Safety evaluation system and method for main grounding grid of transformer substation

Abstract

The invention relates to the technical field of substations, and discloses a safety assessment system and method for a main grounding grid of a substation. The method comprises the following steps: arranging a multi-point potential gradient sensor for a main grounding grid of a power transmission and transformation project transformer substation and carrying out synchronous measurement to obtain an original potential gradient data set; performing effective signal and interference signal distinguishing processing on the original potential gradient data set to obtain potential distribution information of the transformer substation grounding grid; establishing a transformer substation grounding grid dynamic impedance model for fault current analysis to obtain a whole-process potential distribution diagram including an initial stage, a steady-state stage and a recovery stage; and performing security risk assessment based on the whole-process potential distribution diagram, and outputting a security risk area report. According to the invention, effective measurement signals and interference signals are effectively distinguished, reliability and accuracy of grounding grid potential measurement data in a complex electromagnetic environment are significantly improved, and accurate positioning of a risk area and effective tracking of a hazard source are realized.

Description

Technical Field

[0001] This application relates to the technical field of substations, and particularly to a safety assessment system and method for the main grounding grid of a substation. Background Art

[0002] As a crucial safety facility in power transmission and transformation projects, the health status of the main grounding grid of a substation is directly related to the safe operation of the power system and personal safety protection. With the expansion of the power grid scale and the growth of load, the short-circuit fault current in the substation is increasing continuously, and the safety assessment of the grounding grid faces unprecedented challenges. The traditional grounding grid assessment methods mainly fall into two technical routes: frequency-domain measurement method and time-domain impulse method. However, these methods have obvious limitations under complex soil conditions. Due to limited measurement points, the data is sparse and cannot comprehensively reflect the actual state of the grounding grid. At the same time, it is difficult to effectively distinguish the power frequency interference signal from the effective measurement signal, and the background noise is high and the waveform distortion is serious. Especially under the condition of high-load operation of substation equipment, the interference signal is more complex and changeable.

[0003] More importantly, the existing grounding grid safety assessment methods mostly focus on static or steady-state characteristics and ignore the transient potential change law during the whole process of the fault. In particular, the dangerous potential areas that may appear during the short-circuit fault recovery stage have been ignored for a long time, and these areas often lead to excessive touch voltage and step voltage. In addition, the traditional research lacks comprehensive consideration of the seasonal change of soil resistivity, the influence of temperature and humidity, and the skin effect and proximity effect of conductors, resulting in a large deviation between the assessment result and the actual situation, and it is difficult to provide a reliable technical basis for improving the safety of the grounding grid. Summary of the Invention

[0004] This application provides a safety assessment system and method for the main grounding grid of a substation. This application effectively distinguishes the effective measurement signal from the interference signal, significantly improves the reliability and accuracy of the grounding grid potential measurement data in a complex electromagnetic environment, and realizes the precise positioning of risk areas and the effective tracking of hazard sources.

[0005] In the first aspect, this application provides a safety assessment method for the main grounding grid of a substation. The safety assessment method for the main grounding grid of a substation includes:

[0006] Set multi-point potential gradient sensors on the main grounding grid of the substation in the power transmission and transformation project, and perform synchronous measurement through the multi-point potential gradient sensors to obtain an original potential gradient data set;

[0007] Perform processing to distinguish the effective signal from the interference signal on the original potential gradient data set to obtain the potential distribution information of the substation grounding grid;

[0008] Establish a dynamic impedance model of the substation grounding grid according to the potential distribution information of the substation grounding grid;

[0009] The dynamic impedance model of the substation grounding grid is used for fault current analysis to obtain a full - process potential distribution diagram including the initial stage, steady - state stage, and recovery stage;

[0010] Based on the full - process potential distribution diagram, safety risk assessment is carried out to output a safety risk area report.

[0011] In a second aspect, the present application provides a safety assessment system for the main grounding grid of a substation. The safety assessment system for the main grounding grid of a substation includes:

[0012] A setting module, configured to set multi - point potential gradient sensors on the main grounding grid of the power transmission and transformation project substation, and perform synchronous measurement through the multi - point potential gradient sensors to obtain an original potential gradient data set;

[0013] A distinguishing and processing module, configured to distinguish and process the original potential gradient data set into effective signals and interference signals to obtain the potential distribution information of the substation grounding grid;

[0014] A building module, configured to establish a dynamic impedance model of the substation grounding grid according to the potential distribution information of the substation grounding grid;

[0015] A fault current analysis module, configured to use the dynamic impedance model of the substation grounding grid for fault current analysis to obtain a full - process potential distribution diagram including the initial stage, steady - state stage, and recovery stage;

[0016] A safety risk assessment module, configured to perform safety risk assessment based on the full - process potential distribution diagram and output a safety risk area report.

[0017] In the technical solution provided by the present application, through the multi - point potential gradient synchronous measurement data acquisition technology, high - density and high - time - accuracy monitoring of the potential distribution of the substation grounding grid is realized, overcoming the limitation of sparse data in traditional measurement methods. By using the potential gradient signal recognition and screening method based on deep learning, the effective measurement signal and interference signal are effectively distinguished, significantly improving the reliability and accuracy of the grounding grid potential measurement data in a complex electromagnetic environment. The constructed hierarchical soil resistivity dynamic impedance model takes into account the influence of temperature, humidity, and seasonal changes on soil resistivity, as well as the skin effect and proximity effect of conductors, making the characterization of the impedance characteristics of the substation grounding grid more accurate. The transient analysis method of the grounding grid fault current distribution first pays attention to the secondary potential peak phenomenon in the fault recovery stage, reveals the safety risk points ignored by traditional methods, and comprehensively evaluates the change of the potential distribution during the whole process of the fault. The risk assessment method based on the three - dimensional safety risk assessment matrix realizes the precise positioning of the risk area and the effective tracking of the hazard source. Description of the Drawings

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

[0019] Figure 1 It is a schematic diagram of an embodiment of the method for safety assessment of the main grounding grid of a substation in an embodiment of the present application;

[0020] Figure 2 It is a schematic diagram of an embodiment of the system for safety assessment of the main grounding grid of a substation in an embodiment of the present application. Detailed implementation manners

[0021] The embodiments of the present application provide a system and method for safety assessment of the main grounding grid of a substation. The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims and the above accompanying drawings of the present application are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "comprising" or "having" and any variation thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily limit to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0022] For ease of understanding, the following describes the specific process of the embodiments of the present application. Please refer to Figure 1 , an embodiment of the method for safety assessment of the main grounding grid of a substation in an embodiment of the present application includes:

[0023] Step S101: Set multi-point potential gradient sensors on the main grounding grid of the substation of the power transmission and transformation project, and perform synchronous measurement through the multi-point potential gradient sensors to obtain an original potential gradient data set;

[0024] It can be understood that the execution subject of the present application can be a system for safety assessment of the main grounding grid of a substation, or a terminal or a server. Specifically, it is not limited here. The embodiments of the present application take the server as the execution subject as an example for illustration.

