Power distribution network safety protection resource configuration method, device, equipment and medium

Abstract

The invention discloses a power distribution network safety protection resource allocation method, device and equipment and a medium, and the method comprises the steps: carrying out the clustering analysis of a power grid operation state data set, and obtaining a plurality of types of operation conditions; determining a dynamic evolution path of the impact current in the power distribution network according to the power disturbance probability corresponding to each type of operation condition; probability distribution of the short-circuit current under different power grid topologies is identified according to the dynamic evolution path of the impact current, and then high-risk nodes are determined; performing short-circuit fault simulation at the high-risk node, and performing parameter optimization on the protection equipment which does not accord with a preset target to obtain new protection equipment parameters; analyzing the power flow distribution condition of the power distribution network based on the new protection equipment parameters, simulating the fault evolution process to obtain fault diffusion paths, and further performing protection resource configuration of the power distribution network by analyzing the risk level of each path. According to the invention, the protection capability of the power distribution network protection resources in the power disturbance scene and the power grid stability are improved.

Description

Technical Field

[0001] This invention relates to the field of power grid planning technology, and in particular to a method, device, equipment and medium for allocating security protection resources for distribution networks. Background Technology

[0002] With the large-scale integration of new energy sources, the operating environment of power distribution networks has become more complex. Due to the high uncertainty of new energy output, its power fluctuations may trigger inrush currents within the system, thereby threatening power distribution network equipment. This problem may also cause misalignment of the operating sequence of protection devices in the power grid, resulting in protection mechanisms failing to respond in a timely manner, further exacerbating the spread of faults and system instability.

[0003] Therefore, there is an urgent need for a distribution network security protection resource allocation method that can effectively ensure grid stability under new energy power disturbance scenarios. Summary of the Invention

[0004] To address the above technical problems, this invention provides a method, apparatus, equipment, and medium for configuring power distribution network security protection resources, which improves the protection capability of power distribution network protection resources under power disturbance scenarios and ensures power grid stability.

[0005] This invention provides a method for configuring security protection resources in a power distribution network, comprising: Obtain a power grid operation status dataset, perform cluster analysis on the power grid operation status dataset, and obtain several types of operating conditions; Based on the power disturbance probability corresponding to each type of operating condition, the dynamic evolution path of the inrush current in the distribution network is determined. Based on the dynamic evolution path of the impact current, the probability distribution of the short-circuit current under different power grid topologies is identified, and high-risk nodes are determined based on this probability distribution. Short-circuit fault simulation is performed at the high-risk node. Based on the protection action data of each protection device in the short-circuit fault simulation, the parameters of the protection devices that do not meet the preset target are optimized to obtain new protection device parameters. Based on the new protection equipment parameters, the power flow distribution of the distribution network is analyzed, the fault evolution process is simulated, and the fault propagation path is obtained. Based on the fault propagation path, the protection resources of the distribution network are configured by analyzing the risk level of each path.

[0006] As an improvement to the above solution, the step of acquiring power grid operation status data and performing cluster analysis on the power grid operation status data to obtain several types of operating conditions, including: Acquire historical operation data, power grid topology data, protection equipment status data, and fault recording data of the regional power grid to construct a power grid operation status dataset containing time series features; Cluster analysis is performed on the power grid operation status dataset to identify disturbance patterns under different operating conditions. The optimal number of clusters is determined by calculating the distance and profile coefficient between each cluster center, and the clustering results are obtained, where each cluster represents a typical operating condition.

[0007] As an improvement to the above scheme, determining the dynamic evolution path of the inrush current in the distribution network based on the power disturbance probability corresponding to each type of operating condition includes: For each type of operating condition, disturbance events are extracted from the fault waveform data of the corresponding time period, and the disturbance amplitude and duration of each disturbance event are calculated. The disturbance amplitude is the maximum deviation of the power value from the power mean. The probability density function of the disturbance amplitude and duration under each operating condition is fitted using the kernel density estimation method of Gaussian kernel function, and the intensity, dispersion of intensity and distribution type parameters of power disturbance under each operating condition are determined based on the probability density function. Based on the intensity, dispersion, and distribution type parameters of the power disturbance, combined with the pre-established grid impedance matrix and node admittance matrix, the support vector regression method is used to predict the impulse current response and obtain the impulse current amplitude range of each node. Based on the amplitude range of the impact current, the time variation curve of the impact current is calculated using a multi-node parallel computing architecture, and the attenuation coefficient and frequency characteristic parameters of the impact current are determined based on the time variation curve. Based on the attenuation coefficient and frequency characteristic parameters of the inrush current, a state equation describing the change of the amplitude and phase of the inrush current over time is constructed, and the dynamic evolution path of the inrush current among the branches of the distribution network is determined according to the state equation and the power grid topology.

[0008] As an improvement to the above scheme, the step of identifying the probability distribution of short-circuit current under different power grid topologies based on the dynamic evolution path of the impact current, and determining high-risk nodes based on this probability distribution, includes: Based on the dynamic evolution path of the impact current, the current propagation timing and amplitude attenuation law of each branch are extracted, and multiple short-circuit fault scenarios are randomly generated by combining the connection relationship between nodes under different power grid topologies. Based on the short-circuit fault scenario, a short-circuit current sample is obtained by simulation. The distribution of the short-circuit current amplitude of each node is statistically analyzed based on the short-circuit current sample to obtain the short-circuit current probability density function. Based on the short-circuit current probability density function, the short-circuit current samples are sorted from smallest to largest and the cumulative probability is calculated. The amplitude of the short-circuit current sample corresponding to the cumulative probability reaching a preset threshold is used as the first threshold, and the nodes with current amplitudes greater than the first threshold are used as candidate nodes. For each candidate node, the peak value of the short-circuit current, the duration of decay from the peak value to the steady state value, and the proportion of each harmonic component in the short-circuit current to the fundamental component are extracted as clustering feature vectors. Hierarchical clustering method is used to obtain the risk level classification of the node to determine high-risk nodes.

[0009] As an improvement to the above scheme, short-circuit fault simulation is performed at the high-risk node. Based on the protection action data of each protection device in the short-circuit fault simulation, the parameters of the protection devices that do not meet the preset target are optimized to obtain new protection device parameters, including: Based on the location of the high-risk nodes, the power grid topology data, and the status data of the protection equipment, a mapping relationship is established between each high-risk node and its upstream and downstream protection equipment, resulting in the protection equipment sequence associated with each high-risk node and the setting parameter group of the protection equipment. Based on the protection device sequence and the setting parameter group, a short-circuit fault is simulated at the high-risk node, the value of the fault current flowing through each protection device is calculated, the action sequence of the protection devices is determined, and the protection devices with misaligned timing and the action time difference are recorded. Based on the timing misalignment of the protection devices and the time difference between their actions, the current setting value and delay parameter of the protection devices are adjusted until the timing of the actions of the protection devices meets the preset target, thereby obtaining new protection device parameters.

[0010] As an improvement to the above scheme, the analysis of the power flow distribution of the distribution network based on the new protection equipment parameters, and the simulation of the fault evolution process to obtain the fault propagation path, includes: The protection action sequence is determined based on the new protection equipment parameters, and the steady-state power flow distribution of the distribution network is determined in combination with the power grid topology data; Based on the steady-state power flow distribution and the protection action sequence, an initial fault point is set in a multi-node parallel computing architecture. The protection device is simulated to disconnect the faulty branch. The power flow distribution after disconnecting the branch is recalculated. The new voltage values ​​of each node and the new power flow direction of each branch are recorded to obtain power flow transfer data. Based on the power flow transfer data, branches or nodes where power or voltage exceeds the limit are taken as potential fault propagation points. By recording the chain reaction sequence triggered by each action, a set of fault evolution paths with the initial fault point as the root node is formed. Based on the set of fault evolution paths, multiple simulations are performed by changing the initial fault point to calculate the occurrence frequency of each fault evolution path, and the fault propagation path is determined based on the occurrence frequency.

