the strategy parameters in the target energy scheduling planning strategy with the goal of minimizing the power supply feedback information in the strategy optimization function to obtain the optimized target energy scheduling planning strategy.

[0190] In an embodiment of the present application, a fault diagnosis and identification model is combined with the operating data of the target low-voltage distribution network, and the real-time status of the low-voltage distribution network is comprehensively analyzed layer by layer through the network topology layer, the feature analysis layer and the fault analysis layer, and the fault area and fault type are quickly and accurately identified. Based on the accurately identified fault area and fault type, the target energy scheduling planning strategy of the fault area is obtained, and the corresponding energy scheduling planning strategy can be obtained in a shorter time under complex and changeable fault scenarios, thereby improving the adaptability of the low-voltage distribution network and the response speed of the low-voltage distribution network; and, the energy scheduling planning strategy can also be continuously optimized through the load feedback information of the user load end, and the power supply feedback information in the strategy optimization function constructed by the load feedback information is used as the target, and the target energy scheduling planning strategy is optimized, and the optimized target energy scheduling planning strategy is executed. While improving the response speed of the low-voltage distribution network, the adaptability of the low-voltage distribution network is further improved, which is more conducive to ensuring the reliable and stable operation of the low-voltage distribution network.

[0191] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0192] The present application also provides an electronic device, referring to Figure 5 The provided electronic device 500 includes a memory 510, a processor 520, and a computer program 511 stored in the memory 510 and capable of running on the processor 520. When the computer program 511 is executed by the processor, the various processes of the above-mentioned low-voltage distribution network energy scheduling method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0193] The present application also provides a computer-readable storage medium. Figure 6 The computer readable storage medium 600 provided stores a computer program 511. When the computer program 511 is executed by the processor, the various processes of the above-mentioned energy scheduling method embodiment of the low-voltage distribution network are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0194] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0195] It should be noted that the terms "first", "second", etc. in the specification and claims of the embodiments of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described here can be implemented in an order other than the content illustrated or described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device including a series of steps or modules need not be limited to those steps or modules clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices. The division of the modules that appear in the embodiments of the present application is only a logical division. There may be other division methods when implemented in practical applications, such as multiple modules can be combined or integrated into another system, or some features can be ignored, or not executed. In addition, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, and the indirect coupling or communication connection between modules can be electrical or other similar forms, which are not limited in the embodiments of the present application. Furthermore, the modules or sub-modules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed in multiple circuit modules, and some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiment of the present application.

[0196] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0197] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0198] In the several embodiments provided in the embodiments of the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0199] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0200] In addition, each functional module in each embodiment of the present application can be integrated into a processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0201] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product.

[0202] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, a computer, a server, or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server, or data center. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0203] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0204] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded into a computer or other programmable data processing terminal device, so that a series of operation steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable terminal device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0205] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present application.

[0206] Finally, it should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0207] The technical solutions provided in the embodiments of the present application are introduced in detail above. The principles and implementation methods of the embodiments of the present application are explained by using specific examples in the embodiments of the present application. The description of the above embodiments is only used to help understand the methods and core ideas of the embodiments of the present application. At the same time, for those skilled in the art, according to the ideas of the embodiments of the present application, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as a limitation on the embodiments of the present application.

Claims

1. A method for dispatching energy in a low voltage distribution network, characterized in that: Involving an energy dispatching end, the energy dispatching end has a fault diagnosis and identification model, and the method includes: Obtain the operating data of the target low-voltage distribution network; In response to an input instruction of inputting the operation data of the target low-voltage distribution network into the fault diagnosis and identification model, the fault area and fault type of the target low-voltage distribution network are obtained; wherein the fault diagnosis and identification model includes a network topology layer, a feature analysis layer and a fault analysis layer, the fault area and fault type are obtained based on a fault analysis of the fault feature data by the fault analysis layer, the fault feature data are obtained based on feature mining of a fault analysis graph by the feature analysis layer, and the fault analysis graph is generated based on a topological analysis of the operation data by the network topology layer; Obtaining a target energy scheduling planning strategy according to the fault area and the fault type; Receiving load feedback information from a user load end after executing the target energy scheduling planning strategy; the load feedback information is used to construct a strategy optimization function; The target energy scheduling planning strategy is optimized with the power supply feedback information in the strategy optimization function as the target, an optimized target energy scheduling planning strategy is obtained and the optimized target energy scheduling planning strategy is executed.

2. The method according to claim 1, characterized in that The obtaining of a target energy scheduling planning strategy according to the fault area and the fault type includes: Acquire fault information of the fault area, and perform strategy matching in a preset energy scheduling strategy library based on the fault information and the fault type to obtain at least one final candidate energy scheduling planning strategy; An evaluation index of the target low-voltage distribution network is obtained, and a strategy is matched among at least one final candidate energy scheduling planning strategy based on the evaluation index to obtain a target energy scheduling planning strategy.