[0025] Specifically, bipolar probe structure potential gradient sensors are arranged around and at the internal target nodes of the grounding grid of a substation in a power transmission and transformation project. The probe structure is made of stainless steel alloy, and its surface is plated with a platinum layer to ensure the stability and corrosion resistance of measurement. The probe spacing is set to 1 meter, and a reasonable topological layout is formed according to the structural characteristics of the grounding grid. The layout density of measurement points in the area near the grounding downlead is not less than 2 points per square meter, while in other areas it remains not less than 0.5 points per square meter, thus forming a high-density, multi-point coverage potential gradient measurement network. Connect the potential gradient sensors in the measurement network to the central data acquisition unit. The acquisition unit uses a 24-bit high-precision analog-to-digital converter, and the sampling rate is set to 10 kHz, which is much higher than the 100 Hz sampling rate of traditional methods, ensuring that potential changes at the microsecond level can be captured. At the same time, the entire system is synchronized and clock-calibrated through GPS timing technology, so that the measurement time error of all sensors is controlled within 50 nanoseconds, forming a multi-point synchronous measurement system. After completing the synchronous clock calibration, a test excitation current signal of 77 Hz and 5 amperes is injected at the preset positions in the substation through the multi-point synchronous measurement system. The injection duration is 2 seconds to excite the grounding grid of the substation, and the potential gradient changes at each measurement point during this period are captured by the potential gradient sensors. The potential gradient change signals generated by the test excitation signal are collected by the analog-to-digital converter, and a complete data set is automatically stored every 0.2 seconds during the collection process to ensure the integrity and accuracy of the measurement data. In order to accurately record the external environmental conditions corresponding to the measurement data, the atmospheric temperature, humidity, surface temperature at the measurement moment and the operating status of key target equipment inside the substation are synchronously recorded during the measurement process. These data serve as environmental parameter matching data and are matched and integrated with the original measurement data. By combining and processing the original measurement data and the environmental parameter matching data, an original potential gradient data set containing time tags, position coordinates, potential gradient amplitudes, phase information and environmental information is formed.

[0026] Step S102: Distinguish between effective signals and interference signals in the original potential gradient data set to obtain the potential distribution information of the grounding grid of the substation;

[0027] Specifically, the original potential gradient data set is input into a well-trained multi-layer convolutional neural network model, which includes multiple convolutional layers, pooling layers, and fully connected layers. Local features of the data are extracted through convolutional operations, and then the features are dimension-reduced through the pooling layer to reduce redundant information. After integrating global features through the fully connected layer, the model accurately identifies the effective signals and interference signals in the potential gradient data and generates recognition result data. For the interference signals identified in the recognition result data, an adaptive wavelet threshold filtering technique is used for suppression. This technique automatically adjusts the filtering threshold according to the local statistical characteristics of the signals. At the same time, for low-frequency interference signals with a long time scale, smoothing processing is performed through a high-order sliding median filter, so that the low-frequency noise in the data is effectively suppressed, thereby retaining the detailed features of the original signal to the greatest extent. After suppressing the interference signals, the potential gradient data after noise reduction is obtained. The potential gradient data after noise reduction is subjected to frequency domain conversion, the amplitude and phase information of the test signal are extracted from it, and the phase difference and amplitude change relationship between each measurement point are calculated to obtain a spatial distribution matrix representing the potential gradient change relationship of each measurement point. Through eigenvector analysis of the spatial distribution matrix, the main eigenvectors and corresponding eigeninformation are extracted, and they are sorted according to the importance of the features. The first several eigenvectors whose proportion exceeds the preset target value are retained according to the eigenvalue contribution rate. These retained eigenvectors reflect the core information of the potential gradient distribution of the grounding grid, and the potential distribution information of the substation grounding grid is obtained.

[0028] Step S103: Establish a dynamic impedance model of the substation grounding grid according to the potential distribution information of the substation grounding grid;

[0029] Specifically, soil parameter inversion is carried out based on the potential distribution information of the substation grounding grid. Through data fitting and model solving methods, the thickness and resistivity parameters of the soil are inverted. The soil is divided into three layers: the surface layer, the intermediate layer, and the deep layer. The thickness and resistivity parameters of each layer are solved by fitting according to the measurement data, reflecting the electrical characteristics of the soil at different depths. To improve the accuracy of the model, temperature coefficient and humidity coefficient compensation calculations are performed on the inverted soil thickness and resistivity parameters. By introducing a temperature and humidity compensation model, the influence of environmental temperature and humidity changes on soil resistivity is incorporated into the model, and a dynamically changing soil resistivity model is obtained. Based on the temperature and humidity compensated soil resistivity model, a three-dimensional modeling of the substation grounding grid area is carried out. The finite element mesh generation technology is used to divide the grounding grid area into a large number of hexahedral mesh elements. The mesh size near the grounding body is set to a smaller 0.1 m to improve the calculation accuracy, while the mesh size in the area far from the grounding body gradually increases to 1 m, forming a fine meshed three-dimensional model. And a complex impedance representation equation is established according to this meshed three-dimensional model, so as to consider the impedance characteristics at different frequencies. Since the impedance characteristics of the substation grounding grid are affected by the skin effect and proximity effect, in order to more accurately describe the characteristics of the conductor under high-frequency current, a refined model of the grounding grid metal conductor considering the skin effect and proximity effect is constructed according to the complex impedance representation equation. This model simulates the current distribution characteristics by dividing the conductor cross-section into multiple units, thereby improving the modeling accuracy. To improve the adaptability of the model, a seasonal variation correction coefficient is introduced to reflect the variation laws of soil resistivity and grounding impedance under different seasonal conditions. The value range of the correction coefficient is between 0.8 and 1.2, taking the minimum value in summer and the maximum value in winter, so as to obtain a grounding grid model that dynamically reflects seasonal variations. The parameters of the seasonally corrected grounding grid model are optimized and iterated by the genetic algorithm. The genetic algorithm simulates the process of natural evolution, continuously adjusts the model parameters to approach the optimal solution, and the number of iterations is set to 1000 times. The convergence condition is that the parameter change rate of continuous multiple iterations is less than 0.1%, so as to finally obtain an accurate dynamic impedance model of the substation grounding grid, reflecting the grounding grid impedance characteristics under different frequencies and different seasonal conditions.