[0011] As an improvement to the above scheme, the configuration of protection resources for the distribution network based on the fault propagation path and by analyzing the risk level of each path includes: Obtain the probability distribution data of the fault propagation path, and extract the fault propagation path with a probability greater than a preset second threshold as a high-risk propagation channel based on the probability distribution data; The impact range of the high-risk propagation channel is determined based on the number of nodes and branch lengths of the high-risk propagation channel. The risk level of the high-risk propagation channel is calculated by multiplying the influence range of the high-risk propagation channel with the probability of the fault propagation path. Based on the risk level, for each high-risk transmission channel, assess the protection investment value after adding protective measures to the channel and the failure loss value of the channel, and determine the cost-benefit index of the high-risk transmission channel based on the protection investment value and the failure loss value. Based on the cost-benefit indicators in descending order, determine the protection resource configuration scheme for each high-risk transmission channel; the protection resource configuration scheme includes the number of circuit breakers configured at each node, the number of protection device upgrades for each branch, and the installation location of the current limiter.

[0012] The present invention also provides a power distribution network security protection resource allocation device, comprising: The data acquisition module is used to acquire a power grid operation status dataset, perform cluster analysis on the power grid operation status dataset, and obtain several types of operating conditions. The path evolution module is used to determine the dynamic evolution path of the inrush current in the distribution network based on the power disturbance probability corresponding to each type of operating condition. The high-risk node module is used to identify the probability distribution of short-circuit current under different power grid topologies based on the dynamic evolution path of the inrush current, and to determine high-risk nodes based on the probability distribution. The equipment parameter optimization module is used to simulate short-circuit faults at the high-risk nodes, and optimize the parameters of the protection devices that do not meet the preset targets based on the protection action data of each protection device in the short-circuit fault simulation, so as to obtain new protection device parameters. The fault evolution module is used to analyze the power flow distribution of the distribution network based on the new protection equipment parameters, simulate the fault evolution process, and obtain the fault propagation path. The protection resource configuration module is used to configure the protection resources of the distribution network based on the fault propagation path by analyzing the risk level of each path.

[0013] The present invention also provides a computer device, including a processor and a memory, wherein the memory stores a computer program and the computer program is configured to be executed by the processor, wherein the processor executes the computer program to implement the power distribution network security protection resource configuration method described in any of the preceding claims.

[0014] The present invention also provides a computer-readable storage medium storing a computer program, wherein the computer program, when running, controls the device where the computer-readable storage medium is located to execute the power distribution network security protection resource configuration method described above.

[0015] Compared to existing technologies, the beneficial effects of the distribution network security protection resource allocation method, device, equipment, and medium provided by this invention are as follows: By acquiring a power grid operating status dataset, performing cluster analysis on the dataset to obtain several operating conditions, and then determining the dynamic evolution path of the inrush current in the distribution network based on the power disturbance probability corresponding to each operating condition, identifying the probability distribution of short-circuit current under different power grid topologies, and identifying high-risk nodes based on this probability distribution, this invention can determine the inrush current evolution path for various operating conditions, making risk analysis more closely aligned with actual power grid operation, achieving short-circuit current prediction, improving the accuracy and proactivity of power grid risk identification, and also enabling the scientific positioning of key nodes. This allows for precise identification of weak links in the power grid and avoidance of... This avoids blindly expanding the protection scope. By simulating short-circuit faults at high-risk nodes and analyzing the protection action data of each protection device in the simulation, parameters of protection devices that do not meet the preset targets are optimized to obtain new protection device parameters. This enables precise resource optimization and significantly improves the reliability of protection device operation. Based on the new protection device parameters, the power flow distribution of the distribution network is analyzed, and the fault evolution process is simulated to obtain the fault propagation path. Then, by analyzing the risk level of each path, the protection resources of the distribution network are configured, constructing a closed-loop management mechanism for fault defense. This mechanism can perform differentiated resource allocation according to the risk level of the path, thereby maximizing the benefits of safety investment and effectively ensuring grid stability under new energy power disturbance scenarios. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a method for configuring security protection resources in a power distribution network, as provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of a power distribution network security protection resource allocation device provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figure 1 , Figure 1 This is a flowchart illustrating a method for configuring security protection resources in a power distribution network according to an embodiment of the present invention. The method includes: S1: Obtain the power grid operation status dataset, perform cluster analysis on the power grid operation status dataset, and obtain several types of operating conditions; S2: Determine the dynamic evolution path of the inrush current in the distribution network based on the power disturbance probability corresponding to each type of operating condition; S3: Based on the dynamic evolution path of the impact current, identify the probability distribution of the short-circuit current under different power grid topologies, and determine high-risk nodes based on the probability distribution; S4: Perform short-circuit fault simulation at the high-risk node. Based on the protection action data of each protection device in the short-circuit fault simulation, optimize the parameters of the protection devices that do not meet the preset target to obtain new protection device parameters. S5: Based on the parameters of the new protection equipment, analyze the power flow distribution of the distribution network, simulate the fault evolution process, and obtain the fault propagation path; S6: Based on the fault propagation path, the protection resources of the distribution network are configured by analyzing the risk level of each path.

[0019] Specifically, by collecting and clustering data on the regional power grid's operating status, different operating conditions are obtained; disturbance modes under different operating conditions are identified, and the probability density of disturbance amplitude and duration is analyzed to obtain a set of probability distribution features of power disturbances; then, based on the set of probability distribution features of power disturbances, an impulse current response prediction model is constructed to predict the amplitude range of impulse currents caused by disturbance modes. A multi-node parallel computing framework is introduced for time-domain and frequency-domain analysis. By fitting the attenuation characteristics and frequency characteristics of the impulse current, the dynamic evolution path of the impulse current is determined; based on the dynamic evolution path of the impulse current, the probability distribution of short-circuit current under different power grid topologies is identified, and then those exceeding a preset probability threshold are... Nodes are identified as high-risk nodes. Based on the distribution of high-risk nodes and the status data of protection equipment, scenarios with misaligned action timing are simulated. If the short-circuit current value exceeds the preset range, the action time of the protection equipment is optimized. If the action timing of the protection equipment does not meet expectations, the action threshold and delay are adjusted to dynamically correct the scenario and obtain the corrected protection equipment parameters. The power grid topology and power flow distribution corresponding to the corrected protection equipment parameters are analyzed to quickly simulate the fault evolution process and obtain the fault propagation path. High-risk channels are identified based on the distribution of fault propagation paths, and the distribution network security protection configuration is optimized according to the risk level of each path to realize the allocation of distribution network protection resources, thereby improving the power grid's safe operation level and system stability.

[0020] As one optional embodiment, step S1 involves acquiring power grid operating status data and performing cluster analysis on the power grid operating status data to obtain several types of operating conditions, including: Acquire historical operation data, power grid topology data, protection equipment status data, and fault recording data of the regional power grid to construct a power grid operation status dataset containing time series features; Cluster analysis is performed on the power grid operation status dataset to identify disturbance patterns under different operating conditions. The optimal number of clusters is determined by calculating the distance and profile coefficient between each cluster center, and the clustering results are obtained, where each cluster represents a typical operating condition.

[0021] Specifically, the power grid operation status dataset includes historical operation data of the regional power grid, power grid topology data, protection equipment status data, and fault recording data. The historical operation data of the regional power grid includes node voltage, branch current, active power, and reactive power. Node voltage data includes the phase and line voltages of each busbar; branch current data covers the three-phase current values ​​of each transmission line and transformer branch; and active and reactive power data reflect the power injection and outflow at each node. The power grid topology data includes node connection relationships, line parameters, and transformer parameters. This data describes the connectivity and parameters between components such as buses, lines, and transformers. The protection equipment status data records the operation and setting information of relay protection devices, mainly including relay protection, circuit breaker closing / opening, alarms, and event logs. The fault recording data records the changes in electrical quantities before and after a disturbance at a high sampling rate.

