Method, system and equipment for monitoring weak link of power distribution network and storage medium

Abstract

The invention discloses a method, a system and equipment for monitoring weak links of a power distribution network and a storage medium. Comprises: obtaining operation related data of a target power distribution network; performing preliminary state evaluation on each node of the target power distribution network based on the operation related data to generate a preliminary state matrix; and weak link positioning is carried out based on the initial state matrix to obtain weak link data, and an emergency response strategy of the target power distribution network is generated according to the weak link data. Weak link positioning is performed through the initial state matrix to obtain weak link data, so that the part with problems in the power distribution network can be quickly and accurately found out, and the problem finding efficiency is improved. The emergency response strategy is generated according to the weak link data, so that effective countermeasures can be taken quickly when the power distribution network breaks down or is abnormal, the overall operation reliability of the power distribution network is improved, and the stability of power supply is guaranteed. Changes of the power distribution network can be dynamically tracked, and continuously changing power grid operation environments and requirements are adapted.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution networks, and in particular to a monitoring method, system, equipment and storage medium for weak links in a power distribution network. Background Art

[0002] In modern power systems, the distribution network is an important link between the transmission network and user terminals. Its stable operation is crucial to ensuring the quality and reliability of power supply. With the rapid development of social economy, the demand for electricity continues to grow, the scale of the distribution network continues to expand, and the structure is becoming increasingly complex. In the face of such complex and changing situations, traditional distribution network monitoring methods have gradually exposed many shortcomings.

[0003] Existing distribution network monitoring methods mostly rely on traditional monitoring methods and lack in-depth analysis and processing of real-time data. As a result, the assessment of the health status of the distribution network often lags behind the occurrence of actual problems, making it difficult to provide timely warnings and interventions. Traditional methods have limited data processing capabilities and cannot comprehensively consider multi-dimensional operating data, resulting in incomplete and inaccurate identification of weak links. In addition, the lack of an intelligent decision-making mechanism for weak links makes it difficult to quickly formulate emergency response strategies, affecting the overall safety and stability of the distribution network. Summary of the invention

[0004] The present invention provides a monitoring method, system, device and storage medium for weak links in a distribution network, which solves the technical problem that the evaluation of the health status of the traditional distribution network often lags behind the occurrence of actual problems, making it difficult to achieve timely warning and intervention.

[0005] According to one aspect of the present invention, a method for monitoring weak links in a distribution network is provided, the method comprising:

[0006] Obtaining operation-related data of the target distribution network;

[0007] Performing a preliminary state assessment on each node of the target distribution network based on the operation-related data to generate a preliminary state matrix;

[0008] Based on the preliminary state matrix, weak links are located to obtain weak link data, and emergency response strategies for the target distribution network are generated according to the weak link data.

[0009] Optionally, obtaining operation-related data of the target distribution network includes: collecting data from each node of the target distribution network through a distributed sensor network to obtain original operation data, wherein the original operation data includes voltage data, current data, temperature data and load rate data; performing wavelet transform denoising on the original operation data to generate wavelet transform denoised data; performing time series frequency change identification on the wavelet transform denoised data to generate a frequency change identification result; when the frequency change identification result is not within a preset frequency change range, generating time series frequency change data according to the frequency change identification result, and adaptively sampling the time series frequency change data to obtain adaptive sampling data; when the frequency change identification result is within a preset frequency change range, generating data to be reconstructed according to the frequency change identification result; reconstructing the adaptive sampling data and the data to be reconstructed to obtain reconstructed data; and standardizing the reconstructed data to generate operation-related data.

[0010] Optionally, a preliminary state assessment is performed on each node of the target distribution network based on the operation-related data to generate a preliminary state matrix, including: performing spectral analysis on the operation-related data to generate a frequency domain feature set; performing frequency response analysis on each node of the target distribution network based on the frequency domain feature set to obtain a frequency response matrix; performing singular value decomposition on the frequency response matrix to obtain a reduced-dimensional feature vector set; performing trend analysis on the reduced-dimensional feature vector set to obtain trend analysis results; using a preset long short-term memory network to perform similarity prediction and matching on the trend analysis results to obtain a similarity matrix; performing state association analysis on each node of the target distribution network based on the similarity matrix to obtain a state association matrix; using a Bayesian network to estimate the health state probability of each node in the target distribution network based on the state association matrix to obtain a state assessment result corresponding to the degree of each node; and constructing a preliminary state matrix according to the state assessment results corresponding to each node.

[0011] Optionally, weak link positioning is performed based on the preliminary state matrix to obtain weak link data, including: performing structured processing on the preliminary state matrix through a preset topology analysis algorithm to obtain a topology structure matrix of the target distribution network, wherein the topology structure matrix includes the connection relationship between each node, the branch impedance parameters corresponding to each node, and the electrical characteristics of each node; performing network modeling on the target distribution network based on the topology structure matrix to obtain a network feature vector; performing weak link analysis on the target distribution network according to the network feature vector to obtain a node vulnerability index; performing a comprehensive assessment on each node of the target distribution network based on the node vulnerability index to obtain a risk score for each node; performing spatial clustering analysis on the target distribution network according to the risk score of each node through a self-organizing mapping neural network algorithm to obtain a risk hotspot map; determining the location of the weak link in the risk hotspot map through a preset risk assessment algorithm, and performing weak link monitoring at the weak link location to obtain weak link data.

[0012] Optionally, a spatial clustering analysis is performed on the target distribution network according to the risk score of each node through a self-organizing map neural network algorithm to obtain a risk hotspot map, including: performing nonlinear dimensionality reduction mapping on the node risk score through a self-organizing map neural network algorithm to obtain the risk distribution of each node on the mapping grid; using a kernel density estimation method to calculate the relative risk density of each node in the corresponding neighborhood based on the risk distribution of each node to obtain a risk density map of each node; performing local in-depth analysis on the node density distribution in the risk density map of each node to obtain a preliminary risk area division map, wherein the preliminary risk area division map is used to distinguish different risk level areas in the target distribution network; and optimizing the risk area boundaries of the preliminary risk area division map to obtain a risk hotspot map.

[0013] Optionally, an emergency response strategy for the target distribution network is generated based on the weak link data, including: determining the weak link type corresponding to the weak link data; performing semantic analysis on the weak link type to obtain a semantic feature vector; screening historical emergency strategies in a preset database based on the semantic feature vector to obtain a candidate strategy set; and generating an emergency response strategy based on the candidate strategy set.

[0014] Optionally, determining the weak link type corresponding to the weak link data includes: extracting features from the weak link data to obtain a weak link vector, wherein the weak link vector includes a voltage fluctuation vector, a harmonic content vector, a power factor vector and a load imbalance vector; generating a time-frequency feature vector according to the weak link vector, and performing nonlinear dynamic modeling on the time-frequency feature vector to obtain a dynamic modeling vector; performing fault mode identification on the weak link position based on the dynamic modeling vector through a preset fuzzy inference algorithm to obtain a fault mode identification result; and dynamically classifying the fault mode identification result to obtain a weak link type.

[0015] According to another aspect of the present invention, a monitoring system for weak links in a distribution network is provided, the system comprising:

[0016] An operation-related data acquisition module is used to acquire operation-related data of the target distribution network;

[0017] A preliminary state matrix generation module is used to perform preliminary state evaluation on each node of the target distribution network based on operation-related data to generate a preliminary state matrix;

[0018] The emergency response strategy generation module is used to locate weak links based on the preliminary state matrix to obtain weak link data, and generate an emergency response strategy for the target distribution network according to the weak link data.

[0019] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0020] at least one processor;

[0021] and a memory communicatively coupled to the at least one processor;

[0022] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute a method for monitoring weak links in a distribution network as described in any embodiment of the present invention.

[0023] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement a method for monitoring weak links in a distribution network as described in any embodiment of the present invention when executed.

[0024] The technical solution of the embodiment of the present invention locates weak links through the preliminary state matrix to obtain weak link data, which can quickly and accurately find the parts with problems in the distribution network, thereby improving the efficiency of problem discovery. An emergency response strategy is generated based on the weak link data, so that effective response measures can be taken quickly when a failure or abnormality occurs in the distribution network, thereby improving the overall operational reliability of the distribution network and ensuring the stability of power supply. It can dynamically track changes in the distribution network and adapt to the ever-changing power grid operating environment and needs.