[0030] The metal conductor of the grounding grid is divided into grids according to the complex impedance characterization equation, and the conductor cross-section is divided into multiple segmented units. Through the fine dissection of the segmented unit model, the non-uniform distribution of current on the conductor cross-section is described under the action of high-frequency current. Electromagnetic field calculation is carried out on the refined conductor cross-section model, and the current density distribution function representing the skin effect is obtained by using the electromagnetic field solution method. This function reflects the distribution characteristic that as the current frequency increases, the current gradually concentrates on the surface layer of the conductor, thus forming the skin phenomenon. Based on the current density distribution function, the electromagnetic coupling effect between adjacent conductors is calculated, and the conductor mutual impedance matrix representing the proximity effect is constructed, which reflects the impedance change characteristic caused by the mutual coupling effect between different conductors. Seasonal factor analysis is carried out on the conductor mutual impedance matrix. Considering that the soil resistivity fluctuates significantly with the changes of temperature and humidity, a seasonal correction parameter set is introduced to dynamically correct the conductor mutual impedance characteristics under different seasonal conditions. To improve the model accuracy, the soil resistivity is corrected according to the seasonal correction parameter set and the conductor mutual impedance matrix, and a seasonal soil resistivity model reflecting the influence of seasonal changes is obtained. This model accurately describes the impedance change characteristics of the soil under different temperature and humidity conditions. After comprehensively processing the seasonal soil resistivity model, the segmented unit model of the conductor cross-section, the current density distribution function representing the skin effect, and the conductor mutual impedance matrix representing the proximity effect, a seasonally corrected grounding grid model is obtained, which reflects the electromagnetic characteristics of the grounding grid under different seasonal conditions and describes the change of conductor current distribution caused by the skin effect and the proximity effect.

[0031] Step S104: Use the dynamic impedance model of the substation grounding grid for fault current analysis to obtain the whole-process potential distribution diagram including the initial stage, the steady state stage and the recovery stage;

[0032] Specifically, based on the constructed dynamic impedance model, the fault current is decomposed and disassembled into the superposition form of the DC component and the AC component. The DC component reflects the large current transient process at the initial stage of the fault, while the AC component reflects the AC characteristics that gradually decay over time. After decomposition, the fault current function is obtained. The fault current function is decomposed by time step, and the entire analysis time period is divided into multiple equally spaced time step sequences. The length of each time step does not exceed 100 microseconds to ensure accurate capture of the transient change characteristics of the fault current at each stage. For each time point in the decomposed time step sequence, the nodal potential method is used to establish the grid equation of the grounding grid. This grid equation relates the potential changes of each node in the grounding grid to the injected current in matrix form. At the same time, to improve the calculation efficiency, the frequency-domain impedance data is converted into the time-domain response through the fast convolution algorithm to obtain the transient response equation set of the grounding grid. According to the obtained transient response equation set, the transient potential values of each node in the grounding grid and the potential difference between adjacent nodes are calculated step by step in time to obtain the distribution data of each component of the fault current in the grounding grid. Based on the distribution data of the fault current components, the surface electric field intensity is calculated, and the change of the potential gradient is intuitively reflected by the spatial distribution of the electric field intensity. At the same time, to accurately identify the secondary potential peak phenomenon that appears in the fault recovery stage, the maximum potential gradient monitoring algorithm is used for dynamic tracking. When the potential gradient is redistributed due to electromagnetic induction or impedance change during the system recovery process, the monitoring algorithm effectively captures this key phenomenon and records the corresponding potential gradient distribution data. Based on the obtained potential gradient distribution data, the entire fault process is analyzed, focusing on the potential distribution characteristics in the initial stage, steady state stage, and recovery stage, and the migration law of the maximum potential gradient position is analyzed in combination with the change of the nodal potential to form the whole-process potential distribution map.

[0033] Step S105: Perform a safety risk assessment based on the whole-process potential distribution map and output a safety risk area report.

[0034] Specifically, by analyzing the potential variation of the main grounding grid of a substation during the entire fault process through the potential distribution map of the whole process, combining the characteristics of potential gradient distribution, the potential difference between adjacent nodes, and the electric field intensity data, a safety risk assessment is carried out to obtain the space-time risk distribution data covering the entire substation area. This data reflects the risk distribution at different spatial positions and includes the risk evolution trend at each time step. Perform three-dimensional K-means clustering analysis on the space-time risk distribution data to divide the entire risk data set into several risk clustering regions. Different clustering regions represent risk characteristic regions with similar spatial proximity and similar temporal variation patterns. The value of K is automatically adjusted through the silhouette coefficient to ensure that the clustering result reflects the true risk distribution characteristics to the greatest extent. For the identified set of risk clustering regions, calculate the space-time risk density of each clustering region. By comparing the space-time risk density with a preset density threshold, identify the set of high-risk regions. These high-risk regions correspond to the paths where the fault current concentrates, weak points of the grounding grid, or regions with drastic potential gradient changes. To clarify the formation reasons and potential risk sources of the high-risk regions, conduct a hazard source tracking analysis on the identified set of high-risk regions. Use an improved path backtracking algorithm to track the propagation path of the fault current in the grounding grid, and identify the target path of the fault current that causes the risk increase in this region and the information of the key target nodes it passes through. These target nodes are the nodes where the current density abnormally increases or the potential gradient changes significantly. Focusing on monitoring and analyzing these nodes helps to clarify the fundamental source of the fault risk. Based on the identified set of high-risk regions, the target path of the fault current, and the target node information, comprehensively analyze the key information such as the spatial distribution, risk level, over-standard degree, and duration of each risk region, generate a safety risk region report, record the specific location, risk level, fault path characteristics, and key node information of each high-risk region, and put forward corresponding safety protection suggestions for different types of risk regions.

[0035] In the embodiments of the present application, through the multi-point potential gradient synchronous measurement data acquisition technology, the high-density and high-time-precision monitoring of the potential distribution of the substation grounding grid is realized, overcoming the limitation of sparse data in traditional measurement methods. By using the potential gradient signal recognition and screening method based on deep learning, the effective measurement signals and interference signals are effectively distinguished, significantly improving the reliability and accuracy of the grounding grid potential measurement data in a complex electromagnetic environment. The constructed dynamic impedance model of layered soil resistivity takes into account the influence of temperature, humidity and seasonal changes on soil resistivity, as well as the skin effect and proximity effect of conductors, making the characterization of the impedance characteristics of the substation grounding grid more accurate. The transient analysis method of the fault current distribution in the grounding grid first focuses on the secondary potential peak phenomenon in the fault recovery stage, reveals the safety risk points ignored by traditional methods, and comprehensively evaluates the potential distribution changes during the whole process of the fault. The risk assessment method based on the three-dimensional safety risk assessment matrix realizes the precise positioning of the risk area and the effective tracking of the hazard sources.