[0022] First, multi-dimensional data such as historical operation data of the regional power grid, power grid topology data, protection equipment status data, and fault recording data are collected. The collected multi-dimensional data are preprocessed to remove outliers and missing values, and an operation status dataset containing time series features is established.

[0023] Furthermore, K-means clustering analysis is performed on the operational status dataset. By calculating the Euclidean distance and silhouette coefficient between each cluster center, the optimal number of clusters is determined, and the operational condition classification results are obtained. Each cluster represents a typical operational condition. The silhouette coefficient is used to evaluate the clustering effect; the closer its value is to 1, the better the clustering effect. The optimal number of clusters is determined by comparing the silhouette coefficients under different k values.

[0024] In one specific implementation, K-means clustering analysis divides the running state data into several categories through iterative optimization. The clustering process first randomly selects k initial cluster centers, then calculates the Euclidean distance from each data point to each cluster center, assigns the data point to the category of the nearest cluster center, and then recalculates the center point of each category. This process is repeated until convergence.

[0025] As one optional embodiment, step S2, determining the dynamic evolution path of the inrush current in the distribution network based on the power disturbance probability corresponding to each type of operating condition, includes: For each type of operating condition, disturbance events are extracted from the fault waveform data of the corresponding time period, and the disturbance amplitude and duration of each disturbance event are calculated. The disturbance amplitude is the maximum deviation of the power value from the power mean. The probability density function of the disturbance amplitude and duration under each operating condition is fitted using the kernel density estimation method of Gaussian kernel function, and the intensity, dispersion of intensity and distribution type parameters of power disturbance under each operating condition are determined based on the probability density function. Based on the intensity, dispersion, and distribution type parameters of the power disturbance, combined with the pre-established grid impedance matrix and node admittance matrix, the support vector regression method is used to predict the impulse current response and obtain the impulse current amplitude range of each node. Based on the amplitude range of the impact current, the time variation curve of the impact current is calculated using a multi-node parallel computing architecture, and the attenuation coefficient and frequency characteristic parameters of the impact current are determined based on the time variation curve. Based on the attenuation coefficient and frequency characteristic parameters of the inrush current, a state equation describing the change of the amplitude and phase of the inrush current over time is constructed, and the dynamic evolution path of the inrush current among the branches of the distribution network is determined according to the state equation and the power grid topology.

[0026] Specifically, based on characteristic parameters such as voltage deviation, power factor, load factor, and frequency offset, disturbance modes under different operating conditions are identified. Voltage deviation reflects the degree of deviation of node voltage from its rated value; power factor reflects the level of reactive power consumption; load factor characterizes the load level of lines or transformers; and frequency offset reflects the stability of the system frequency. For each type of operating condition, disturbance events are extracted from fault waveform data of the corresponding time period, and the disturbance amplitude and duration of each event are calculated. The disturbance amplitude is defined as the maximum deviation of power from the average power value of the previous 30 seconds, and the duration is defined as the time interval from when the power deviation exceeds 5% of the average power value of the previous 30 seconds to when it recovers to within 2% of that average value. The extraction and feature calculation of disturbance events rely on accurate judgment of steady-state values. The average power value of the previous 30 seconds serves as a steady-state reference value, effectively filtering out the influence of short-term fluctuations. A disturbance is determined to begin when the power deviation exceeds 5% of the steady-state value and to end when it recovers to within 2%. This dual-threshold judgment method avoids misjudgment of disturbance boundaries. Furthermore, the probability density function of the disturbance amplitude and duration under each operating condition is fitted using the kernel density estimation method of the Gaussian kernel function. The kernel density estimation of the Gaussian kernel function is obtained by placing a Gaussian distribution function at each data point and then superimposing all Gaussian functions. The bandwidth parameter is determined by the cross-validation method, so that the density estimate is neither too smooth nor too coarse.

[0027] Furthermore, based on the probability density functions of disturbance amplitude and duration under various operating conditions, statistical characteristic parameters of power disturbances under each operating condition are calculated, including mean, variance, skewness, and kurtosis. Then, the parameters of the normal, Weibull, and log-normal distributions are calculated using the maximum likelihood estimation method. The optimal distribution type is determined through a chi-square goodness-of-fit test, resulting in a set of probability distribution characteristics of power disturbances corresponding to each operating condition. This set includes statistical parameters of the disturbance amplitude, namely the mean, variance, and distribution type parameters. These parameters directly reflect the disturbance patterns of the distribution network under different operating conditions. Specifically, the mean of the disturbance amplitude characterizes the average intensity of the disturbance under that condition, the variance reflects the dispersion of the disturbance intensity, and the distribution type parameters, such as the standard deviation of the normal distribution or the shape parameter of the Weibull distribution, describe the probabilistic characteristics of the disturbance occurrence. This step accurately identifies the statistical patterns of power disturbances under different operating conditions, providing reliable probability distribution characteristics for distribution network operation status assessment and fault prediction.

[0028] Specifically, the parameters of a normal distribution include the mean and standard deviation; the parameters of a Weibull distribution include shape and scale parameters; and the parameters of a log-normal distribution include the logarithmic mean and logarithmic standard deviation. Maximum likelihood estimation determines the distribution type by finding the parameter values ​​that maximize the probability of the observed data occurring. The chi-square goodness-of-fit test judges whether the data conforms to the assumed distribution by comparing the difference between the observed frequencies and the theoretical frequencies; the smaller the test statistic, the better the fit.

[0029] Furthermore, based on the intensity, dispersion, and distribution type parameters of the power disturbance, and combined with the pre-established grid impedance matrix and node admittance matrix, a support vector regression (SVR) method is used to construct an impulse current response prediction model. The input variables of this model include disturbance amplitude, disturbance duration, and inter-node impedance values, while the output variable is the impulse current amplitude range for each node. The grid impedance matrix records the electrical impedance relationship between nodes, and the node admittance matrix is ​​the inverse of the impedance matrix; both together describe the electrical connection characteristics of the distribution network. Specifically, the SVR method establishes a nonlinear mapping relationship between input and output by constructing a hyperplane in a high-dimensional feature space. The inter-node impedance values ​​in the input variables are extracted from the impedance matrix, reflecting the electrical distance between the disturbance source and the response point. A larger impedance value indicates a greater electrical distance and a more significant attenuation of the impulse current propagation. Historical data is used to train the impulse current response prediction model, learning the complex relationship between disturbance parameters and impulse current, and outputting the upper and lower limits of the possible impulse current amplitude for each node, forming an amplitude range interval.

[0030] Furthermore, based on the amplitude range of the impulse current, a multi-node parallel computing architecture is introduced. For each node, an impulse current differential equation is constructed, containing inductance, resistance, and capacitance parameters. The time-varying curve of the impulse current is obtained by numerical integration. Simultaneously, a fast Fourier transform is applied to this time-varying curve for frequency domain conversion, obtaining the amplitude and phase data of the fundamental frequency component and harmonic components. Based on the amplitude and phase data, the least squares method is used to fit the amplitude-frequency response curves and phase-frequency response curves at different frequencies, obtaining the frequency characteristic parameters of the impulse current. Specifically, the multi-node parallel computing architecture allows simultaneous calculations on multiple nodes, significantly improving computational efficiency. The impulse current differential equation is established based on Kirchhoff's laws. The inductance parameter in the equation reflects the inductance characteristics of the line and transformer, the resistance parameter reflects the conductor's resistance loss, and the capacitance parameter considers the line's capacitance to ground. Numerical integration methods, such as the Runge-Kutta method, solve the differential equation through iterative calculation, obtaining the continuous change curve of the impulse current over time. The Fast Fourier Transform (FFT) converts the time-domain signal to the frequency domain. The fundamental frequency component is typically 50Hz or 60Hz, while the harmonic components are integer multiples of the fundamental frequency. The amplitude and phase information of these frequency components reveal the spectral characteristics of the impulse current. The amplitude-frequency response curve describes the amplitude attenuation of different frequency components, while the phase-frequency response curve describes the phase shift characteristics. These frequency response parameters together constitute a complete description of the impulse current in the frequency domain, providing fundamental data for subsequent dynamic evolution analysis.