[0025] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 is a flow chart of a method for monitoring weak links in a distribution network provided according to Embodiment 1 of the present invention;

[0028] Figure 2 is a flow chart of another method for monitoring weak links in a distribution network provided according to Embodiment 2 of the present invention;

[0029] Figure 3 is a structural schematic diagram of a monitoring system for weak links in a distribution network provided according to a third embodiment of the present invention;

[0030] Figure 4It is a structural schematic diagram of an electronic device for implementing a method for monitoring weak links in a distribution network according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] Embodiment 1

[0032] Figure 1 A flow chart of a method for monitoring weak links in a distribution network is provided for the first embodiment of the present invention. This embodiment is applicable to monitoring weak links in a distribution network. The method can be performed by a monitoring device for weak links in a distribution network. The monitoring device for weak links in a distribution network can be implemented in the form of hardware and / or software. The monitoring device for weak links in a distribution network can be configured in a computer controller. Figure 1 As shown, the method includes:

[0033] S110: Obtain operation-related data of the target distribution network.

[0034] The distribution network refers to a power network that receives electric energy from the transmission network or regional power plants and distributes it locally or step by step according to voltage to various users through distribution facilities. The target distribution network can be a certain area of ​​a city, an industrial park, or the part of the distribution network corresponding to a specific user group. Operation-related data refers to data that can reflect the various states and characteristics of the distribution network during operation. Each node of the target distribution network can represent different electrical components of the target distribution network.

[0035] Optionally, obtaining operation-related data of the target distribution network includes: collecting data from each node of the target distribution network through a distributed sensor network to obtain original operation data, wherein the original operation data includes voltage data, current data, temperature data and load rate data; performing wavelet transform denoising on the original operation data to generate wavelet transform denoised data; performing time series frequency change identification on the wavelet transform denoised data to generate a frequency change identification result; when the frequency change identification result is not within a preset frequency change range, generating time series frequency change data according to the frequency change identification result, and adaptively sampling the time series frequency change data to obtain adaptive sampling data; when the frequency change identification result is within a preset frequency change range, generating data to be reconstructed according to the frequency change identification result; reconstructing the adaptive sampling data and the data to be reconstructed to obtain reconstructed data; and standardizing the reconstructed data to generate operation-related data.

[0036] Specifically, the distributed sensor network can be widely distributed on each node of the target distribution network to monitor key parameters such as voltage, current, temperature and load rate in real time. Among them, voltage data reflects the voltage level of each node in the distribution network; current data provides information on the size and flow direction of the current in the distribution network; temperature data indicates the temperature change of the equipment; load rate data indicates the load degree of the equipment or line.

[0037] Furthermore, when collecting data, it is necessary to deploy a variety of sensors and monitoring devices, which will be distributed at various nodes of the target distribution network to collect multi-dimensional operating data such as current, voltage, temperature and load rate in real time. By integrating these sensors, the various electrical components of the distribution network can continuously feedback their health status under different operating conditions, thus forming a comprehensive operating data network. For example, in a specific distribution network, smart sensors can be installed at key locations such as transformers, switchgear and distribution lines. These sensors will monitor the load and working status of electrical components in real time and transmit the data to the central processing system through a wireless network. The system can integrate and analyze the collected multi-dimensional data to provide a detailed view of the operation of the distribution network. This multi-dimensional operating data not only includes the basic electrical parameters of each node, but also combines environmental factors such as the impact of temperature changes on the performance of electrical equipment, so as to more comprehensively reflect the health status of the distribution network. In the actual application scenario, suppose that during the peak power consumption period of a city distribution network, the sensor can monitor the abnormal increase in the temperature of a transformer in real time, and this information will be recorded in time as an important part of the multi-dimensional operating data. By comparing these real-time data with historical data, the system can quickly determine whether the node is in a dangerous state and incorporate relevant information into the construction of the preliminary state matrix. This process not only improves the efficiency of data collection, but also ensures the real-time and accuracy of the data, laying a solid foundation for subsequent risk assessment and monitoring of weak links. Therefore, through effective real-time data collection, the target distribution network can promptly identify potential risks in a dynamically changing environment and ensure the stability and safety of power supply.

[0038] Among them, wavelet transform is a signal processing technology that can decompose the signal into components of different frequencies and time scales. By selecting a suitable wavelet basis function, the noise in the signal can be effectively removed while retaining useful information. Specifically, the following formula (1) can be used for wavelet transform denoising:

[0039] (1)

[0040] in, represents the wavelet transform denoised data, Represents the original multi-dimensional running data, Representative Dimensions, Represents the total number of dimensions, represents the scale of wavelet transform, represents the maximum scale of wavelet transform, represents the time shift index of the wavelet transform, Representative in the Dimension, Scale and The wavelet coefficients under translation, Representative in the Scale and The wavelet basis function under translation, represents the threshold function, which represents the threshold function for denoising the wavelet coefficients. Represents the threshold parameter for denoising.

[0041] Furthermore, after obtaining the wavelet transform denoised data, the time series analysis method can be used to analyze the wavelet transform denoised data and extract the frequency change characteristics. For example, tools such as Fourier transform and wavelet analysis can be used to identify the frequency change trend and periodicity in the data. Specifically, the following formula (2) can be used to obtain the frequency change identification result:

[0042] (2)

[0043] in, represents the frequency change recognition result, is the wavelet transform denoised data, represents the frequency index, represents the total number of frequency indices, represents an imaginary unit, satisfying , Representative frequency values, represents the Diracdelta function, capturing the frequency , represents the threshold function, which represents the threshold function for identifying the frequency component. Represents the threshold parameter for the recognition process.

[0044] Specifically, when the frequency change recognition result is not within the preset frequency change range, the frequency change portion that exceeds the range is determined based on the frequency change recognition result, and the time series frequency change data is generated. The time series frequency change data is adaptively sampled, and the sampling frequency is adjusted according to the change characteristics of the data to obtain more accurate information. Adaptive sampling can improve the resolution and accuracy of the data, and help to better analyze and handle abnormal situations. When the frequency change recognition result is within the preset frequency change range, the data portion within the normal range is determined to generate the data to be reconstructed. These data can be used directly in the subsequent reconstruction process, or reconstructed together with the adaptive sampling data. Specifically, the following formula (3) can be used for adaptive sampling:

[0045] (3)

[0046] in, represents adaptive sampling data, represents the time point of the time series frequency change data, Represents the previous point in time. Represents the time series frequency change data, that is, the frequency change recognition result, represents the total number of frequency indices, represents the frequency index, stands for the Dirac delta function, capturing time , represents the derivative of the time series frequency variation data, Represents the adjustment parameter of adaptive sampling. Specifically, the following formula (4) can be used to reconstruct the adaptive sampling data and the data to be reconstructed:

[0047] (4)

[0048] in, Represents the reconstruction data, is the time point of reconstructing the data, is the total number of interpolation basis functions, is the interpolation coefficient, Representative interpolation basis functions, represents adaptive sampling data, Represents the time series frequency change data, represents the modulation function, represents an imaginary unit, satisfying Represents frequency-varying data. The adaptive sampling data and the data to be reconstructed are reconstructed to obtain complete and processed operation-related data. The reconstruction process can restore the integrity and accuracy of the data while removing the influence of noise and outliers.

[0049] Specifically, standardization can transform the reconstructed data into a form with a uniform scale and distribution, which is convenient for data analysis and comparison. Standardization can eliminate the dimension and unit differences of the data and improve the comparability and versatility of the data. Standardization methods can include mean normalization, standard deviation normalization, etc. By calculating the mean and standard deviation of the data, the data is normalized so that the mean of the data is 0 and the standard deviation is 1.

[0050] For example, in a specific distribution network application scenario, when the system monitors abnormal fluctuations in the current and temperature values ​​of a transformer in real time, these data will be transmitted to the distributed sensor network for collection. After wavelet transform denoising, the system can extract clear current and temperature change data and identify frequency changes. If it is identified that the current frequency change exceeds the preset range, the system will further analyze the data to confirm whether the transformer is in an overload state, and perform adaptive sampling to obtain relevant high-frequency and low-frequency data. By reconstructing these important data and performing standardized processing, the multi-dimensional operating data finally formed will be able to accurately reflect the operating status of the transformer, providing an important basis for subsequent risk assessment and decision-making. Such a data processing process ensures the safe and stable operation of the distribution network, improves the system's responsiveness and intelligent decision-making capabilities, and thus provides strong support for the reliability of power supply.