[0036] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0037] Potential gradient sensors with bipolar probe structures are arranged according to the topological structure at the target nodes around and inside the substation grounding grid of the power transmission and transformation project to obtain a measurement network;

[0038] Connect the potential gradient sensors in the measurement network to the central data acquisition unit, and perform synchronous clock calibration through GPS timing technology to obtain a multi-point synchronous measurement system;

[0039] Inject test excitation conditions at the preset positions of the substation through the multi-point synchronous measurement system;

[0040] Under the test excitation conditions, use an analog-to-digital converter to collect the potential gradient changes of all measurement points to obtain the original measurement data;

[0041] Mark the original measurement data with time, and synchronously record the atmospheric temperature, humidity, surface temperature and the operating status of the target equipment in the substation at the measurement moment to obtain the environment parameter matching data;

[0042] Combine and process the original measurement data and the environment parameter matching data to generate the original potential gradient data set.

[0043] Specifically, sensors are arranged within the grounding grid area of a substation in a power transmission and transformation project. Potential gradient sensors with a bipolar probe structure are arranged around the perimeter of the substation grounding grid and at key internal target nodes. These sensors are made of stainless steel alloy material, and a platinum layer is plated on the probe surface to ensure high corrosion resistance and excellent measurement stability during long-term operation. The probe spacing of each bipolar probe is set to 1 meter to ensure that the measurement accuracy meets the engineering requirements. At the same time, the sensor layout is carried out according to the topological structure of the grounding grid, so that the measurement point density in the grounding lead area is not less than 2 points per square meter, and in other areas, it remains no less than 0.5 points per square meter, forming a high-density potential gradient measurement network covering the entire substation grounding grid. Connect the potential gradient sensors in the measurement network to the central data acquisition unit. This data acquisition unit uses a 24-bit high-precision analog-to-digital converter, which has higher measurement accuracy and anti-interference ability. The sampling rate is set to 10 kHz to capture potential gradient change data at the microsecond level. To ensure the synchronization of data acquisition at each measurement point, the entire system is clock-synchronized and calibrated through GPS timing technology. The GPS timing system controls the clock error of all sensors within 50 nanoseconds to achieve a multi-point synchronous measurement system. After completing the clock synchronization and calibration, test excitation conditions are injected at the preset positions in the substation through this multi-point synchronous measurement system. During the excitation process, a test current signal with a frequency of 77 Hz and a magnitude of 5 amperes is injected into different positions of the substation through a preset program, and the injection duration is 2 seconds. This excitation signal forms a stable potential gradient change field in the grounding grid, so as to capture the potential changes at each measurement point during this period through the arranged potential gradient sensors. Under the test excitation conditions, the analog-to-digital converter is used to perform real-time data acquisition on the potential gradient changes at all measurement points. The original measurement data is stored with high precision at 10 kHz, and during the acquisition process, a complete data set is automatically stored every 0.2 seconds to ensure that no key measurement information is missed during the test. To more accurately analyze the influence of environmental factors during the potential gradient change process and effectively compensate the measurement data for the environment, time stamps are added to the original measurement data, and the atmospheric temperature, humidity, surface temperature, and operating status of key target equipment in the substation are synchronously recorded at the measurement moment. These environmental parameters are bound to the original measurement data as matching data to generate an environmental parameter matching data set. The original measurement data and the environmental parameter matching data are combined and processed to generate an original potential gradient data set, which includes time stamps, position coordinates, potential gradient amplitudes, phase information, and corresponding environmental parameter information.

[0044] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0045] Input the original potential gradient data set into a multi-layer convolutional neural network model for signal feature extraction to obtain recognition result data;

[0046] Suppress the interference signals in the recognition result data to obtain the potential gradient data after noise reduction;

[0047] Perform a fast Fourier transform on the potential gradient data after noise reduction to obtain frequency-domain feature data;

[0048] Calculate the phase difference and amplitude ratio between each measurement point based on the frequency-domain feature data to obtain a spatial distribution matrix characterizing the potential gradient relationship between each measurement point;

[0049] Perform eigenvector analysis on the spatial distribution matrix to obtain the eigen-decomposition result;

[0050] Based on the eigen-decomposition result, retain the first k eigenvectors with a contribution rate exceeding the preset target value to obtain the potential distribution information of the substation grounding grid.

[0051] Specifically, the original potential gradient data set is input into a well-trained multi-layer convolutional neural network model. This model has been trained with a large number of samples, including various typical potential gradient signal sample data, such as normal signals, power frequency interference signals, lightning interference signals, and switch operation interference signals. After model training and parameter optimization, the model can identify and extract the main signal features in the original data. The convolutional neural network model includes 5 convolutional layers, 3 pooling layers, and 2 fully connected layers. The convolutional layers perform multi-scale feature extraction on the input data, and the pooling layers are used for dimensionality reduction to reduce the computational amount. The fully connected layers integrate the features and perform signal classification to generate the recognition result data. The recognition result data contains the feature information of the effective signal and can also mark different types of interference signals. For the interference signals in the recognition result data, interference suppression processing is performed. The identified interference signals are effectively suppressed by the adaptive wavelet threshold filtering technology. This technology automatically adjusts the filtering threshold according to the local characteristics of the signal, thereby maximizing the retention of the information of the effective signal while suppressing the interference. For low-frequency interference signals with a long time scale, a high-order sliding median filter is used for smoothing processing to effectively suppress the low-frequency noise in the data and generate the denoised potential gradient data. The fast Fourier transform is performed on the denoised potential gradient data to convert the time-domain signal into a frequency-domain signal. The Fourier transform extracts the amplitude and phase information of different frequency components, calculates the phase difference and amplitude ratio between each measurement point, and the frequency-domain feature data can more clearly characterize the variation law of the potential gradient signal in space. Through the analysis of the frequency-domain feature data, a spatial distribution matrix representing the potential gradient relationship between each measurement point is constructed. The elements of this spatial distribution matrix represent the potential gradient relationship between different measurement points. The rows and columns of the matrix correspond to different measurement points respectively, and the data in the matrix reflect the distribution of the amplitude ratio and phase difference of the potential gradient between the measurement points, forming a spatial feature description. In order to extract the key feature information hidden in the spatial distribution matrix, eigenvector analysis is performed on the spatial distribution matrix. A set of eigenvectors and corresponding eigenvalues are extracted through matrix eigenvalue decomposition. These eigenvectors reflect the main patterns and change trends of the grounding grid potential distribution, and the magnitude of the eigenvalue is used to measure the importance of the corresponding eigenvector. In order to optimize the data, the eigenvectors with a high contribution rate are retained. According to the eigenvalue decomposition result, the first k eigenvectors with a contribution rate exceeding the preset target value are retained. These eigenvectors accurately describe the potential gradient distribution information of the grounding grid, thereby forming a reduced-dimensional feature matrix and generating an accurate potential distribution information data set.