[0031] Furthermore, based on the time-varying curve of the impulse current, the time interval required for the impulse current to decay from its peak value to its steady-state value is identified. The attenuation coefficient and time constant are determined by fitting the impulse current envelope using an exponential function. The envelope connects the peak points of the impulse current waveform, exhibiting exponential decay characteristics; the attenuation coefficient determines the rate of attenuation; and the time constant represents the time required for the impulse current to decay to 36.8% of its initial value. Subsequently, based on the attenuation coefficient, time constant, and frequency characteristic parameters, a state equation describing the changes in the impulse current amplitude and phase over time is constructed. Combined with the network topology, the propagation sequence and propagation delay of the impulse current from the disturbance source node to adjacent nodes are determined, and the dynamic evolution path of the impulse current between branches is plotted.

[0032] As one optional embodiment, step S3, which involves identifying the probability distribution of the short-circuit current under different power grid topologies based on the dynamic evolution path of the impact current, and determining high-risk nodes based on this probability distribution, includes: Based on the dynamic evolution path of the impact current, the current propagation timing and amplitude attenuation law of each branch are extracted, and multiple short-circuit fault scenarios are randomly generated by combining the connection relationship between nodes under different power grid topologies. Based on the short-circuit fault scenario, a short-circuit current sample is obtained by simulation. The distribution of the short-circuit current amplitude of each node is statistically analyzed based on the short-circuit current sample to obtain the short-circuit current probability density function. Based on the short-circuit current probability density function, the short-circuit current samples are sorted from smallest to largest and the cumulative probability is calculated. The amplitude of the short-circuit current sample corresponding to the cumulative probability reaching a preset threshold is used as the first threshold, and the nodes with current amplitudes greater than the first threshold are used as candidate nodes. For each candidate node, the peak value of the short-circuit current, the duration of decay from the peak value to the steady state value, and the proportion of each harmonic component in the short-circuit current to the fundamental component are extracted as clustering feature vectors. Hierarchical clustering method is used to obtain the risk level classification of the node to determine high-risk nodes.

[0033] Specifically, the dynamic evolution path of the inrush current records the complete propagation process of the current in the distribution network. Based on the dynamic evolution path of the inrush current, the current propagation timing and amplitude attenuation law of each branch are extracted. Combining the connection relationship between nodes under different power grid topologies, a set of short-circuit fault scenarios is generated using the Monte Carlo method. The current amplitude when a short circuit occurs at each node under each topology is calculated. The number of samples in each amplitude interval is counted, and the frequency distribution is obtained by dividing by the total number of samples, thus obtaining the short-circuit current probability density function. Among them, the current propagation timing describes the time sequence of the inrush current spreading from the fault point to surrounding nodes; the amplitude attenuation law reflects the energy loss caused by line impedance during the propagation of the inrush current. The farther the node is from the fault point, the more obvious the amplitude attenuation of the inrush current. The connection relationship between nodes under the power grid topology determines the current propagation path. For example, the propagation characteristics of ring network structure and radial structure are significantly different.

[0034] The Monte Carlo method simulates the uncertainty of short-circuit faults through extensive random sampling. First, it determines the random parameters of fault occurrence, including the probability distribution of fault location at each node, the randomness of the fault occurrence time, and the volatility of the system's operating state. Then, in each simulation run, a set of parameter values ​​is randomly selected from these probability distributions, and the short-circuit current under that scenario is calculated. After tens of thousands of simulations, a large number of short-circuit current samples are obtained. Finally, these samples are divided into several intervals according to their amplitude, and the number of samples falling into each interval is counted. Dividing this number by the total number of samples yields the probability value for that interval, thus constructing a complete probability density function.

[0035] The simulation utilizes a multi-node parallel computing architecture to perform short-circuit current simulations on each node. Specifically, three-phase short circuits, two-phase short circuits, and single-phase-to-ground short circuits are defined, with the transition resistance randomly selected from zero ohms to a preset upper limit. The fault type directly affects the characteristics of the short-circuit current. Three-phase short circuits generate the largest short-circuit current, exhibiting symmetrical characteristics; two-phase short circuits have the next largest current amplitude, but exhibit an asymmetrical component; single-phase-to-ground short circuits are common in neutral-point grounded systems, and their current magnitude depends on the zero-sequence impedance.

[0036] Furthermore, the tail region is determined by calculating the cumulative probability of the short-circuit current amplitude. The cumulative probability is calculated by sorting the short-circuit current samples from smallest to largest. The cumulative probability of each sample point is equal to its index divided by the total number of samples. When the cumulative probability reaches a preset percentile value (such as 95% or 99%), the current amplitude corresponding to the cumulative probability is selected as the starting point of the tail region, and nodes exceeding the starting point are selected as candidate nodes.

[0037] For each candidate node, the peak short-circuit current, the duration of decay from the peak to the steady-state value, and the proportion of each harmonic component in the short-circuit current to the fundamental component are extracted as clustering feature vectors. A hierarchical clustering method is used to calculate the Euclidean distance between the feature vectors to construct a distance matrix. Based on a preset distance threshold, nearby nodes are merged to form different clusters, each representing a risk level. Furthermore, the risk level ranking of each cluster is determined by the feature mean of the cluster. Clusters with large peak short-circuit currents, long durations, and high harmonic content are classified as high-risk. On a pre-established power grid topology map containing node coordinates, nodes are assigned corresponding labels based on the risk level of their respective clusters, resulting in a distribution map of high-risk nodes. The selection of clustering feature vectors reflects the multidimensional characteristics of short-circuit current: the peak short-circuit current directly reflects the severity of the fault, the duration represents the time the protection device needs to withstand the impact, and the proportion of harmonic components represents the degree of distortion in the current waveform. Specifically, the hierarchical clustering method starts with each node as an independent cluster, calculates the Euclidean distance between the feature vectors of any two nodes, merges the two closest nodes into a new cluster, recalculates the inter-cluster distance, and iterates until all nodes are assigned to a set number of clusters.

[0038] As one optional embodiment, in step S4, short-circuit fault simulation is performed at the high-risk node. Based on the protection action data of each protection device in the short-circuit fault simulation, the parameters of the protection devices that do not meet the preset target are optimized to obtain new protection device parameters, including: Based on the location of the high-risk nodes, the power grid topology data, and the status data of the protection equipment, a mapping relationship is established between each high-risk node and its upstream and downstream protection equipment, resulting in the protection equipment sequence associated with each high-risk node and the setting parameter group of the protection equipment. Based on the protection device sequence and the setting parameter group, a short-circuit fault is simulated at the high-risk node, the value of the fault current flowing through each protection device is calculated, the action sequence of the protection devices is determined, and the protection devices with misaligned timing and the action time difference are recorded. Based on the timing misalignment of the protection devices and the time difference between their actions, the current setting value and delay parameter of the protection devices are adjusted until the timing of the actions of the protection devices meets the preset target, thereby obtaining new protection device parameters.

[0039] Specifically, the distribution map of high-risk nodes provides spatial distribution information on weak links in the distribution network. The risk level value of each node reflects the potential threat to the system when a fault occurs at that location. The risk level value is usually quantified from 1 to 10, where 10 represents the highest risk. This quantification method facilitates subsequent protection configuration optimization. Through the distribution map of high-risk nodes, the location identifier and risk level value of each high-risk node are extracted. This is combined with the current setting value, action delay time, and circuit breaker type recorded in the protection equipment status data. The protection equipment status data includes real-time operating parameters of the relay protection device; the current setting value is the action threshold of the protection device; the action delay time ensures the selectivity of the protection; and the circuit breaker type is related to the ability to interrupt fault current. Furthermore, according to the power grid topology, starting from the high-risk node, the corresponding upstream protection equipment is traced upstream along the transmission line, and the downstream protection equipment is located. A mapping relationship is established between each high-risk node and its upstream and downstream protection equipment. This mapping relationship forms a hierarchical structure of protection equipment. Each node is typically configured with main protection and backup protection. The main protection is responsible for quickly clearing the fault, and the backup protection operates when the main protection fails. This allows us to obtain the sequence of protection devices associated with each node and their setting parameter groups. The setting parameter groups contain complete configuration information for each protection device, and these parameters work together to form an overall protection scheme.