[0051] S120: Perform a preliminary status assessment on each node of the target distribution network based on the operation-related data to generate a preliminary status matrix.

[0052] Among them, the elements of the preliminary state matrix represent the multi-dimensional operating data of different electrical components at a specific time point, the rows of the preliminary state matrix represent the different electrical components of the target distribution network, and the columns represent the categories of the multi-dimensional operating data. The preliminary state matrix reflects the health of each node at the current moment.

[0053] Optionally, a preliminary state assessment is performed on each node of the target distribution network based on the operation-related data to generate a preliminary state matrix, including: performing spectral analysis on the operation-related data to generate a frequency domain feature set; performing frequency response analysis on each node of the target distribution network based on the frequency domain feature set to obtain a frequency response matrix; performing singular value decomposition on the frequency response matrix to obtain a reduced-dimensional feature vector set; performing trend analysis on the reduced-dimensional feature vector set to obtain trend analysis results; using a preset long short-term memory network to perform similarity prediction and matching on the trend analysis results to obtain a similarity matrix; performing state association analysis on each node of the target distribution network based on the similarity matrix to obtain a state association matrix; using a Bayesian network to estimate the health state probability of each node in the target distribution network based on the state association matrix to obtain a state assessment result corresponding to the degree of each node; and constructing a preliminary state matrix according to the state assessment results corresponding to each node.

[0054] Among them, spectrum analysis is a method of converting operation-related data in the time domain into the frequency domain for analysis. Through tools such as Fourier transform, the signal can be decomposed into components of different frequencies, thereby revealing the frequency characteristics of the signal. Specifically, the frequency domain feature set can be calculated using the following formula (5):

[0055] (5)

[0056] in, is an element of the frequency domain feature set, indicating the Line Column in frequency The frequency domain characteristics of represents the Fourier transform operator, is an element of the multi-dimensional running data matrix, indicating the Line Listed in time The data on It is a specific point in time. In represents an imaginary unit, satisfying , The frequency components representing the frequency domain characteristics, represents the total number of frequency components, Representative Amplitude coefficient, Representative angular frequency, Representative Phase shift, Representative Amplitude coefficient, Representative angular frequency, Representative Phase shift.

[0057] Specifically, frequency response analysis is to study the response characteristics of the system to input signals of different frequencies. Based on the frequency domain feature set, frequency response analysis is performed on each node of the target distribution network to obtain a frequency response matrix. Specifically, the frequency response matrix can be calculated using the following formula (6):

[0058] (6)

[0059] in, is the frequency response matrix, Represents the elements of the frequency response matrix, indicating the Line The frequency response values ​​of the columns, represents the index of the node in the target distribution network, represents the index of the frequency domain feature set, represents the total number of nodes in the target distribution network, Represents the total number of frequency domain feature sets.

[0060] Specifically, singular value decomposition is a matrix decomposition method that can decompose a matrix into the product of three matrices. Through singular value decomposition, the main feature information of the matrix can be extracted and the dimension of the data can be reduced. Specifically, the following formula (7) can be used to calculate the dimension reduction feature vector set:

[0061] (7)

[0062] in, represents the frequency response matrix, represents a left singular matrix, represents the singular value matrix, represents a right singular matrix, represents the rank after dimensionality reduction, Represents the front of the left singular matrix List, represents the front of the right singular matrix List, The front of the singular value matrix The diagonal matrix of singular values, Represents the frequency response matrix after dimensionality reduction.

[0063] Specifically, trend analysis is to analyze the time series of data to find out the changing trends and patterns of data. In the distribution network status assessment, trend analysis can help discover the changing trends of node status and predict possible problems. The similarity prediction and matching of the trend analysis results are performed using the preset long short-term memory network to obtain a similarity matrix. Specifically, the similarity matrix can be calculated using the following formula (8):

[0064] (8)

[0065] in, represents the similarity matrix, represents the activation function (such as sigmoid), represents the total number of time steps, Represents the time step The weight of , tanh represents the hyperbolic tangent activation function, represents the hidden state at the previous time step, The weight matrix representing the hidden state, represents the input of the current time step, represents the input weight matrix, b represents the bias vector, A represents the first trend analysis result matrix, B represents the second trend analysis result matrix, C represents the third trend analysis result matrix, D represents the fourth trend analysis result matrix, d represents the normalization factor, and o represents element multiplication (Hadamard product).

[0066] Specifically, state association analysis is to find out the association relationship between nodes by analyzing the correlation between the states of each node. The state association matrix can be calculated using the following formula (9):

[0067] (9)

[0068] in, represents the state association matrix, Represents the similarity matrix Line Column elements, Represents the state influence factor matrix Line Column elements, Represents the state association calculation matrix Line Column elements, Represents the state association calculation matrix Line Column elements, Represents the normalization factor, corresponding to the List, Represents the total number of matching operations, represents the total number of nodes in the target distribution network, Represents the total number of categories for multi-dimensional operational data.

[0069] Specifically, the Bayesian network is a probabilistic graphical model that can estimate the probability of unknown variables based on known variable relationships and prior knowledge. In the distribution network state assessment, the Bayesian network can use the information in the state association matrix, combined with prior knowledge and observation data, to estimate the health state of each node. The state assessment results of each node are then arranged in the order of the nodes to form a matrix. This matrix is ​​the preliminary state matrix, which reflects the preliminary state assessment of each node in the target distribution network. The following formula (10) is an example of a preliminary state matrix:

[0070] (10)

[0071] in, is the element of the initial state matrix, indicating the Line Listed in time Multi-dimensional operation data on Represents the index of the electrical components in the target distribution network, Represents the category index of multi-dimensional running data, Represents a specific point in time. represents the total number of nodes in the target distribution network, Represents the total number of categories for multi-dimensional operational data.

[0072] Specific application scenario: Based on the operation-related data, a preliminary state assessment is performed on each node of the target distribution network. First, spectrum analysis is required to extract the frequency domain feature set. This process can reveal the characteristics of the node at different frequencies by transforming the time domain signal. For example, if the spectrum analysis of a transformer shows abnormal frequency components, this may indicate that it has a potential fault. After obtaining the frequency domain features, frequency response analysis is performed to form a frequency response matrix. This matrix provides detailed information on the response of each node at different frequencies, which can help identify which nodes behave abnormally at specific frequencies. Then, by performing singular value decomposition on the frequency response matrix, a set of reduced-dimensional feature vectors can be obtained, which helps to simplify the data and highlight important changes in operating status. Subsequently, trend analysis is performed on the reduced-dimensional feature vector set to obtain trend analysis results to reveal the trend of operating status changes of each node in different time periods. For example, if the operating status of a node shows an upward trend, this may indicate that it is gradually moving towards an overload state, and vice versa. At this time, the preset long-short memory network is used to perform similarity prediction and matching on the trend analysis results to form a similarity matrix, which can capture the state change relationship between nodes. Finally, based on the similarity matrix, the state correlation analysis of each node of the target distribution network is performed to obtain the state correlation matrix. This matrix helps identify which nodes have correlation in health status, and if a node fails, whether other nodes will also be affected. Using the Bayesian network, according to the state correlation matrix, the probability of the health status of each node is estimated, so as to obtain the state assessment results corresponding to each node. This comprehensive analysis method ensures the accuracy and timeliness of distribution network monitoring and provides a scientific basis for operation and maintenance decisions. In this way, potential risks of the distribution network can be effectively warned and prevented to ensure its stable operation.

[0073] S130, locating weak links based on the preliminary state matrix to obtain weak link data, and generating an emergency response strategy for the target distribution network according to the weak link data.

[0074] Among them, weak link location refers to finding the relatively vulnerable and problem-prone parts of the distribution network based on the preliminary status matrix. Emergency response strategy refers to the response measures formulated for possible emergencies in the distribution network. It includes isolation measures, load transfer plans, emergency repair plans, emergency power supply arrangements, etc. when a fault occurs, with the aim of restoring the normal operation of the distribution network in the shortest possible time and reducing the impact on users.