[0052] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0053] Invert the soil parameters based on the potential distribution information of the substation grounding grid to obtain the soil thickness and resistivity parameters;

[0054] Calculate the temperature coefficient and humidity coefficient of soil thickness and resistivity parameters to obtain a soil resistivity model with temperature and humidity compensation;

[0055] Based on the soil resistivity model with temperature and humidity compensation, perform three-dimensional modeling to obtain a three-dimensional grid model of the substation grounding grid area, and establish a complex impedance characterization equation according to the three-dimensional grid model;

[0056] Construct a refined model of the grounding grid metal conductor considering the skin effect and proximity effect according to the complex impedance characterization equation, and introduce a seasonal variation correction coefficient to obtain a seasonally corrected grounding grid model;

[0057] Through the genetic algorithm, perform parameter optimization iteration on the seasonally corrected grounding grid model to obtain the dynamic impedance model of the substation grounding grid.

[0058] Specifically, based on the potential distribution information of the substation grounding grid, soil parameter inversion is carried out. During the inversion process, by matching the measured potential gradient change data with the theoretical calculation model, the soil in the grounding grid area is divided into three-layer structures: the surface layer, the intermediate layer, and the deep layer. The thickness of each layer of soil and the corresponding resistivity parameters are inversely obtained, and these parameters reflect the electrical characteristics of the soil at different depths. To improve the accuracy of the soil resistivity model, temperature coefficient and humidity coefficient compensation calculations are performed on the inversely obtained soil thickness and resistivity parameters. By introducing a temperature and humidity correction model, the influence of ambient temperature and humidity changes on soil resistivity is incorporated into the calculation scope, forming a dynamically changing soil resistivity model. As the temperature increases, the soil resistivity shows a downward trend, and when the humidity increases, the soil resistivity also decreases accordingly. By combining the temperature coefficient and humidity coefficient, the change law of soil resistivity under different environmental conditions is accurately reflected. Based on the soil resistivity model after temperature and humidity compensation, a three-dimensional model of the substation grounding grid area is established. The finite element mesh division technology is used to divide the entire grounding grid area into multiple hexahedral grid units. Among them, the grid division density near the grounding body is higher, and the unit size reaches 0.1 meters, while in the area far from the grounding body, the grid size gradually increases to 1 meter, forming a high-precision meshed three-dimensional model. According to this meshed three-dimensional model, a complex impedance characterization equation is established. By introducing complex impedance to represent the impedance characteristics of the grounding grid area under different frequency conditions, this equation reflects the resistance characteristics under low-frequency conditions and captures the inductance and capacitance effects under high-frequency conditions. To more accurately describe the current distribution characteristics of the metal conductors in the grounding grid, a refined model of the grounding grid metal conductors considering the skin effect and proximity effect is constructed based on the complex impedance characterization equation. The skin effect causes high-frequency currents to concentrate on the surface layer of the conductor, while the proximity effect reflects the change in current distribution due to electromagnetic coupling between adjacent conductors. When constructing the refined model, the conductor cross-section is divided into multiple segmented units, and by calculating the current density distribution of different segmented units, the electromagnetic characteristics of the conductor at different frequencies are more accurately reflected. At the same time, to improve the adaptability and accuracy of the model, a seasonal change correction coefficient is introduced to reflect the change laws of soil resistivity and grounding impedance under different seasonal conditions. The value range of this correction coefficient is between 0.8 and 1.2. In winter, due to the decrease in soil humidity and the increase in resistivity, the correction coefficient takes the maximum value, while in summer, due to the high soil humidity, the correction coefficient takes the minimum value, thus obtaining a grounding grid model that dynamically reflects the influence of seasonal changes.In order to optimize the model parameters and make the dynamic impedance model more in line with the actual working conditions, the parameters of the seasonally corrected grounding grid model are optimized and iterated by the genetic algorithm. The genetic algorithm optimizes the model parameters by simulating the natural evolution process. The initial population consists of a set of random parameter combinations. By continuously performing crossover, mutation, and selection, it gradually approaches the optimal solution. In the iterative process of each generation, the fitness function is calculated to judge the fitting degree between the current parameter combination and the actual measurement data. After multiple iterations, when the parameter change rate for multiple consecutive generations is less than the set threshold, it can be judged that the model converges. Finally, an optimized dynamic impedance model of the substation grounding grid is obtained, which can accurately reflect the impedance characteristics of the grounding grid under different frequencies and different seasonal conditions.

[0059] In a specific embodiment, the process of performing the steps of constructing a refined model of the grounding grid metal conductor considering the skin effect and proximity effect according to the complex impedance characterization equation and introducing a seasonal change correction coefficient to obtain a seasonally corrected grounding grid model can specifically include the following steps:

[0060] Subdivide the grounding grid metal conductor according to the complex impedance characterization equation to obtain a segmented unit model of the conductor cross-section;

[0061] Perform electromagnetic field calculations on the segmented unit model of the conductor cross-section to obtain a current density distribution function characterizing the skin effect;

[0062] Calculate the electromagnetic coupling effect between adjacent conductors based on the current density distribution function characterizing the skin effect to obtain a conductor mutual impedance matrix characterizing the proximity effect;

[0063] Conduct seasonal factor analysis on the conductor mutual impedance matrix characterizing the proximity effect to obtain a set of season correction parameters;

[0064] According to the set of season correction parameters and the conductor mutual impedance matrix characterizing the proximity effect, perform correction calculations on the soil resistivity to obtain a seasonal soil resistivity model;

[0065] Integrate the seasonal soil resistivity model with the segmented unit model of the conductor cross-section, the current density distribution function characterizing the skin effect, and the conductor mutual impedance matrix characterizing the proximity effect to obtain a seasonally corrected grounding grid model.