[0040] Furthermore, based on the protection device sequence and its setting parameter group, a short-circuit fault is simulated at a high-risk node. The fault current flowing through each protection device is calculated, and the action sequence is determined according to the current setting value and action delay time of each device. When the action time of the protection device closer to the fault point is longer than that of the protection device farther from the fault point, it is marked as a timing misalignment, and the difference between the timing misaligned protection device and the actual action time is recorded. It should be noted that the judgment of timing misalignment is based on the basic principle of protection coordination. Under normal circumstances, the protection device closest to the fault point should act first, so as to minimize the impact range of the fault. When the remote protection acts before the near protection, it will lead to a large-scale power outage. This phenomenon is called cascading tripping. The calculation of the actual action time difference is obtained by comparing the action times of each protection device. This time difference reflects the degree of irrationality of the current configuration and provides a quantitative basis for subsequent optimization.

[0041] Furthermore, for protection devices with misaligned timing and the actual time difference between their actions, it is determined whether the fault current exceeds a preset multiple of the rated current. If it does, a genetic algorithm is used to adjust the action delay of the protection devices. This algorithm aims to minimize the total action time while maintaining a preset millisecond difference between adjacent protection devices, outputting new delay parameters. If the fault current is lower than a preset multiple of the minimum operating current, the current setting value is increased proportionally and the corresponding delay is shortened. Finally, based on the new delay parameters and the adjusted current setting value, the action time of each protection device under the fault scenario is recalculated to verify whether the near-end protection acts before the far-end protection. If the action sequence is still reversed, the time coordination interval between adjacent protection devices is fine-tuned, iterating repeatedly until a tiered action sequence from near to far is formed. The output includes protection device parameters containing the new setting values ​​and delay parameters for each device. These optimized parameters can maintain the correct action sequence under various fault scenarios, effectively avoiding cascading tripping and failure to operate.

[0042] As one optional embodiment, step S5, which involves analyzing the power flow distribution of the distribution network based on the new protection device parameters, simulating the fault evolution process, and obtaining the fault propagation path, includes: The protection action sequence is determined based on the new protection equipment parameters, and the steady-state power flow distribution of the distribution network is determined in combination with the power grid topology data; Based on the steady-state power flow distribution and the protection action sequence, an initial fault point is set in a multi-node parallel computing architecture. The protection device is simulated to disconnect the faulty branch. The power flow distribution after disconnecting the branch is recalculated. The new voltage values ​​of each node and the new power flow direction of each branch are recorded to obtain power flow transfer data. Based on the power flow transfer data, branches or nodes where power or voltage exceeds the limit are taken as potential fault propagation points. By recording the chain reaction sequence triggered by each action, a set of fault evolution paths with the initial fault point as the root node is formed. Based on the set of fault evolution paths, multiple simulations are performed by changing the initial fault point to calculate the occurrence frequency of each fault evolution path, and the fault propagation path is determined based on the occurrence frequency.

[0043] Specifically, based on the new protection equipment parameters, the new setting parameters and operating sequence of each protection device are determined. The electrical connection relationships between nodes are extracted from the power grid operation database to form a node connection matrix. The resistance and reactance parameters of each branch are obtained. The power balance equation is solved iteratively using the Newton-Raphson method to calculate the voltage amplitude, phase angle of each node, and the active and reactive power values ​​of each branch, thus obtaining steady-state power flow distribution data. The node connection matrix is ​​a mathematical expression of the power grid topology, and the elements in the matrix indicate whether there is a direct electrical connection between nodes. Specifically, if there is a transmission line or transformer connection between node i and node j, the corresponding element in the matrix is ​​1; otherwise, it is 0. This binary matrix clearly describes the physical connection relationships of the distribution network, providing a network structure foundation for subsequent power flow calculations. The resistance and reactance parameters of the branches reflect the electrical characteristics of the lines; resistance affects active power loss, and reactance affects reactive power flow. In one possible implementation, the Newton-Raphson method calculates power flow distribution by iteratively solving a set of nonlinear power balance equations. This method first sets the initial values ​​of the voltage at each node, then calculates the power deviation, which is the difference between the injected power and the calculated power at the node. The nonlinear problem is linearized using the Jacobian matrix, and the voltage correction is solved. The node voltage is continuously updated until the power deviation is less than the convergence accuracy. The final voltage magnitude and phase angle determine the direction and magnitude of power flow in the network. Active power flows from nodes with leading phase angles to nodes with lagging phase angles, while reactive power flows from nodes with high voltage magnitudes to nodes with low voltage magnitudes.

[0044] Furthermore, based on the steady-state power flow distribution data and the protection action sequence, an initial fault point is set in the multi-node parallel computing architecture. The first protection device action is simulated to disconnect the faulty branch, updating the connectivity state of the corresponding branch in the node connection matrix. The power flow distribution after disconnecting the branch is recalculated, and the new voltage values ​​of each node and the new power flow direction of each branch are recorded to obtain the power flow transfer data after the first protection action. Then, based on the power flow transfer data, it is checked whether the power of each branch exceeds the preset multiple of the rated capacity and whether the voltage of each node is lower than the preset percentage of the rated value. If so, it indicates that there is an over-limit situation, and the branch or node is marked as a potential fault propagation point. Subsequent protection actions are simulated according to the protection action sequence, and the chain reaction sequence triggered by each action is recorded to form a set of fault evolution paths with the initial fault as the root node. Among them, the identification of potential fault propagation points is based on the load-bearing capacity limit of the equipment. Branch power exceeding the rated capacity will cause line heating, and long-term overload may cause new faults. Low node voltage will affect the normal operation of electrical equipment and may even lead to voltage collapse in severe cases.

[0045] It's important to note that topology changes caused by protection actions directly impact power flow distribution. When a branch is disconnected due to a fault, the power that originally flowed through that branch must be transmitted through other paths; this phenomenon is called power flow transfer. Updating the node connection matrix essentially involves setting the matrix elements corresponding to the disconnected branch to zero, indicating that the connection no longer exists. When recalculating the power flow, the system automatically searches for new power transmission paths, which may lead to a sharp increase in power on some branches, creating an overload risk.

[0046] Furthermore, based on the fault evolution path set, multiple simulations were conducted by changing the location, type, and severity parameters of the initial fault. The fault evolution path set exhibits a tree-like structure, with the initial fault as the root node. Each protection action may trigger new over-limit situations, forming branch nodes. By changing the initial conditions and conducting numerous simulations, it was found that certain evolution paths occur frequently, indicating that these paths are the main channels for fault propagation. By statistically analyzing the number of times each evolution path appears in all simulation scenarios and calculating the proportion of the path's occurrences to the total number of simulations as the path probability, and summarizing all paths and their corresponding probability values, the probability distribution data of fault propagation paths was obtained. This quantifies the probability of different propagation paths and provides a basis for formulating targeted preventive measures. High-probability paths often correspond to weak links in the power grid, requiring focused strengthening of protection configurations or optimization of network structure.

[0047] As one optional embodiment, step S6, which involves configuring the protection resources of the distribution network based on the fault propagation path by analyzing the risk level of each path, includes: Obtain the probability distribution data of the fault propagation path, and extract the fault propagation path with a path probability greater than a preset second threshold as a high-risk propagation channel based on the probability distribution data; The impact range of the high-risk propagation channel is determined based on the number of nodes and branch lengths of the high-risk propagation channel. The risk level of the high-risk transmission channel is calculated by multiplying the influence range of the high-risk transmission channel by the path probability. Based on the risk level, for each high-risk transmission channel, assess the protection investment value after adding protective measures to the channel and the failure loss value of the channel, and determine the cost-benefit index of the high-risk transmission channel based on the protection investment value and the failure loss value. Based on the cost-benefit indicators in descending order, a protection resource configuration scheme is determined for each high-risk transmission channel; the protection resource configuration scheme includes the number of circuit breakers configured at each node, the number of protection device upgrades for each branch, and the installation location of the current limiter.