[0075] Optionally, weak link positioning is performed based on the preliminary state matrix to obtain weak link data, including: performing structured processing on the preliminary state matrix through a preset topology analysis algorithm to obtain a topology structure matrix of the target distribution network, wherein the topology structure matrix includes the connection relationship between each node, the branch impedance parameters corresponding to each node, and the electrical characteristics of each node; performing network modeling on the target distribution network based on the topology structure matrix to obtain a network feature vector; performing weak link analysis on the target distribution network according to the network feature vector to obtain a node vulnerability index; performing a comprehensive assessment on each node of the target distribution network based on the node vulnerability index to obtain a risk score for each node; performing spatial clustering analysis on the target distribution network according to the risk score of each node through a self-organizing mapping neural network algorithm to obtain a risk hotspot map; determining the location of the weak link in the risk hotspot map through a preset risk assessment algorithm, and performing weak link monitoring at the weak link location to obtain weak link data.

[0076] Specifically, the topology analysis algorithm can determine the connection relationship between nodes, the impedance parameters of branches, and the electrical characteristics of nodes by traversing each node and branch of the distribution network. Specifically, the topology matrix of the target distribution network can be calculated using the following formula (11):

[0077] (11)

[0078] Among them, T is the topological structure matrix, Zij represents the The node and The branch impedance between the nodes is Indicates The current of the node, Indicates The voltage of the node, Indicates The power of a node, Indicates The node and The connection relationship between nodes, Indicates the total number of nodes.

[0079] Specifically, network modeling is to abstract the distribution network into a mathematical model for analysis and calculation. Specifically, the target distribution network can be modeled based on the topological structure matrix through graph theory technology, as shown in the following formula (12):

[0080] (12)

[0081] in, represents the network feature vector, H represents the feature matrix, Total number of nodes, is the topological matrix Line Column elements, representing nodes and nodes The connection relationship between The degree matrix Line Column elements, representing nodes The degree, Representative Node The characteristic vector of Represents the regularization parameter, which controls the feature matrix H and the node feature vector The gap, Represents the regularization parameter, which controls the characteristic matrix H and the network characteristic vector The gap.

[0082] Specifically, the node vulnerability index can be calculated using the following formula (13):

[0083] (13)

[0084] in, Representative Node The vulnerability indicators is the topological matrix Line Column elements, representing nodes and nodes The connection relationship between Representative Node The network feature vector of Represents the total number of nodes, represents the regularization parameter, which is used to control the power effect, Representation Node In the Power on each path Indicates the path For Node The influence coefficient of represents the regularization parameter used to control the effect of current and voltage, Ii node The current, represent The voltage of L represents the total number of normalization factors, Represents the normalized matrix Line Column elements, Represents the lth row of the normalized matrix Column elements.

[0085] Specifically, the risk score of each node can be calculated using the following formula (14):

[0086] (14)

[0087] in, represents the risk score of node i, represents the adjacency matrix and the vulnerability coordinate weight parameter, Power paths and influence coefficient weight parameters, represents the current and voltage weight parameters, represents the temperature weight parameter, The elements of the adjacency matrix represent nodes and nodes The connection relationship between Is a node The vulnerability indicators Total number of nodes, Is a node In the The power on the path, Representative path For Node The influence coefficient of represents the total number of power paths, Represents the normalized matrix The element in row and column l, Represents the normalized matrix Line Column elements, L represents the total number of normalization factors, Ii represents the node The current, Representation Node The voltage, Representative Node The geographic location feature vector, is the communication characteristic for the node The influence coefficient of Represents the total number of temperature eigenvectors.

[0088] Among them, the self-organizing map neural network is an unsupervised learning algorithm that can map high-dimensional data to low-dimensional space and maintain the topological structure of the data. In the risk analysis of distribution networks, the self-organizing map neural network can perform spatial clustering analysis based on the risk score of each node and cluster nodes with similar risk levels together.

[0089] Specifically, the risk score of each node is used as input data and input into the self-organizing map neural network. The network gradually forms clusters of the input data through competitive learning and weight adjustment. Each cluster represents a risk level or area. The clustering results are visualized to obtain a risk hotspot map. The risk hotspot map is usually displayed in the form of a map, and different colors or patterns represent different risk levels. High-risk areas are presented as hotspots in the map, that is, the risk hotspot map. The risk hotspot map includes high-risk areas, medium-risk areas, and low-risk areas. Finally, the location of the weak link is obtained by real-time positioning of the high-risk area and the medium-risk area, as shown in the following formula (15):

[0090] (15)

[0091] in, Represents the location of the weak link, represents the weight parameter for the overall risk assessment, and Represent the weight parameters of high-risk and medium-risk areas, and Represent the weight parameters of power path and temperature characteristics respectively, Representative Node High risk score, Representative Node A medium risk score, Representative Node The vulnerability indicators Represents the total number of nodes, Representative Node The power, Representative Node The influence coefficient on the risk assessment algorithm, Representative Node The normalization factor of Representative Node The normalization factor of Representative Node The geographic location feature vector, Representative Node The communication characteristics influence coefficient.

[0092] Specific application scenario: First, the preset topology analysis algorithm is used to structure the preliminary state matrix to obtain the topology matrix of the target distribution network. The topology matrix contains the connection relationship between each node, the branch impedance parameters and electrical characteristics corresponding to each node. This information lays an important foundation for subsequent risk assessment. By analyzing these structural features, the system can clearly identify the overall layout of the distribution network and the interaction between each node, thereby providing a basis for locating the weak links. After forming the topology matrix, the system will then apply graph theory technology to model the target distribution network and obtain the network feature vector. At this point, the network feature vector not only describes the basic characteristics of the network, but also reveals the connection strength and interdependence between nodes. Based on these network feature vectors, the system will analyze the weak links of the target distribution network and obtain the node vulnerability index. The calculation of the node vulnerability index will reflect the ability of each node to withstand pressure in the network. The higher the vulnerability index, the more likely the node is to become a weak link in the system. Based on the node vulnerability index, the system will conduct a comprehensive assessment of each node in the target distribution network to obtain the risk score of each node. This process combines multiple assessment dimensions to ensure the accuracy and reliability of the final risk score. After obtaining the risk score of each node, the system will use the self-organizing map neural network algorithm to perform spatial cluster analysis on these scores to generate a risk hotspot map. The risk hotspot map divides the distribution network into high-risk areas, medium-risk areas and low-risk areas, so that managers can intuitively understand which areas may have potential problems. After that, the system will again use the preset risk assessment algorithm to locate the high-risk areas and medium-risk areas in real time to obtain the location of the weak links. This process ensures that in actual operation, managers can quickly identify the areas that need to be monitored. After confirming the location of the weak links, the system will monitor the weak links at these locations to collect more detailed data. Through monitoring, managers can obtain weak link data in a timely manner and take corresponding measures to avoid potential risks affecting the safety and stability of the distribution network. For example, if in a city's distribution network, nodes in certain areas show high vulnerability indicators due to aging, through the above steps, the system can quickly identify these high-risk nodes and monitor them in real time to ensure that these weak links will not cause large-scale power outages or other safety issues under high load or extreme weather conditions. This method not only improves the management efficiency of the distribution network, but also enhances its reliability, providing effective support for practical applications.

[0093] Optionally, a spatial clustering analysis is performed on the target distribution network according to the risk score of each node through a self-organizing map neural network algorithm to obtain a risk hotspot map, including: performing nonlinear dimensionality reduction mapping on the node risk score through a self-organizing map neural network algorithm to obtain the risk distribution of each node on the mapping grid; using a kernel density estimation method to calculate the relative risk density of each node in the corresponding neighborhood based on the risk distribution of each node to obtain a risk density map of each node; performing local in-depth analysis on the node density distribution in the risk density map of each node to obtain a preliminary risk area division map, wherein the preliminary risk area division map is used to distinguish different risk level areas in the target distribution network; and optimizing the risk area boundaries of the preliminary risk area division map to obtain a risk hotspot map.