[0066] Specifically, according to the complex impedance characterization equation, the metal conductors in the grounding grid are finely divided into grids. By dividing the conductor cross-section into multiple segmented unit models, the distribution characteristics of high-frequency current on the conductor cross-section can be more accurately described. The segmented unit model can fully consider the change of current density at different positions and the complex change of the conductor cross-section under different electromagnetic environments. After grid subdivision, the conductor cross-section is divided into multiple independent units, and the current density of each unit is calculated independently, so as to more precisely capture the non-uniformity characteristics of current distribution. Based on the conductor cross-section model after grid subdivision, electromagnetic field calculation is carried out. By solving the Maxwell equations, the current density distribution function characterizing the skin effect is obtained. The skin effect is a phenomenon in which the alternating current generates an induced electromagnetic field in the conductor, causing the current to gradually concentrate on the surface layer of the conductor, thus forming a phenomenon that the current density rapidly decays with the increase of distance. This current density distribution function accurately describes the difference between the current density on the conductor surface layer and the current density inside the conductor. After obtaining the current density distribution function of the skin effect, the electromagnetic coupling effect between adjacent conductors is further considered. By calculating the current distribution after refining the conductor cross-section, the electromagnetic mutual inductance and impedance change between adjacent conductors are calculated. This electromagnetic coupling effect forms the proximity effect, which will cause the current density between adjacent conductors to change, thus affecting the impedance characteristics of the conductor. By establishing the conductor mutual impedance matrix, the impedance change characteristics caused by the proximity effect between different conductors are described. Each element of this matrix represents the mutual impedance relationship between different conductors. Since the soil resistivity changes significantly with the change of temperature and humidity, seasonal factor analysis is carried out on the conductor mutual impedance matrix. By introducing a set of seasonal correction parameters to compensate for the change law of soil resistivity under different seasonal conditions, these seasonal correction parameter sets are obtained by fitting calculation based on historical meteorological data and the change law of soil resistivity. The correction parameter sets are dynamically adjusted according to the seasonal change, so as to ensure that the conductor mutual impedance matrix maintains high accuracy under different environmental conditions. According to the seasonal correction parameter sets and the conductor mutual impedance matrix, dynamic correction calculation is carried out on the soil resistivity to obtain a seasonal soil resistivity model, which reflects the change law of soil resistivity under different seasonal conditions, and the corrected soil resistivity information is introduced into the grounding grid impedance model, so that the model can maintain high calculation accuracy under different seasonal conditions. In order to form a seasonally corrected grounding grid model, the seasonal soil resistivity model is comprehensively processed with the previously established conductor cross-section segmented unit model, the current density distribution function characterizing the skin effect, and the conductor mutual impedance matrix characterizing the proximity effect. In the comprehensive processing process, the data of each part are mapped to a unified spatial coordinate, and dynamic parameter matching is carried out on the time axis, so as to ensure the compatibility and consistency between different data sources.By integrating multi-dimensional data such as soil resistivity, conductor current distribution, and electromagnetic coupling relationship, a seasonally corrected grounding grid model is obtained. This model accurately reflects the electromagnetic characteristics of the grounding grid under different seasons and different frequency conditions, and provides accurate data support for subsequent fault current analysis, transient current distribution simulation, and safety risk assessment.

[0067] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0068] Based on the dynamic impedance model of the substation grounding grid, the fault current is decomposed into the superposition form of the DC component and the AC component to obtain the fault current function;

[0069] The fault current function is decomposed by time step to obtain a time step sequence;

[0070] For each time point in the time step sequence, the node potential method is used to establish the grounding grid mesh equation, and the fast convolution algorithm is used to convert the frequency-domain impedance into the time-domain response to obtain the grounding grid transient response equation set;

[0071] According to the grounding grid transient response equation set, the transient potential values of each node of the grounding grid and the potential difference between adjacent nodes at each time step are calculated to obtain the fault current component distribution data;

[0072] Based on the fault current component distribution data, the surface electric field intensity is calculated, and the potential gradient maximum value monitoring algorithm is used to detect the secondary potential peak phenomenon in the fault recovery stage to obtain the potential gradient distribution data;

[0073] Based on the potential gradient distribution data, the potential distribution characteristics and the migration law of the maximum potential gradient position in the initial stage, steady state stage, and recovery stage are analyzed to obtain the whole-process potential distribution map.

[0074] Specifically, based on the dynamic impedance model of the substation grounding grid, the fault current is decomposed. By analyzing the current characteristics at the moment of fault occurrence, the fault current is disassembled into the superposition form of a DC component and an AC component. The DC component mainly reflects the rapidly decaying current formed after a large current is injected into the grounding grid in the initial stage of the fault, which has a short time constant and rapidly weakens. The AC component reflects the alternating current formed by the superposition of the power system's power frequency current as the fault process progresses. Its time constant is relatively long, and it maintains a relatively stable amplitude in the steady state. By decomposing the fault current into the superposition form of DC and AC components, the current characteristics at different stages are reflected. The fault current function is decomposed by time step. To improve the calculation accuracy and capture the details of transient potential changes, the entire analysis period is divided into multiple equally spaced time step sequences. The length of the time step is set to not exceed 100 microseconds to ensure high-precision capture of the dynamic change characteristics of the fault current, and a corresponding fault current component is established for each time step to form a time step sequence. For each time point in the time step sequence, the nodal potential method is used to establish the grid equation of the grounding grid. This equation couples the potential change of the grounding grid with the nodal admittance matrix and reflects the distribution of current in the grounding grid at different time steps by introducing the injection current vector. Since the dynamic impedance model is usually established in the frequency domain, in order to convert it into the data required for time-domain analysis, the frequency-domain impedance data is converted into the time-domain response through the fast convolution algorithm. The fast convolution algorithm maps the complex impedance matrix in the frequency domain into the time domain to form the transient response equation set of the grounding grid, which describes the transient potential changes of each node of the grounding grid at different time steps. According to the transient response equation set of the grounding grid, the transient potential values of each node of the grounding grid and the potential difference between adjacent nodes are calculated step by step in time. By analyzing the dynamic changes of each node's potential, the distribution data of each component of the fault current in the grounding grid is obtained, and the change characteristics of the potential gradient at different stages can be accurately captured. By solving the spatial gradient of the surface potential distribution, the change of the surface electric field strength is obtained. In order to identify the key potential gradient change phenomenon that appears in the fault recovery stage, the maximum potential gradient monitoring algorithm is used for dynamic tracking. This algorithm can accurately capture the potential redistribution phenomenon caused by electromagnetic induction and system impedance changes in the fault recovery stage, so as to detect the occurrence of the "secondary potential peak". The secondary potential peak occurs during the gradual decay of the fault current. Due to the non-linear characteristics of the grounding grid impedance and the electromagnetic induction effect, the potential gradient changes to a peak value again in a short time. This phenomenon is an important characteristic that is difficult to capture by traditional fault potential analysis methods. Therefore, using the maximum potential gradient monitoring algorithm can significantly improve the analysis accuracy in the fault recovery stage.Based on the potential gradient distribution data, analyze the entire fault process. From the initial stage to the steady state stage, and then to the recovery stage, study the potential distribution characteristics of each stage respectively, and analyze the dynamic migration law of the position of the maximum potential gradient. In the initial stage, due to the rapid injection of fault current, the potential gradient changes violently, and the maximum potential gradient appears in the area closer to the fault point. As the fault process enters the steady state stage, the potential gradient gradually stabilizes, and the position of the maximum potential gradient migrates to the area far from the fault point. In the recovery stage, due to the gradual attenuation of the current and the change of system impedance, the potential gradient will change again, accompanied by the appearance of a secondary potential peak. By analyzing these stage characteristics, obtain the potential distribution diagram of the whole process, which reflects the potential change of the grounding grid and the dynamic change characteristics of the potential gradient during the whole fault process.

[0075] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0076] Perform a safety risk assessment through the potential distribution diagram of the whole process to obtain space-time risk distribution data;

[0077] Perform three-dimensional K-means clustering analysis on the space-time risk distribution data to obtain a set of risk clustering regions;

[0078] Calculate the space-time risk density of each clustering region for the set of risk clustering regions, and compare the space-time risk density with a preset density threshold to obtain a set of high-risk regions;

[0079] Conduct a hazard source tracking analysis on each high-risk region in the set of high-risk regions to obtain the target path of the fault current and the target node information;

[0080] Generate a safety risk area report based on the set of high-risk regions, the target path of the fault current and the target node information.