[0048] Specifically, by analyzing the probability distribution data of fault propagation paths, when the probability of a certain propagation path exceeds a set threshold, it indicates that the path is easily activated under various fault scenarios and becomes the main channel for fault propagation. Therefore, paths with probability values ​​exceeding the preset threshold are extracted as high-risk propagation channels. The number of nodes involved in the path reflects the scope of the fault, while the branch length reflects the geographical span of the fault propagation. By calculating the number of nodes involved in each high-risk propagation channel and the branch length, and combining the product of the path probability value and the scope of influence, the risk level value is determined. The high-risk propagation channels are then sorted from high to low risk to form a list.

[0049] Furthermore, based on the list of high-risk propagation channels, three types of protective measures are assessed for each channel: adding circuit breakers, improving the sensitivity of relay protection devices, and installing fault current limiters. The total equipment procurement cost, installation cost, and maintenance cost of each measure are calculated as the protection investment. At the same time, the expected power outage loss value is calculated based on the fault probability and the number of affected users of the channel, thus obtaining the protection investment value and fault loss value for each channel.

[0050] The configuration of protective measures requires corresponding technical means to address different types of risks. Adding circuit breakers can quickly interrupt the current path when a fault occurs, preventing the fault from escalating; improving the sensitivity of relay protection devices can detect abnormal currents more quickly, shortening fault identification time; fault current limiters reduce the electromagnetic stress on equipment by limiting the peak value of short-circuit current. The assessment of fault loss involves multiple dimensions of economic impact. Direct losses include economic losses caused by production losses and inconvenience to users during power outages, which can be estimated using historical power outage data and regional economic indicators. The number of affected users includes not only those directly affected by power outages but also sensitive users affected by voltage quality degradation. The expected power outage loss is equal to the product of the fault probability, average outage time, number of affected users, and loss per unit time.

[0051] Furthermore, based on the protection investment value and the fault loss value, the residual risk value after taking protective measures is calculated. The risk reduction is obtained by subtracting the residual risk value from the original fault loss value. The ratio of the risk reduction to the protection investment value is then used as a cost-benefit indicator. Cost-benefit analysis optimizes resource allocation by comparing the input-output ratio of protective measures. The calculation of the residual risk value considers the effectiveness of the protective measures. Different protective measures have different effects on risk reduction; circuit breakers mainly reduce the probability of fault propagation, upgraded protection devices shorten the fault duration, and current limiters reduce the fault current amplitude. The risk reduction reflects the actual effect of the protective measures. The cost-benefit indicator compares this effect with the required investment; a higher indicator value indicates a better risk reduction effect per unit of investment.

[0052] Furthermore, protection resources are allocated based on the priority order of benefit index values ​​from highest to lowest. The number of circuit breakers configured at each node, the number of protection device upgrades for each branch, and the installation location of current limiters are recorded. A target protection resource distribution matrix is ​​constructed, with rows representing network locations, columns representing the three types of protection equipment, and element values ​​representing the configuration quantity. Specifically, the rows of the matrix correspond to various key locations in the distribution network, including important nodes and key branches; the columns correspond to the three types of protection equipment; and the element values ​​represent the number of devices configured at that location. This matrix representation clearly shows the spatial distribution of protection resources, facilitating the development of specific implementation plans. Then, based on the target protection resource distribution matrix, the optimal allocation of protection resources is achieved. By prioritizing the protection of key locations on high-risk channels, the maximum risk reduction effect is achieved within a limited investment budget, forming a scientifically sound and reasonable upgrade plan for the distribution network security protection system.

[0053] This invention, through obtaining a power grid operation status dataset, performs cluster analysis on the dataset to obtain several operating conditions. Then, based on the power disturbance probability corresponding to each operating condition, it determines the dynamic evolution path of the inrush current in the distribution network, identifies the probability distribution of short-circuit current under different power grid topologies, and identifies high-risk nodes based on this probability distribution. This allows for the determination of inrush current evolution paths for various operating conditions, making risk analysis more closely aligned with actual power grid operation. It achieves short-circuit current prediction, improves the accuracy and proactivity of distribution network risk identification, and enables the scientific positioning of key nodes. It can accurately pinpoint weak links in the power grid, avoiding blindly expanding the protection scope. Furthermore, by identifying high-risk nodes... Short-circuit fault simulation is conducted, and based on the protection action data of each protection device in the simulation, the parameters of protection devices that do not meet the preset targets are optimized to obtain new protection device parameters. This enables precise resource optimization and significantly improves the reliability of protection device operation. Based on the new protection device parameters, the power flow distribution of the distribution network is analyzed, the fault evolution process is simulated, and the fault propagation path is obtained. Then, by analyzing the risk level of each path, the protection resources of the distribution network are configured, constructing a closed-loop management mechanism for fault defense. This mechanism can perform differentiated resource allocation according to the risk level of the path, thereby maximizing the benefits of safety investment and effectively ensuring the stability of the distribution network under the scenario of new energy power disturbance.

[0054] Accordingly, the present invention also provides a power distribution network security protection resource configuration device, which can realize all the processes of the power distribution network security protection resource configuration method in the above embodiments.

[0055] Please see Figure 2 , Figure 2 This is a schematic diagram of a power distribution network security protection resource configuration device provided in an embodiment of the present invention. The power distribution network security protection resource configuration device includes: Data acquisition module 201 is used to acquire power grid operation status dataset, perform cluster analysis on the power grid operation status dataset, and obtain several types of operation conditions; The path evolution module 202 is used to determine the dynamic evolution path of the inrush current in the distribution network based on the power disturbance probability corresponding to each type of operating condition. The high-risk node module 203 is used to identify the probability distribution of short-circuit current under different power grid topologies based on the dynamic evolution path of the impact current, and to determine high-risk nodes based on the probability distribution. The equipment parameter optimization module 204 is used to simulate short circuit faults at the high-risk node, and optimize the parameters of the protection devices that do not meet the preset target based on the protection action data of each protection device in the short circuit fault simulation, so as to obtain new protection device parameters. The fault evolution module 205 is used to analyze the power flow distribution of the distribution network based on the new protection equipment parameters, simulate the fault evolution process, and obtain the fault propagation path. The protection resource configuration module 206 is used to configure the protection resources of the distribution network based on the fault propagation path by analyzing the risk level of each path.

[0056] Preferably, the data acquisition module 201 is specifically used for: Acquire historical operation data, power grid topology data, protection equipment status data, and fault recording data of the regional power grid to construct a power grid operation status dataset containing time series features; Cluster analysis is performed on the power grid operation status dataset to identify disturbance patterns under different operating conditions. The optimal number of clusters is determined by calculating the distance and profile coefficient between each cluster center, and the clustering results are obtained, where each cluster represents a typical operating condition.

[0057] Preferably, the path evolution module 202 is specifically used for: For each type of operating condition, disturbance events are extracted from the fault waveform data of the corresponding time period, and the disturbance amplitude and duration of each disturbance event are calculated. The disturbance amplitude is the maximum deviation of the power value from the power mean. The probability density function of the disturbance amplitude and duration under each operating condition is fitted using the kernel density estimation method of Gaussian kernel function, and the intensity, dispersion of intensity and distribution type parameters of power disturbance under each operating condition are determined based on the probability density function. Based on the intensity, dispersion, and distribution type parameters of the power disturbance, combined with the pre-established grid impedance matrix and node admittance matrix, the support vector regression method is used to predict the impulse current response and obtain the impulse current amplitude range of each node. Based on the amplitude range of the impact current, the time variation curve of the impact current is calculated using a multi-node parallel computing architecture, and the attenuation coefficient and frequency characteristic parameters of the impact current are determined based on the time variation curve. Based on the attenuation coefficient and frequency characteristic parameters of the inrush current, a state equation describing the change of the amplitude and phase of the inrush current over time is constructed, and the dynamic evolution path of the inrush current among the branches of the distribution network is determined according to the state equation and the power grid topology.