[0094] Specifically, the Self-Organizing Map (SOM) neural network is an unsupervised learning algorithm that can map high-dimensional data to a low-dimensional space (usually a two-dimensional grid) while maintaining the topological structure of the data. The controller can input the risk score of each node as an input vector into the input layer of the SOM. Then, each node in the output layer is compared with the input vector and the distance between them is calculated (usually using the Euclidean distance). The output node with the smallest distance (called the winning node) is activated. The weights of the winning node and its neighboring nodes are adjusted according to the input vector so that they are closer to the input vector. This process is repeated until the node weights of the output layer stabilize. Finally, each input vector (node ​​risk score) is mapped to a node in the output layer, thereby obtaining the risk distribution of each node on the mapped grid, as shown in the following formula (16):

[0095] (16)

[0096] in, Representative Node and nodes Risk distribution on the mapped grid, represents the nonlinear activation function, is the total number of node features, is the activation function in the self-organizing map neural network, is the dimension of the input features, is the weight parameter of the self-organizing map neural network, Is a node The lth risk score of is an element of the adjacency matrix, representing the node and nodes The connection relationship between Is a node vulnerability indicators.

[0097] Specifically, kernel density estimation is a nonparametric estimation method used to estimate the probability density function of a random variable. Kernel density estimation estimates the probability density by placing a kernel function around each data point and summing all kernel functions. When calculating the relative risk density, for each node on the mapping grid (corresponding to a risk score), its neighborhood range is determined. The neighborhood range can be a fixed radius or an adjacent area. In the neighborhood of each node, the risk density of the node is calculated using the kernel density estimation method. Specifically, for each node in the neighborhood, its distance from the current node is calculated, and its contribution to the risk density of the current node is calculated based on the kernel function. The contributions of all nodes in the neighborhood are added together to obtain the relative risk density of the current node. This density value reflects the relative size of the risk of the node in its neighborhood. Repeat the above process for all nodes on the mapping grid to obtain the risk density map of each node. The risk density map shows the relative risk density of each node, and the darker area indicates the area with higher risk density. Specifically, the following formula (17) can be used to calculate the risk density map of each node:

[0098] (17)

[0099] in, Representative Node In Location The risk density of represents the total number of nodes, h represents the bandwidth of the kernel density estimation, represents the kernel function, Representative Node location, Representative Node and nodes The risk relationship between them.

[0100] Specifically, local depth analysis is a data analysis method used to explore the local structure and distribution of data. In the risk density map, local depth analysis can help determine the boundaries between areas with higher risk density and areas with lower risk density. The local depth can be determined by calculating the distance from the node to other nodes in its neighborhood. Nodes with larger distances have higher local depths, meaning they are relatively isolated in the local area. The risk density map is divided into different areas based on the local depth of the nodes. Generally, nodes with larger local depths are considered to be in areas with lower risk density, while nodes with smaller local depths are considered to be in areas with higher risk density.

[0101] However, the preliminary risk area division map may have unclear or inaccurate boundaries, so it is necessary to refine and adjust the boundaries in the preliminary risk area division map. Image processing techniques, such as edge detection and morphological operations, can be used to improve the quality of the boundaries. Specifically, the following formula (18) can be used to optimize the risk area boundaries:

[0102] (18)

[0103] in, represents the risk heat map, represents the risk area boundary, It is the first Line The risk value of the column, is a boundary optimization function based on the boundary and distance function , It is Line The boundary conditions of the column, Indicates Line The distance function of the column, , are the number of rows and columns of the preliminary risk area division map.

[0104] Specific application scenarios: First, the controller can use the self-organizing map neural network algorithm to perform nonlinear dimensionality reduction mapping on the node risk score, thereby generating the risk distribution of each node on the mapping grid. This mapping process can convert high-dimensional risk score data into a more understandable two-dimensional or three-dimensional form, making the position of each node in the risk distribution clearer. This result will provide an intuitive basis for subsequent risk analysis. After obtaining the risk distribution of each node, the system will use the kernel density estimation method to calculate the relative risk density of each node in the corresponding neighborhood based on these risk distributions, thereby forming a risk density map for each node. Through this process, the system can identify the concentration of risks in different areas, and then reflect the risk level that certain nodes may face in the overall network. For example, in a certain distribution network area, if the node density map shows high-risk aggregation, this indicates that there may be potential problems such as equipment aging and excessive load in the area. Next, the system will perform a local in-depth analysis of the node density distribution in the risk density map of each node to obtain a preliminary risk area division map. This division map is designed to distinguish different risk level areas in the target distribution network, so that operation and maintenance personnel can quickly identify high-risk, medium-risk and low-risk areas. In this process, the formation of the preliminary risk area division map is based on a comprehensive assessment of the risk density and node status of each area, thereby ensuring the accuracy of the division. After obtaining the preliminary risk area division map, the system will optimize the risk area boundaries to obtain the final risk hotspot map. This step will further improve the accuracy of the area division, ensure that the boundaries of each risk level area are more in line with the actual situation, and enable more effective monitoring and management in actual operation and maintenance. For example, when the operation and maintenance personnel find that an area is marked as a high-risk area based on the risk hotspot map, they can take immediate measures, such as increasing the detection frequency or performing equipment maintenance, to prevent the occurrence of potential failures. In summary, through the self-organizing map neural network algorithm, kernel density estimation and local depth analysis, the system can achieve comprehensive risk assessment and hotspot monitoring of the distribution network, ensure the safe and stable operation of the distribution network, and provide a scientific basis for operation and maintenance decisions.

[0105] The technical solution of the embodiment of the present invention locates weak links through the preliminary state matrix to obtain weak link data, which can quickly and accurately find the parts with problems in the distribution network, thereby improving the efficiency of problem discovery. An emergency response strategy is generated based on the weak link data, so that effective response measures can be taken quickly when a failure or abnormality occurs in the distribution network, thereby improving the overall operational reliability of the distribution network and ensuring the stability of power supply. It can dynamically track changes in the distribution network and adapt to the ever-changing power grid operating environment and needs.

[0106] Embodiment 2

[0107] Figure 2This is a flow chart of a method for monitoring weak links in a distribution network provided in the second embodiment of the present invention. This embodiment adds a specific process of generating an emergency response strategy for a target distribution network based on weak link data on the basis of the first embodiment. The specific contents of steps S210-S220 are roughly the same as steps S110-S120 in the first embodiment, so they will not be described in detail in this embodiment. Figure 2 As shown, the method includes:

[0108] S210: Obtain operation-related data of the target distribution network.

[0109] Optionally, obtaining operation-related data of the target distribution network includes: collecting data from each node of the target distribution network through a distributed sensor network to obtain original operation data, wherein the original operation data includes voltage data, current data, temperature data and load rate data; performing wavelet transform denoising on the original operation data to generate wavelet transform denoised data; performing time series frequency change identification on the wavelet transform denoised data to generate a frequency change identification result; when the frequency change identification result is not within a preset frequency change range, generating time series frequency change data according to the frequency change identification result, and adaptively sampling the time series frequency change data to obtain adaptive sampling data; when the frequency change identification result is within a preset frequency change range, generating data to be reconstructed according to the frequency change identification result; reconstructing the adaptive sampling data and the data to be reconstructed to obtain reconstructed data; and standardizing the reconstructed data to generate operation-related data.

[0110] S220: Perform a preliminary status assessment on each node of the target distribution network based on the operation-related data to generate a preliminary status matrix.

[0111] Optionally, a preliminary state assessment is performed on each node of the target distribution network based on the operation-related data to generate a preliminary state matrix, including: performing spectral analysis on the operation-related data to generate a frequency domain feature set; performing frequency response analysis on each node of the target distribution network based on the frequency domain feature set to obtain a frequency response matrix; performing singular value decomposition on the frequency response matrix to obtain a reduced-dimensional feature vector set; performing trend analysis on the reduced-dimensional feature vector set to obtain trend analysis results; using a preset long short-term memory network to perform similarity prediction and matching on the trend analysis results to obtain a similarity matrix; performing state association analysis on each node of the target distribution network based on the similarity matrix to obtain a state association matrix; using a Bayesian network to estimate the health state probability of each node in the target distribution network based on the state association matrix to obtain a state assessment result corresponding to the degree of each node; and constructing a preliminary state matrix according to the state assessment results corresponding to each node.

[0112] S230, locating weak links based on the preliminary state matrix to obtain weak link data.