[0081] Specifically, the potential variation of the substation grounding grid during the entire fault process is analyzed through the full-process potential distribution map. The full-process potential distribution map dynamically captures the potential variations of each node of the grounding grid in the initial fault stage, steady-state stage, and recovery stage, and combines the potential gradient change data and the surface electric field intensity change information to form a risk distribution dataset covering the spatial and temporal dimensions of the substation grounding grid. This dataset contains the potential gradient change conditions at different spatial positions and records the change trends of the potential gradient at different time steps, thus reflecting the potentially high-risk areas and potential abnormal distribution characteristics during the entire fault process. To identify the risk distribution characteristics of different regions in the substation grounding grid, three-dimensional K-means clustering analysis is performed on the spatial-temporal risk distribution data. By combining the coordinate information in the spatial dimension with the risk change data in the temporal dimension, a three-dimensional risk matrix is formed, and the K-means clustering algorithm is used to automatically group this risk matrix. The K-means clustering algorithm automatically divides the spatial-temporal data into several risk clustering regions according to the risk characteristics of different regions. Each risk clustering region represents a region with similar risk characteristics at a specific spatial position and time stage, thereby effectively dividing the entire grounding grid area into different risk regions. After the division of the risk clustering regions is completed, the spatio-temporal risk density of each clustering region is calculated for the set of risk clustering regions. By analyzing the potential gradient change amplitude, potential difference between adjacent nodes, and the change of the surface electric field intensity within each clustering region, the spatio-temporal risk density of each clustering region is obtained, and the calculated spatio-temporal risk density is compared with a preset density threshold to identify the set of high-risk regions. The high-risk regions are concentrated near the fault current injection point, the position of the grounding lead, or the region where the potential gradient changes violently. These regions are dangerous areas where the potential exceeds the standard, the electric field intensity increases abnormally, and the step voltage and touch voltage exceed the standard. To analyze the risk sources of the high-risk regions, a hazard source tracking analysis is performed on the identified set of high-risk regions. Through an improved current path backtracking algorithm, the propagation path of the fault current in the grounding grid is tracked to identify the target path of the fault current and the target node information that causes the risk increase in this region. This algorithm is based on the change trend of the node potential difference and performs layer-by-layer backtracking along the current propagation direction to accurately determine the propagation path of the fault current and the positions of the key nodes. These key nodes are the nodes where the current density increases abnormally or the nodes where the potential changes significantly. Focusing on the analysis and monitoring of these nodes can effectively improve the safety of the grounding grid.Based on the above-obtained high-risk area set, fault current target path, and target node information, comprehensively analyze key information such as the spatial distribution characteristics, risk level, over-standard degree, and duration of each risk area, and generate a safety risk area report. This report records the specific location, risk characteristics, fault current propagation path, and key node information of each high-risk area. By classifying the risk levels of different types of risk areas, quantifying the over-standard degree of each area, and combining data analysis in the spatial-temporal dimension, it provides data support for the safety assessment of the substation grounding grid. At the same time, this safety risk area report puts forward corresponding safety protection suggestions for each high-risk area, including adding vertical grounding electrodes, optimizing the grounding grid structure, increasing equipotential bonding measures, etc.

[0082] The above described the method for safety assessment of the main grounding grid of a substation in the embodiments of the present application. Next, the system for safety assessment of the main grounding grid of a substation in the embodiments of the present application will be described. Please refer to Figure 2 , an embodiment of the system for safety assessment of the main grounding grid of a substation in the embodiments of the present application includes:

[0083] A setting module 201, configured to set multi-point potential gradient sensors on the main grounding grid of the substation of the power transmission and transformation project, and perform synchronous measurement through the multi-point potential gradient sensors to obtain an original potential gradient data set;

[0084] A distinguishing and processing module 202, configured to perform distinguishing and processing on the original potential gradient data set to distinguish effective signals from interference signals, and obtain the potential distribution information of the substation grounding grid;

[0085] A building module 203, configured to build a dynamic impedance model of the substation grounding grid according to the potential distribution information of the substation grounding grid;

[0086] A fault current analysis module 204, configured to use the dynamic impedance model of the substation grounding grid for fault current analysis to obtain a whole-process potential distribution diagram including the initial stage, steady state stage, and recovery stage;

[0087] A safety risk assessment module 205, configured to perform safety risk assessment based on the whole-process potential distribution diagram and output a safety risk area report.

[0088] Through the collaborative cooperation of the above-mentioned various components, through the multi-point potential gradient synchronous measurement data acquisition technology, the high-density and high-time-precision monitoring of the potential distribution of the substation grounding grid is realized, overcoming the limitation of sparse data in traditional measurement methods. By using the potential gradient signal recognition and screening method based on deep learning, the effective measurement signals and interference signals are effectively distinguished, significantly improving the reliability and accuracy of the grounding grid potential measurement data in a complex electromagnetic environment. The constructed hierarchical soil resistivity dynamic impedance model takes into account the effects of temperature, humidity, and seasonal changes on soil resistivity, as well as the skin effect and proximity effect of conductors, making the characterization of the impedance characteristics of the substation grounding grid more accurate. The transient analysis method of the grounding grid fault current distribution first focuses on the secondary potential peak phenomenon in the fault recovery stage, reveals the safety risk points ignored by traditional methods, and comprehensively evaluates the potential distribution changes during the entire fault process. The risk assessment method based on the three-dimensional safety risk assessment matrix realizes the precise positioning of risk areas and the effective tracking of hazard sources.

[0089] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the above-described systems, systems, and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0090] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a device for the safety assessment of the main grounding grid of a substation (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0091] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A safety assessment method for the main grounding grid of a substation, characterized in that, Including: Setting multi-point potential gradient sensors on the main grounding grid of the substation in the power transmission and transformation project, and synchronously measuring through the multi-point potential gradient sensors to obtain an original potential gradient data set; Distinguishing effective signals from interference signals in the original potential gradient data set to obtain the potential distribution information of the substation grounding grid; Establishing a dynamic impedance model of the substation grounding grid according to the potential distribution information of the substation grounding grid; Using the dynamic impedance model of the substation grounding grid for fault current analysis to obtain a whole-process potential distribution map including the initial stage, steady state stage and recovery stage; Conducting a safety risk assessment based on the whole-process potential distribution map and outputting a safety risk area report.