3. The method according to claim 2, characterized in that The fault information includes fault size, fault level and fault range, wherein the fault level represents the importance of the fault area in the target low-voltage distribution network, and the fault range represents the number of user load cells connected to the fault area in the target low-voltage distribution network; The performing strategy matching in a preset energy scheduling strategy library based on the fault information and the fault type to obtain at least one final candidate energy scheduling planning strategy includes: Based on the fault type, strategy matching is performed in the preset energy scheduling strategy library to obtain multiple initial candidate energy scheduling planning strategies; Determining a first fault severity coefficient based on the fault size, the importance of the fault area in the target low-voltage distribution network, and the number of user load cells connected to the fault area in the target low-voltage distribution network; Based on the first fault severity coefficient, at least one target candidate energy scheduling planning strategy is screened out from a plurality of initial candidate energy scheduling planning strategies; the at least one target candidate energy scheduling planning strategy includes a first energy scheduling planning strategy having a fault severity coefficient that is the same as the first fault severity coefficient; Based on the first energy scheduling planning strategy, at least one final candidate energy scheduling planning strategy is obtained.

4. The method according to claim 3, characterized in that The obtaining, based on the first energy scheduling planning strategy, at least one final candidate energy scheduling planning strategy comprises: Taking the first energy scheduling planning strategy as the center, determining the strategy similarity and / or strategy correlation between the first energy scheduling planning strategy and each second energy scheduling planning strategy; the second energy scheduling planning strategy is a strategy other than the first energy scheduling planning strategy in the at least one target candidate energy scheduling planning strategy; At least one final candidate energy scheduling planning strategy among the target candidate energy scheduling planning strategies is determined based on the strategy similarity and / or the strategy relevance.

5. The method according to claim 2, characterized in that: The step of performing strategy matching in at least one final candidate energy scheduling planning strategy based on the evaluation index to obtain a target energy scheduling planning strategy includes: Acquire historical energy dispatch data of each final candidate energy dispatch planning strategy based on the evaluation index; Based on the preset target optimization function and the historical energy scheduling data, energy scheduling prediction is performed on each final candidate energy scheduling planning strategy to obtain a comprehensive scheduling coefficient of each final candidate energy scheduling planning strategy; Selecting at least one initial energy scheduling planning strategy from a plurality of final candidate energy scheduling planning strategies based on the comprehensive scheduling coefficient; Obtaining the predicted load amount of the user load cell connected to the fault area at the current time; The target energy scheduling planning strategy is determined based on the predicted load and the available capacity of the energy supply source of each initial energy scheduling planning strategy.

6. The method according to claim 1, characterized in that The load feedback information includes actual load information, load change trend information and power supply feedback information; After the load feedback information of the user load end, the method further includes: Determining an energy supply deviation value based on the actual load information and the available capacity of the energy supply source of the target energy scheduling planning strategy; Determining the degree of difference in energy change trends based on the load change trend information and the energy change trend information of the target energy scheduling planning strategy; A strategy optimization function is constructed based on the energy supply deviation value, the degree of difference in the energy change trend and the power supply feedback information.

7. The method according to claim 6, characterized in that The target energy scheduling planning strategy is optimized with the power supply feedback information in the strategy optimization function as the target to obtain the optimized target energy scheduling planning strategy, including: The strategy parameters in the target energy scheduling planning strategy are optimized with the goal of minimizing the power supply feedback information in the strategy optimization function to obtain an optimized target energy scheduling planning strategy.

8. An energy dispatching device for a low voltage distribution network, characterized in that: Applied to an energy dispatching terminal, the energy dispatching terminal has a fault diagnosis and identification model, and the device includes: An operation data acquisition module is used to acquire the operation data of the target low-voltage distribution network; A fault diagnosis module, for responding to an input instruction of inputting the operation data of the target low-voltage distribution network into the fault diagnosis identification model, to obtain the fault area and fault type of the target low-voltage distribution network; wherein the fault diagnosis identification model comprises a network topology layer, a feature analysis layer and a fault analysis layer, the fault area and fault type are obtained based on a fault analysis of the fault feature data by the fault analysis layer, the fault feature data are obtained based on feature mining of a fault analysis graph by the feature analysis layer, and the fault analysis graph is generated based on a topological analysis of the operation data by the network topology layer; A planning strategy generation module, used to obtain a target energy scheduling planning strategy according to the fault area and the fault type; A feedback information receiving module, used to receive load feedback information from a user load end after executing the target energy scheduling planning strategy; the load feedback information is used to construct a strategy optimization function; The energy scheduling module is used to optimize the target energy scheduling planning strategy with the power supply feedback information in the strategy optimization function as the target, obtain the optimized target energy scheduling planning strategy and execute the optimized target energy scheduling planning strategy.

9. An electronic device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein when the computer program is executed by the processor, the energy dispatching method for a low-voltage distribution network as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the energy scheduling method for a low-voltage distribution network as described in any one of claims 1 to 7 is implemented.

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