[0113] Optionally, weak link positioning is performed based on the preliminary state matrix to obtain weak link data, including: performing structured processing on the preliminary state matrix through a preset topology analysis algorithm to obtain a topology structure matrix of the target distribution network, wherein the topology structure matrix includes the connection relationship between each node, the branch impedance parameters corresponding to each node, and the electrical characteristics of each node; performing network modeling on the target distribution network based on the topology structure matrix to obtain a network feature vector; performing weak link analysis on the target distribution network according to the network feature vector to obtain a node vulnerability index; performing a comprehensive assessment on each node of the target distribution network based on the node vulnerability index to obtain a risk score for each node; performing spatial clustering analysis on the target distribution network according to the risk score of each node through a self-organizing mapping neural network algorithm to obtain a risk hotspot map; determining the location of the weak link in the risk hotspot map through a preset risk assessment algorithm, and performing weak link monitoring at the weak link location to obtain weak link data.

[0114] Optionally, a spatial clustering analysis is performed on the target distribution network according to the risk score of each node through a self-organizing map neural network algorithm to obtain a risk hotspot map, including: performing nonlinear dimensionality reduction mapping on the node risk score through a self-organizing map neural network algorithm to obtain the risk distribution of each node on the mapping grid; using a kernel density estimation method to calculate the relative risk density of each node in the corresponding neighborhood based on the risk distribution of each node to obtain a risk density map of each node; performing local in-depth analysis on the node density distribution in the risk density map of each node to obtain a preliminary risk area division map, wherein the preliminary risk area division map is used to distinguish different risk level areas in the target distribution network; and optimizing the risk area boundaries of the preliminary risk area division map to obtain a risk hotspot map.

[0115] S240: Determine the weak link type corresponding to the weak link data.

[0116] Optionally, determining the weak link type corresponding to the weak link data includes: extracting features from the weak link data to obtain a weak link vector, wherein the weak link vector includes a voltage fluctuation vector, a harmonic content vector, a power factor vector and a load imbalance vector; generating a time-frequency feature vector according to the weak link vector, and performing nonlinear dynamic modeling on the time-frequency feature vector to obtain a dynamic modeling vector; performing fault mode identification on the weak link position based on the dynamic modeling vector through a preset fuzzy inference algorithm to obtain a fault mode identification result; and dynamically classifying the fault mode identification result to obtain a weak link type.

[0117] Among them, the types of weak links include voltage problems (such as overvoltage, undervoltage), current problems (such as overcurrent, short circuit), equipment failure (such as transformer failure, switch failure), line problems (such as disconnection, grounding fault), etc. Determining the type of weak links helps to formulate emergency response strategies in a targeted manner. Voltage fluctuation refers to the change of grid voltage in a short period of time. Harmonics refer to the current or voltage components in the grid whose frequency is an integer multiple of the fundamental wave. Excessive harmonic content will have an adverse effect on grid equipment and power quality. By performing harmonic analysis on the current or voltage signal at the weak link position, the content of each harmonic can be obtained, thereby forming a harmonic content vector. Power factor refers to the ratio of active power to apparent power, which reflects the utilization efficiency of electric energy in the grid. Load imbalance refers to the degree of imbalance between three-phase loads. In a three-phase grid, if the three-phase load is unbalanced, it will cause imbalance in grid voltage and current, affecting the stability and reliability of the grid.

[0118] It should be noted that time-frequency analysis is an analysis method that considers the characteristics of the signal in both the time and frequency domains. For the voltage fluctuation vector, harmonic content vector, power factor vector and load imbalance vector in the weak link vector, time-frequency analysis methods such as short-time Fourier transform and wavelet transform can be used to convert them into feature representations in the time-frequency domain, thereby obtaining a time-frequency feature vector. The time-frequency feature vector can more comprehensively reflect the dynamic characteristics of the weak link. Specifically, the time-frequency feature vector can be calculated using the following formula (19):

[0119] (19)

[0120] in, is the time-frequency feature vector, W is the weak link vector, is a scale function, representing the decomposition of the sth scale is a frequency function, indicating the Frequency decomposition, is the number of scales, is the number of frequencies.

[0121] Specifically, nonlinear dynamic modeling is a method used to describe the dynamic behavior of complex systems. For time-frequency feature vectors, nonlinear dynamic modeling methods, such as neural networks, support vector machines, etc., can be used to establish their dynamic models. By training and learning the time-frequency feature vectors, a dynamic modeling vector can be obtained, which can better reflect the nonlinear dynamic characteristics of the weak link. Specifically, the dynamic modeling vector can be calculated using the following formula (20):

[0122] (20)

[0123] in, is the dynamic modeling vector, It is time-frequency feature vectors, is a nonlinear dynamic function acting on the time-frequency feature vectors, is a nonlinear dynamic function acting on the time-frequency feature vectors, is the total number of time-frequency eigenvectors.

[0124] Specifically, fuzzy reasoning is a reasoning method based on fuzzy logic that can handle uncertainty and ambiguity. Through the preset fuzzy reasoning algorithm, the fault mode of the weak link location is identified based on the dynamic modeling vector to obtain the fault mode recognition result, which is shown in the following formula (21):

[0125] (twenty one)

[0126] in, Failure mode identification results, is the fuzzy inference function, It is A fuzzy membership function acts on the dynamic modeling vector , It is A fuzzy rule acts on the weak link position L, is the dynamic modeling vector, L is the weak link position vector, is the number of fuzzy rules.

[0127] Based on the results of fault mode identification, a dynamic classification method can be used to more accurately classify the types of weak links, as shown in the following formula (22):

[0128] (twenty two)

[0129] in, It is a weak link type. It is The weight coefficient of the class, is a dynamic classification function, acting on the Failure mode identification results and classification standards , It is The failure mode identification results are It is classification criteria, and K is the number of classification categories.

[0130] Specific application scenarios: First, edge computing technology is applied to the processing of weak link data to achieve real-time feature extraction. By extracting features from weak link data, the system can generate an extracted weak link vector, which contains information in multiple dimensions such as voltage fluctuation vector, harmonic content vector, power factor vector, and load imbalance vector. These feature vectors can fully reflect the operating status of the distribution network and provide key data support for subsequent analysis. For example, in an electric power system, voltage fluctuations may lead to unstable operation of equipment. Therefore, by monitoring this vector, potential problems can be discovered in a timely manner. After obtaining the extracted weak link vector, the system will perform multi-scale decomposition on it to obtain a time-frequency feature vector. Through this process, the system can capture the changing characteristics of the signal at different time scales and frequency ranges, so that the dynamic behavior of the weak link can be more deeply understood. At this time, the time-frequency feature vector can not only reveal the transient characteristics of the equipment operation, but also analyze the possible long-term trends, providing a multi-level perspective for subsequent fault analysis. Next, the system performs nonlinear dynamic modeling on the time-frequency feature vector to obtain a dynamic modeling vector. The modeling process aims to capture the nonlinear relationship within the system so that the model can more accurately reflect the complex dynamic behavior of the distribution network. Through such dynamic modeling, the system can identify specific patterns and trends, providing a more accurate data basis for fault pattern identification. Subsequently, through the preset fuzzy reasoning algorithm, based on the dynamic modeling vector, the system performs fault pattern identification on the weak link location to obtain the fault pattern identification result. The fuzzy reasoning algorithm plays a key role in this process because it can handle uncertainty and ambiguity, enabling the system to more accurately identify potential fault modes in a complex operating environment. For example, when a weak link fails due to excessive load, the fuzzy reasoning algorithm can combine historical data and real-time monitoring information to comprehensively determine the type of fault and its possible impact. Finally, the system will dynamically classify the fault pattern identification results to obtain the type of weak link. Through this classification process, operation and maintenance personnel can quickly identify the types corresponding to different weak links, providing guidance for subsequent maintenance and management. For example, after a certain area is identified as a load imbalance problem, relevant personnel can carry out targeted load allocation to avoid equipment damage or system crash, thereby improving the reliability and stability of the entire distribution network. Therefore, the implementation of this series of steps not only improves the accuracy of identifying weak links, but also provides important support for the optimized management of distribution networks.

[0131] S250: semantically analyze the weak link type to obtain a semantic feature vector.