2. The safety assessment method for the main grounding grid of a substation according to claim 1, wherein The setting of multi-point potential gradient sensors on the main grounding grid of the substation in the power transmission and transformation project and synchronously measuring through the multi-point potential gradient sensors to obtain an original potential gradient data set includes: Arranging potential gradient sensors with a bipolar probe structure at the surrounding and internal target nodes of the grounding grid of the substation in the power transmission and transformation project according to the topological structure to obtain a measurement network; Connecting the potential gradient sensors in the measurement network to a central data acquisition unit and performing synchronous clock calibration through GPS timing technology to obtain a multi-point synchronous measurement system; Injecting test excitation conditions at a preset position in the substation through the multi-point synchronous measurement system; Under the test excitation conditions, using an analog-to-digital converter to collect the potential gradient changes of all measurement points to obtain original measurement data; Marking time for the original measurement data and synchronously recording the atmospheric temperature, humidity, surface temperature and the operating state of the target equipment in the substation at the measurement moment to obtain environment parameter matching data; Combining and processing the original measurement data and the environment parameter matching data to generate an original potential gradient data set.

3. The safety assessment method for the main grounding grid of a substation according to claim 1, characterized in that, The distinguishing of effective signals from interference signals in the original potential gradient data set to obtain the potential distribution information of the substation grounding grid includes: Inputting the original potential gradient data set into a multi-layer convolutional neural network model for signal feature extraction to obtain recognition result data; Suppressing the interference signals in the recognition result data to obtain noise-reduced potential gradient data; Performing a fast Fourier transform on the noise-reduced potential gradient data to obtain frequency domain feature data; Calculating the phase difference and amplitude ratio between each measurement point according to the frequency domain feature data to obtain a spatial distribution matrix representing the potential gradient relationship between each measurement point; Performing eigenvector analysis on the spatial distribution matrix to obtain an eigen-decomposition result; Based on the eigen-decomposition result, retaining the first k eigenvectors with a contribution rate exceeding a preset target value to obtain the potential distribution information of the substation grounding grid.

4. The safety assessment method for the main grounding grid of a substation according to claim 1, wherein The establishing of a dynamic impedance model of the substation grounding grid according to the potential distribution information of the substation grounding grid includes: Inverting the soil parameters according to the potential distribution information of the substation grounding grid to obtain the soil thickness and resistivity parameters; Calculating the temperature coefficient and humidity coefficient for the soil thickness and resistivity parameters to obtain a soil resistivity model with temperature and humidity compensation; Perform three-dimensional modeling based on the soil resistivity model with temperature and humidity compensation to obtain a grid-based three-dimensional model of the substation grounding grid area, and establish a complex impedance representation equation according to the grid-based three-dimensional model; Construct a refined model of the grounding grid metal conductor considering the skin effect and proximity effect according to the complex impedance representation equation, and introduce a seasonal variation correction coefficient to obtain a seasonally corrected grounding grid model; Perform parameter optimization iteration on the seasonally corrected grounding grid model through a genetic algorithm to obtain a dynamic impedance model of the substation grounding grid.

5. The safety assessment method for the main grounding grid of a substation according to claim 4, wherein The step of constructing a refined model of the grounding grid metal conductor considering the skin effect and proximity effect according to the complex impedance representation equation, and introducing a seasonal variation correction coefficient to obtain a seasonally corrected grounding grid model includes: Perform grid subdivision on the grounding grid metal conductor according to the complex impedance representation equation to obtain a segmented unit model of the conductor cross-section; Perform electromagnetic field calculation on the segmented unit model of the conductor cross-section to obtain a current density distribution function characterizing the skin effect; Calculate the electromagnetic coupling influence between adjacent conductors based on the current density distribution function characterizing the skin effect to obtain a conductor mutual impedance matrix characterizing the proximity effect; Perform seasonal factor analysis on the conductor mutual impedance matrix characterizing the proximity effect to obtain a set of seasonal correction parameters; Perform correction calculation on the soil resistivity according to the set of seasonal correction parameters and the conductor mutual impedance matrix characterizing the proximity effect to obtain a seasonal soil resistivity model; Perform comprehensive processing on the seasonal soil resistivity model, the segmented unit model of the conductor cross-section, the current density distribution function characterizing the skin effect, and the conductor mutual impedance matrix characterizing the proximity effect to obtain a seasonally corrected grounding grid model.

6. The safety assessment method for the main grounding grid of a substation according to claim 1, characterized in that The step of using the dynamic impedance model of the substation grounding grid for fault current analysis to obtain a full-process potential distribution map including the initial stage, steady state stage, and recovery stage includes: Based on the dynamic impedance model of the substation grounding grid, decompose the fault current into a superposition form of a DC component and an AC component to obtain a fault current function; Perform time step decomposition on the fault current function to obtain a time step sequence; For each time point in the time step sequence, establish a grounding grid mesh equation using the node potential method, and convert the frequency domain impedance into a time domain response through a fast convolution algorithm to obtain a grounding grid transient response equation set; According to the grounding grid transient response equation set, calculate the transient potential values of each node of the grounding grid and the potential difference between adjacent nodes at each time step to obtain fault current component distribution data; Calculate the surface electric field intensity based on the fault current component distribution data, and use the maximum potential gradient monitoring algorithm to detect the secondary potential peak phenomenon in the fault recovery stage to obtain potential gradient distribution data; Analyze the potential distribution characteristics and the migration law of the maximum potential gradient position in the initial stage, steady state stage, and recovery stage based on the potential gradient distribution data to obtain a full-process potential distribution map.

7. The safety assessment method for the main grounding grid of a substation according to claim 1, wherein The step of performing safety risk assessment based on the full-process potential distribution map and outputting a safety risk area report includes: Perform a safety risk assessment through the whole-process potential distribution map to obtain spatial-temporal risk distribution data; Perform three-dimensional K-means clustering analysis on the spatial-temporal risk distribution data to obtain a set of risk clustering regions; Calculate the spatio-temporal risk density of each clustering region for the set of risk clustering regions, and compare the spatio-temporal risk density with a preset density threshold to obtain a set of high-risk regions; Conduct a hazard source tracking analysis on each high-risk region in the set of high-risk regions to obtain the target path of the fault current and the target node information; Generate a safety risk area report based on the set of high-risk regions, the target path of the fault current, and the target node information.

8. A safety assessment system for the main grounding grid of a substation, characterized in that, For implementing the method for safety assessment of the main grounding grid of a substation as described in any one of claims 1-7, the system for safety assessment of the main grounding grid of a substation includes: A setting module for setting multi-point potential gradient sensors on the main grounding grid of the substation of the power transmission and transformation project, and performing synchronous measurement through the multi-point potential gradient sensors to obtain an original potential gradient data set; A discrimination processing module for performing discrimination processing on the original potential gradient data set between effective signals and interference signals to obtain the potential distribution information of the substation grounding grid; A building module for building a dynamic impedance model of the substation grounding grid according to the potential distribution information of the substation grounding grid; A fault current analysis module for using the dynamic impedance model of the substation grounding grid for fault current analysis to obtain a whole-process potential distribution map including the initial stage, the steady state stage, and the recovery stage; A safety risk assessment module for performing a safety risk assessment based on the whole-process potential distribution map and outputting a safety risk area report.

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