[0058] Preferably, the high-risk node module 203 is specifically used for: Based on the dynamic evolution path of the impact current, the current propagation timing and amplitude attenuation law of each branch are extracted, and multiple short-circuit fault scenarios are randomly generated by combining the connection relationship between nodes under different power grid topologies. Based on the short-circuit fault scenario, a short-circuit current sample is obtained by simulation. The distribution of the short-circuit current amplitude of each node is statistically analyzed based on the short-circuit current sample to obtain the short-circuit current probability density function. Based on the short-circuit current probability density function, the short-circuit current samples are sorted from smallest to largest and the cumulative probability is calculated. The amplitude of the short-circuit current sample corresponding to the cumulative probability reaching a preset threshold is used as the first threshold, and the nodes with current amplitudes greater than the first threshold are used as candidate nodes. For each candidate node, the peak value of the short-circuit current, the duration of decay from the peak value to the steady state value, and the proportion of each harmonic component in the short-circuit current to the fundamental component are extracted as clustering feature vectors. Hierarchical clustering method is used to obtain the risk level classification of the node to determine high-risk nodes.

[0059] Preferably, the device parameter optimization module 204 is specifically used for: Based on the location of the high-risk nodes, the power grid topology data, and the status data of the protection equipment, a mapping relationship is established between each high-risk node and its upstream and downstream protection equipment, resulting in the protection equipment sequence associated with each high-risk node and the setting parameter group of the protection equipment. Based on the protection device sequence and the setting parameter group, a short-circuit fault is simulated at the high-risk node, the value of the fault current flowing through each protection device is calculated, the action sequence of the protection devices is determined, and the protection devices with misaligned timing and the action time difference are recorded. Based on the timing misalignment of the protection devices and the time difference between their actions, the current setting value and delay parameter of the protection devices are adjusted until the timing of the actions of the protection devices meets the preset target, thereby obtaining new protection device parameters.

[0060] Preferably, the fault evolution module 205 is specifically used for: The protection action sequence is determined based on the new protection equipment parameters, and the steady-state power flow distribution of the distribution network is determined in combination with the power grid topology data; Based on the steady-state power flow distribution and the protection action sequence, an initial fault point is set in a multi-node parallel computing architecture. The protection device is simulated to disconnect the faulty branch. The power flow distribution after disconnecting the branch is recalculated. The new voltage values ​​of each node and the new power flow direction of each branch are recorded to obtain power flow transfer data. Based on the power flow transfer data, branches or nodes where power or voltage exceeds the limit are taken as potential fault propagation points. By recording the chain reaction sequence triggered by each action, a set of fault evolution paths with the initial fault point as the root node is formed. Based on the set of fault evolution paths, multiple simulations are performed by changing the initial fault point to calculate the occurrence frequency of each fault evolution path, and the fault propagation path is determined based on the occurrence frequency.

[0061] Preferably, the protection resource configuration module 206 is specifically used for: Obtain the probability distribution data of the fault propagation path, and extract the fault propagation path with a probability greater than a preset second threshold as a high-risk propagation channel based on the probability distribution data; The impact range of the high-risk propagation channel is determined based on the number of nodes and branch lengths of the high-risk propagation channel. The risk level of the high-risk propagation channel is calculated by multiplying the influence range of the high-risk propagation channel with the probability of the fault propagation path. Based on the risk level, for each high-risk transmission channel, assess the protection investment value after adding protective measures to the channel and the failure loss value of the channel, and determine the cost-benefit index of the high-risk transmission channel based on the protection investment value and the failure loss value. Based on the cost-benefit indicators in descending order, determine the protection resource configuration scheme for each high-risk transmission channel; the protection resource configuration scheme includes the number of circuit breakers configured at each node, the number of protection device upgrades for each branch, and the installation location of the current limiter.

[0062] In specific implementation, the working principle, control process and technical effects of the power distribution network security protection resource configuration device provided in the embodiments of the present invention are the same as those of the power distribution network security protection resource configuration method in the above embodiments, and will not be repeated here.

[0063] See Figure 3 , Figure 3This is a structural block diagram of a computer device provided in an embodiment of the present invention. The computer device includes: a processor 301, a memory 302, and a computer program stored in the memory 302 and executable on the processor 301. When the processor 301 executes the computer program, it implements the steps in the above-described embodiment of the power distribution network security protection resource configuration method. Alternatively, when the processor 301 executes the computer program, it implements the functions of each module / unit in the above-described device embodiments.

[0064] For example, the computer program may be divided into one or more modules / units, which are stored in the memory 302 and executed by the processor 301 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the computer device.

[0065] The computer device may include, but is not limited to, processor 301 and memory 302. Those skilled in the art will understand that the schematic diagram is merely an example of a computer device and does not constitute a limitation on the computer device. It may include more or fewer components than illustrated, or combine certain components, or different components. For example, the computer device may also include input / output devices, network access devices, buses, etc.

[0066] The processor 301 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor 301 is the control center of the computer device, connecting various parts of the entire computer device through various interfaces and lines.

[0067] The memory 302 can be used to store the computer programs and / or modules. The processor 301 implements various functions of the computer device by running or executing the computer programs and / or modules stored in the memory 302 and calling the data stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory 302 may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0068] Wherein, if the modules / units integrated into the computer device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor 301, it can implement the steps of the various method embodiments described above. Wherein, the computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0069] This invention also provides a computer-readable storage medium, which includes a stored computer program, wherein the computer program, when running, controls the device where the computer-readable storage medium is located to execute the power distribution network security protection resource configuration method described in any of the above embodiments.

[0070] This invention provides a method, device, equipment, and medium for allocating security protection resources in a distribution network. Its beneficial effects are as follows: By acquiring a power grid operating status dataset, performing cluster analysis on the dataset to obtain several operating conditions, and then determining the dynamic evolution path of inrush current in the distribution network based on the power disturbance probability corresponding to each operating condition, the invention identifies the probability distribution of short-circuit current under different power grid topologies and identifies high-risk nodes based on this probability distribution. This allows for the determination of inrush current evolution paths for various operating conditions, making risk analysis more closely aligned with the actual operation of the distribution network, achieving short-circuit current prediction, improving the accuracy and proactivity of distribution network risk identification, and enabling the scientific positioning of key nodes. It can accurately pinpoint weak links in the power grid and avoid blindly predicting risks. Expanding the protection scope; by simulating short-circuit faults at high-risk nodes, and based on the protection action data of each protection device in the short-circuit fault simulation, the parameters of protection devices that do not meet the preset targets are optimized to obtain new protection device parameters, enabling precise resource optimization and significantly improving the operational reliability of protection devices; based on the new protection device parameters, the power flow distribution of the distribution network is analyzed, the fault evolution process is simulated, the fault propagation path is obtained, and then the protection resources of the distribution network are configured by analyzing the risk level of each path, thus constructing a closed-loop management mechanism for fault defense. This mechanism can perform differentiated resource configuration according to the risk level of the path, thereby maximizing the benefits of safety investment and effectively ensuring the stability of the distribution network under the scenario of new energy power disturbance.

[0071] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for allocating security protection resources in a power distribution network, characterized in that, include: Obtain a power grid operation status dataset, perform cluster analysis on the power grid operation status dataset, and obtain several types of operating conditions; Based on the power disturbance probability corresponding to each type of operating condition, the dynamic evolution path of the inrush current in the distribution network is determined. Based on the dynamic evolution path of the impact current, the probability distribution of the short-circuit current under different power grid topologies is identified, and high-risk nodes are determined based on this probability distribution. Short-circuit fault simulation is performed at the high-risk node. Based on the protection action data of each protection device in the short-circuit fault simulation, the parameters of the protection devices that do not meet the preset target are optimized to obtain new protection device parameters. Based on the new protection equipment parameters, the power flow distribution of the distribution network is analyzed, the fault evolution process is simulated, and the fault propagation path is obtained. Based on the fault propagation path, the protection resources of the distribution network are configured by analyzing the risk level of each path.