[0132] Among them, semantic parsing is to convert the weak link type described in natural language into a semantic feature representation that can be understood by a computer. Natural language processing techniques such as lexical analysis, syntactic analysis, and semantic role labeling can be used. For example, for the weak link type of "transformer overheating fault", key semantic elements such as "transformer", "overheating", and "fault" can be extracted through semantic parsing. The extracted semantic elements are then converted into numerical vector representations, namely semantic feature vectors. Word vector techniques such as Word2Vec and GloVe can be used to represent each semantic element as a low-dimensional vector. Finally, the vectors of all semantic elements are combined to form a semantic feature vector.

[0133] S260: Filter historical emergency strategies in a preset database based on the semantic feature vector to obtain a candidate strategy set.

[0134] The preset database is a database containing historical emergency strategies, where the strategies are formulated for different types of weak links and failure conditions. The database may include information such as the description of the strategy, applicable conditions, and implementation steps.

[0135] S270: Generate an emergency response strategy based on the candidate strategy set.

[0136] Optionally, an emergency response strategy is generated based on a candidate strategy set, including: performing a strategy evaluation on the candidate strategy set through a preset Pareto front search algorithm to obtain a strategy evaluation result; weighting the strategy evaluation result to obtain a strategy allocation weight; ranking the candidate strategy set based on the strategy allocation weight to obtain an optimal emergency response strategy; and performing intelligent strategy matching on the target distribution network based on the optimal emergency response strategy to obtain an emergency response strategy.

[0137] Among them, the Pareto front search algorithm is a multi-objective optimization algorithm used to find a set of optimal solutions among multiple objectives. In the evaluation of emergency response strategies, there are usually multiple objectives to consider, such as minimizing power outage time, minimizing economic losses, maximizing system reliability, etc. Weight allocation is to determine the importance of each objective in the comprehensive evaluation. Different objectives may have different importance for the selection of emergency response strategies. For example, in some cases, minimizing power outage time may be more important than minimizing economic losses. Through weight allocation, the relative importance of each objective can be adjusted according to the actual situation.

[0138] Specifically, when sorting strategies, the controller multiplies the performance indicators of each strategy on each target by the corresponding weight, and then sums them up to get the comprehensive score, and then sorts the candidate strategy set according to the comprehensive score. Intelligent strategy matching is to match and adjust the optimal emergency response strategy with the specific situation of the target distribution network to ensure the effectiveness and feasibility of the strategy. The target distribution network may have different topological structures, equipment characteristics and operating conditions, so the optimal emergency response strategy needs to be appropriately adjusted and optimized.

[0139] Specific application scenarios: First, the system will perform semantic analysis on the weak link type, aiming to extract the key information of the weak link, thereby generating a semantic feature vector. The feature vector can comprehensively reflect the specific characteristics, potential risks and related operating status of the weak link, which provides the necessary basic data for subsequent strategy matching. For example, if the weak link type indicates that there is a voltage fluctuation problem, the semantic feature vector will contain relevant voltage characteristic information to help the system identify the emergency strategy applicable to the problem. Next, based on the semantic feature vector, the system will search and filter the historical emergency strategies in the preset database to form a candidate strategy set. In this process, the system uses an intelligent matching algorithm to filter out the strategy that is most relevant to the current situation by analyzing the similarity between the historical emergency strategies and the current weak link characteristics. This step ensures that the selected strategy is not only targeted, but also provides an effective solution for the current weak link based on historical data and previous response experience. For example, if a similar voltage fluctuation event has been successfully handled in a historical strategy, then the strategy will be included in the candidate strategy set. Subsequently, the system will perform a strategy evaluation on the candidate strategy set through a preset Pareto frontier search algorithm to obtain a strategy evaluation result. The Pareto front search algorithm can effectively balance multiple objectives, allowing the system to consider multiple factors simultaneously when evaluating strategies, such as response time, resource consumption, and potential risks. Through this evaluation, the system can identify the advantages and disadvantages of each candidate strategy, ensuring that the final strategy selected is not only effective but also feasible. After obtaining the strategy evaluation results, the system will assign weights to give different strategies different importance. This process is based on the specific performance of the strategy evaluation. For example, a strategy has a high historical success rate in dealing with voltage fluctuations, so it will be given a higher weight when assigning weights. In this way, the system can ensure that more attention is paid to those strategies that have performed well in actual applications when making decisions. Finally, based on the weights assigned to the strategies, the system will rank the candidate strategy set to obtain the optimal emergency response strategy. This ranking process not only takes into account the historical performance of the strategy, but also combines the specific conditions of the current weak links to ensure that the selected strategy can minimize risks in the shortest time and at the lowest cost. For example, in the case of large voltage fluctuations, the system gives priority to those proven fast response strategies to immediately stabilize the operation of the power grid. Through the above steps, the system will achieve intelligent strategy matching for the target distribution network, ensuring that when faced with weak links, an effective emergency response strategy can be quickly formulated to maintain the safe and stable operation of the distribution network. The intelligence and efficiency of this process greatly improves the responsiveness and flexibility of the distribution network in dealing with emergencies, and ultimately ensures the reliability and continuity of power supply.

[0140] The technical solution of the embodiment of the present invention locates weak links through the preliminary state matrix to obtain weak link data, which can quickly and accurately find the parts with problems in the distribution network, thereby improving the efficiency of problem discovery. An emergency response strategy is generated based on the weak link data, so that effective response measures can be taken quickly when a failure or abnormality occurs in the distribution network, thereby improving the overall operational reliability of the distribution network and ensuring the stability of power supply. It can dynamically track changes in the distribution network and adapt to the ever-changing power grid operating environment and needs.

[0141] Embodiment 3

[0142] Figure 3 This is a schematic diagram of the structure of a monitoring system for weak links in a distribution network provided by the third embodiment of the present invention. Figure 3 As shown, the system includes: an operation-related data acquisition module 310, which is used to acquire operation-related data of the target distribution network;

[0143] A preliminary state matrix generating module 320 is used to perform preliminary state evaluation on each node of the target distribution network based on the operation-related data to generate a preliminary state matrix;

[0144] The emergency response strategy generation module 330 is used to locate weak links based on the preliminary state matrix to obtain weak link data, and generate an emergency response strategy for the target distribution network according to the weak link data.

[0145] Optionally, the operation-related data acquisition module 310 is specifically used to: collect data from each node of the target distribution network through a distributed sensor network to obtain original operation data, wherein the original operation data includes voltage data, current data, temperature data and load rate data; perform wavelet transform denoising on the original operation data to generate wavelet transform denoised data; perform time series frequency change identification on the wavelet transform denoised data to generate a frequency change identification result; when the frequency change identification result is not within a preset frequency change range, generate time series frequency change data according to the frequency change identification result, and adaptively sample the time series frequency change data to obtain adaptive sampling data; when the frequency change identification result is within a preset frequency change range, generate data to be reconstructed according to the frequency change identification result; reconstruct the adaptive sampling data and the data to be reconstructed to obtain reconstructed data; and standardize the reconstructed data to generate operation-related data.

[0146] Optionally, the preliminary state matrix generation module 320 is specifically used to: perform spectral analysis on the operation-related data to generate a frequency domain feature set; perform frequency response analysis on each node of the target distribution network based on the frequency domain feature set to obtain a frequency response matrix; perform singular value decomposition on the frequency response matrix to obtain a reduced-dimensional feature vector set; perform trend analysis on the reduced-dimensional feature vector set to obtain trend analysis results; use a preset long short-term memory network to perform similarity prediction and matching on the trend analysis results to obtain a similarity matrix; perform state association analysis on each node of the target distribution network based on the similarity matrix to obtain a state association matrix; use a Bayesian network to estimate the health state probability of each node in the target distribution network based on the state association matrix to obtain a state assessment result corresponding to the degree of each node; and construct a preliminary state matrix based on the state assessment results corresponding to each node.

[0147] Optionally, the emergency response strategy generation module 330 specifically includes: a weak link data generation unit, which is used to: perform structured processing on the preliminary state matrix through a preset topology analysis algorithm to obtain a topology structure matrix of the target distribution network, wherein the topology structure matrix includes the connection relationship between each node, the branch impedance parameters corresponding to each node, and the electrical characteristics of each node; perform network modeling on the target distribution network based on the topology structure matrix to obtain a network feature vector; perform weak link analysis on the target distribution network according to the network feature vector to obtain a node vulnerability index; perform a comprehensive assessment on each node of the target distribution network based on the node vulnerability index to obtain a risk score for each node; perform spatial clustering analysis on the target distribution network according to the risk score of each node through a self-organizing mapping neural network algorithm to obtain a risk hotspot map; determine the location of the weak link in the risk hotspot map through a preset risk assessment algorithm, and monitor the weak link at the weak link location to obtain weak link data.