2. The method for allocating distribution network security protection resources as described in claim 1, characterized in that, The process involves acquiring power grid operation status data, performing cluster analysis on the data to obtain several categories of operating conditions, including: Acquire historical operation data, power grid topology data, protection equipment status data, and fault recording data of the regional power grid to construct a power grid operation status dataset containing time series features; Cluster analysis is performed on the power grid operation status dataset to identify disturbance patterns under different operating conditions. The optimal number of clusters is determined by calculating the distance and profile coefficient between each cluster center, and the clustering results are obtained, where each cluster represents a typical operating condition.

3. The method for allocating distribution network security protection resources as described in claim 1, characterized in that, The determination of the dynamic evolution path of the inrush current in the distribution network based on the power disturbance probability corresponding to each type of operating condition includes: For each type of operating condition, disturbance events are extracted from the fault waveform data of the corresponding time period, and the disturbance amplitude and duration of each disturbance event are calculated. The disturbance amplitude is the maximum deviation of the power value from the power mean. The probability density function of the disturbance amplitude and duration under each operating condition is fitted using the kernel density estimation method of Gaussian kernel function, and the intensity, dispersion of intensity and distribution type parameters of power disturbance under each operating condition are determined based on the probability density function. Based on the intensity, dispersion, and distribution type parameters of the power disturbance, combined with the pre-established grid impedance matrix and node admittance matrix, the support vector regression method is used to predict the impulse current response and obtain the impulse current amplitude range of each node. Based on the amplitude range of the impact current, the time variation curve of the impact current is calculated using a multi-node parallel computing architecture, and the attenuation coefficient and frequency characteristic parameters of the impact current are determined based on the time variation curve. Based on the attenuation coefficient and frequency characteristic parameters of the inrush current, a state equation describing the change of the amplitude and phase of the inrush current over time is constructed, and the dynamic evolution path of the inrush current among the branches of the distribution network is determined according to the state equation and the power grid topology.

4. The method for allocating distribution network security protection resources as described in claim 1, characterized in that, The step of identifying the probability distribution of short-circuit current under different power grid topologies based on the dynamic evolution path of the impact current, and determining high-risk nodes based on this probability distribution, includes: Based on the dynamic evolution path of the impact current, the current propagation timing and amplitude attenuation law of each branch are extracted, and multiple short-circuit fault scenarios are randomly generated by combining the connection relationship between nodes under different power grid topologies. Based on the short-circuit fault scenario, a short-circuit current sample is obtained by simulation. The distribution of the short-circuit current amplitude of each node is statistically analyzed based on the short-circuit current sample to obtain the short-circuit current probability density function. Based on the short-circuit current probability density function, the short-circuit current samples are sorted from smallest to largest and the cumulative probability is calculated. The amplitude of the short-circuit current sample corresponding to the cumulative probability reaching a preset threshold is used as the first threshold, and the nodes with current amplitudes greater than the first threshold are used as candidate nodes. For each candidate node, the peak value of the short-circuit current, the duration of decay from the peak value to the steady state value, and the proportion of each harmonic component in the short-circuit current to the fundamental component are extracted as clustering feature vectors. Hierarchical clustering method is used to obtain the risk level classification of the node to determine high-risk nodes.

5. The method for allocating security protection resources for power distribution networks as described in claim 1, characterized in that, The process involves simulating short-circuit faults at high-risk nodes. Based on the protection action data of each protection device in the short-circuit fault simulation, the parameters of protection devices that do not meet the preset targets are optimized to obtain new protection device parameters, including: Based on the location of the high-risk nodes, the power grid topology data, and the status data of the protection equipment, a mapping relationship is established between each high-risk node and its upstream and downstream protection equipment, resulting in the protection equipment sequence associated with each high-risk node and the setting parameter group of the protection equipment. Based on the protection device sequence and the setting parameter group, a short-circuit fault is simulated at the high-risk node, the value of the fault current flowing through each protection device is calculated, the action sequence of the protection devices is determined, and the protection devices with misaligned timing and the action time difference are recorded. Based on the timing misalignment of the protection devices and the time difference between their actions, the current setting value and delay parameter of the protection devices are adjusted until the timing of the actions of the protection devices meets the preset target, thereby obtaining new protection device parameters.

6. The method for allocating security protection resources for power distribution networks as described in claim 1, characterized in that, The analysis of power flow distribution in the distribution network based on the new protection equipment parameters, and the simulation of the fault evolution process to obtain the fault propagation path, includes: The protection action sequence is determined based on the new protection equipment parameters, and the steady-state power flow distribution of the distribution network is determined in combination with the power grid topology data; Based on the steady-state power flow distribution and the protection action sequence, an initial fault point is set in a multi-node parallel computing architecture. The protection device is simulated to disconnect the faulty branch. The power flow distribution after disconnecting the branch is recalculated. The new voltage values ​​of each node and the new power flow direction of each branch are recorded to obtain power flow transfer data. Based on the power flow transfer data, branches or nodes where power or voltage exceeds the limit are taken as potential fault propagation points. By recording the chain reaction sequence triggered by each action, a set of fault evolution paths with the initial fault point as the root node is formed. Based on the set of fault evolution paths, multiple simulations are performed by changing the initial fault point to calculate the occurrence frequency of each fault evolution path, and the fault propagation path is determined based on the occurrence frequency.

7. The method for allocating distribution network security protection resources as described in claim 1, characterized in that, The configuration of protection resources for the distribution network based on the fault propagation path and by analyzing the risk level of each path includes: Obtain the probability distribution data of the fault propagation path, and extract the fault propagation path with a probability greater than a preset second threshold as a high-risk propagation channel based on the probability distribution data; The impact range of the high-risk propagation channel is determined based on the number of nodes and branch lengths of the high-risk propagation channel. The risk level of the high-risk propagation channel is calculated by multiplying the influence range of the high-risk propagation channel with the probability of the fault propagation path. Based on the risk level, for each high-risk transmission channel, assess the protection investment value after adding protective measures to the channel and the failure loss value of the channel, and determine the cost-benefit index of the high-risk transmission channel based on the protection investment value and the failure loss value. Based on the cost-benefit indicators in descending order, determine the protection resource configuration scheme for each high-risk transmission channel; the protection resource configuration scheme includes the number of circuit breakers configured at each node, the number of protection device upgrades for each branch, and the installation location of the current limiter.

8. A power distribution network security protection resource allocation device, characterized in that, include: The data acquisition module is used to acquire a power grid operation status dataset, perform cluster analysis on the power grid operation status dataset, and obtain several types of operating conditions. The path evolution module is used to determine the dynamic evolution path of the inrush current in the distribution network based on the power disturbance probability corresponding to each type of operating condition. The high-risk node module is used to identify the probability distribution of short-circuit current under different power grid topologies based on the dynamic evolution path of the inrush current, and to determine high-risk nodes based on the probability distribution. The equipment parameter optimization module is used to simulate short-circuit faults at the high-risk nodes, and optimize the parameters of the protection devices that do not meet the preset targets based on the protection action data of each protection device in the short-circuit fault simulation, so as to obtain new protection device parameters. The fault evolution module is used to analyze the power flow distribution of the distribution network based on the new protection equipment parameters, simulate the fault evolution process, and obtain the fault propagation path. The protection resource configuration module is used to configure the protection resources of the distribution network based on the fault propagation path by analyzing the risk level of each path.

9. A computer device, characterized in that, The device includes a processor and a memory, wherein the memory stores a computer program and the computer program is configured to be executed by the processor, wherein the processor executes the computer program to implement the power distribution network security protection resource configuration method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the device containing the computer-readable storage medium executes the computer program, it implements the power distribution network security protection resource configuration method as described in any one of claims 1 to 7.

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