[0148] Optionally, the weak link data generation unit specifically includes: a risk hotspot map generation subunit, which is used to: perform nonlinear dimensionality reduction mapping on the node risk score through a self-organizing mapping neural network algorithm to obtain the risk distribution of each node on the mapping grid; use the kernel density estimation method to calculate the relative risk density of each node in the corresponding neighborhood based on the risk distribution of each node to obtain a risk density map of each node; perform local in-depth analysis on the node density distribution in the risk density map of each node to obtain a preliminary risk area division map, wherein the preliminary risk area division map is used to distinguish different risk level areas in the target distribution network; optimize the risk area boundaries of the preliminary risk area division map to obtain a risk hotspot map.

[0149] Optionally, the emergency response strategy generation module 330 specifically includes: an emergency response strategy generation unit, which is used to: determine the weak link type corresponding to the weak link data; perform semantic analysis on the weak link type to obtain a semantic feature vector; screen the historical emergency strategies in a preset database based on the semantic feature vector to obtain a candidate strategy set; and generate an emergency response strategy based on the candidate strategy set.

[0150] Optionally, the emergency response strategy generation unit specifically includes: a weak link type determination subunit, which is used to: extract features from the weak link data to obtain a weak link vector, wherein the weak link vector includes a voltage fluctuation vector, a harmonic content vector, a power factor vector and a load imbalance vector; generate a time-frequency feature vector based on the weak link vector, and perform nonlinear dynamic modeling on the time-frequency feature vector to obtain a dynamic modeling vector; perform fault mode identification on the weak link location based on the dynamic modeling vector through a preset fuzzy inference algorithm to obtain a fault mode identification result; and dynamically classify the fault mode identification result to obtain a weak link type.

[0151] The technical solution of the embodiment of the present invention locates weak links through the preliminary state matrix to obtain weak link data, which can quickly and accurately find the parts with problems in the distribution network, thereby improving the efficiency of problem discovery. An emergency response strategy is generated based on the weak link data, so that effective response measures can be taken quickly when a failure or abnormality occurs in the distribution network, thereby improving the overall operational reliability of the distribution network and ensuring the stability of power supply. It can dynamically track changes in the distribution network and adapt to the ever-changing power grid operating environment and needs.

[0152] A monitoring system for weak links in a distribution network provided in an embodiment of the present invention can execute a monitoring method for weak links in a distribution network provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0153] Embodiment 4

[0154] Figure 4 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0155] like Figure 4As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0156] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0157] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a monitoring method for weak links in a distribution network.

[0158] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0159] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A monitoring method for weak links in a distribution network. It is characterized in that include: Obtaining operation-related data of the target distribution network; Performing a preliminary state assessment on each node of the target distribution network based on the operation-related data to generate a preliminary state matrix; Based on the preliminary state matrix, weak link locations are performed to obtain weak link data, and an emergency response strategy for the target distribution network is generated according to the weak link data.

2. The method according to claim 1, It is characterized in that The obtaining of operation-related data of the target distribution network includes: Collecting data from each node of the target distribution network through a distributed sensor network to obtain original operation data, wherein the original operation data includes voltage data, current data, temperature data and load rate data; Performing wavelet transform denoising processing on the original operation data to generate wavelet transform denoised data; Performing time series frequency change recognition on the wavelet transform denoised data to generate a frequency change recognition result; When the frequency change recognition result is not within the preset frequency change range, generating time series frequency change data according to the frequency change recognition result, and adaptively sampling the time series frequency change data to obtain adaptive sampling data; When the frequency change identification result is within a preset frequency change range, generating data to be reconstructed according to the frequency change identification result; Reconstructing the adaptive sampling data and the data to be reconstructed to obtain reconstructed data; The reconstructed data is normalized to generate operation-related data.

3. The method according to claim 1, It is characterized in that The performing a preliminary state evaluation on each node of the target distribution network based on the operation-related data to generate a preliminary state matrix includes: performing spectrum analysis on the operation-related data to generate a frequency domain feature set; Performing frequency response analysis on each node of the target distribution network based on the frequency domain feature set to obtain a frequency response matrix; Performing singular value decomposition on the frequency response matrix to obtain a reduced-dimensional feature vector set; Performing trend analysis on the dimension-reduced feature vector set to obtain a trend analysis result; Using a preset long short-term memory network to perform similarity prediction and matching on the trend analysis results to obtain a similarity matrix; Based on the similarity matrix, a state correlation analysis is performed on each node of the target distribution network to obtain a state correlation matrix; Using a Bayesian network, based on the state association matrix, the health state probability of each node of the target distribution network is estimated to obtain a state assessment result corresponding to each node; The preliminary state matrix is ​​constructed according to the state evaluation results corresponding to each node.

4. The method according to claim 1, It is characterized in that The step of locating weak links based on the preliminary state matrix to obtain weak link data includes: The preliminary state matrix is ​​structured by a preset topology analysis algorithm to obtain a topology matrix of the target distribution network, wherein the topology matrix includes connection relationships between nodes, branch impedance parameters corresponding to each node, and electrical characteristics of each node; Performing network modeling on the target distribution network based on the topological structure matrix to obtain a network feature vector; Performing a weak link analysis on the target distribution network according to the network feature vector to obtain a node vulnerability index; Comprehensively evaluating each node of the target distribution network based on the node vulnerability index to obtain a risk score for each node; Performing spatial clustering analysis on the target distribution network according to the risk score of each node by using a self-organizing map neural network algorithm to obtain a risk hotspot map; The weak link location is determined in the risk hot spot map by a preset risk assessment algorithm, and weak link monitoring is performed at the weak link location to obtain weak link data.

5. The method according to claim 4, It is characterized in that The method of performing spatial clustering analysis on the target distribution network according to the risk score of each node by using a self-organizing map neural network algorithm to obtain a risk hotspot map includes: Performing nonlinear dimensionality reduction mapping on the node risk score by using a self-organizing map neural network algorithm to obtain the risk distribution of each node on the mapping grid; Using a kernel density estimation method, based on the risk distribution of each node, the relative risk density of each node in the corresponding neighborhood is calculated to obtain a risk density map of each node; Performing local depth analysis on the node density distribution in the risk density map of each node to obtain a preliminary risk area division map, wherein the preliminary risk area division map is used to distinguish different risk level areas in the target distribution network; The risk area boundaries of the preliminary risk area division map are optimized to obtain a risk hot spot map.

6. The method according to claim 1, It is characterized in that The generating of the emergency response strategy of the target distribution network according to the weak link data includes: Determine the weak link type corresponding to the weak link data; Performing semantic analysis on the weak link type to obtain a semantic feature vector; Based on the semantic feature vector, the historical emergency strategies in the preset database are screened to obtain a candidate strategy set; An emergency response strategy is generated according to the candidate strategy set.

7. The method according to claim 6, It is characterized in that The determining the weak link type corresponding to the weak link data includes: Extracting features from the weak link data to obtain a weak link vector, wherein the weak link vector includes a voltage fluctuation vector, a harmonic content vector, a power factor vector, and a load imbalance vector; Generate a time-frequency feature vector according to the weak link vector, and perform nonlinear dynamic modeling on the time-frequency feature vector to obtain a dynamic modeling vector; By using a preset fuzzy inference algorithm, fault mode identification is performed on the weak link location based on the dynamic modeling vector to obtain a fault mode identification result; The fault mode recognition results are dynamically classified to obtain weak link types.

8. A monitoring system for weak links in distribution networks. It is characterized in that include: An operation-related data acquisition module is used to acquire operation-related data of the target distribution network; A preliminary state matrix generating module, used for performing preliminary state evaluation on each node of the target distribution network based on the operation-related data to generate a preliminary state matrix; The emergency response strategy generation module is used to locate weak links based on the preliminary state matrix to obtain weak link data, and generate an emergency response strategy for the target distribution network according to the weak link data.

9. An electronic device, It is characterized in that The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method according to any one of claims 1 to 7.

10. A computer storage medium, It is characterized in that The computer storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method according to any one of claims 1 to 7 when executed.

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