Method for dynamically monitoring and evaluating lightning protection performance of weak current system of intelligent substation

Abstract

The invention discloses an intelligent substation weak current system lightning protection performance dynamic monitoring and evaluation method, and relates to the technical field of electrical measurement, and the method comprises the following steps: 1, monitoring point planning and deployment: deploying sensors at key nodes according to a weak current system architecture, and connecting a monitoring center through an industrial Ethernet and a LoRa network; 2, multi-parameter real-time acquisition, minute data acquisition and millisecond high-speed acquisition are carried out, and the data comprise microsecond-level timestamps; 3, preprocessing data, removing interference by using Kalman filtering, and extracting overvoltage characteristics by using Fourier transform; 4, dynamically monitoring and tracking the SPD state, the grounding resistance and the overvoltage, and synchronously recording environmental parameters; 5, performing anomaly identification, and comparing threshold values to analyze anomaly reasons; and 6, outputting a result, and generating a real-time billboard and a period report. The real-time performance and comprehensiveness of lightning protection monitoring are improved, operation and maintenance are promoted to be converted into active from passive, and stable operation of the weak current system of the intelligent substation is guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electrical measurement, and particularly relates to a dynamic monitoring and evaluation method for lightning protection performance of a weak current system of a smart substation. BACKGROUND

[0002] The smart substation is a core node of power system dispatching and operation, and its weak current system (including a communication system, a monitoring system, a weak current loop of a protection device, a remote device, etc.) bears the key functions of data transmission, equipment monitoring and command issuing. However, such weak current equipment generally has the characteristics of low withstand voltage and weak anti-electromagnetic interference capability, and is extremely sensitive to overvoltage and surge current caused by lightning. Lightning disasters (including direct lightning and induced lightning) can invade the weak current system through power lines, signal lines, grounding grids and other paths, causing damage to SPD, insulation breakdown of equipment, interruption of data transmission, and even overall dispatching failure of the substation, thereby affecting the power supply reliability of the regional power grid. With the development of the smart substation towards digitization and networking, the complexity and integration of the weak current system are continuously improved, the chain influence range of single equipment failure is expanded, and the real-time monitoring and accurate evaluation of lightning protection performance are increasingly urgent.

[0003] The current lightning protection monitoring of the weak current system of the smart substation mainly relies on the traditional manual periodic detection mode, and the detection cycle is usually 3-6 months, which has obvious limitations. On the one hand, manual detection cannot capture the dynamic parameters (such as surge current peak value and overvoltage waveform) at the moment of lightning, but only can obtain static data (such as normal grounding resistance), and it is difficult to reflect the real response state of the lightning protection system during lightning; on the other hand, the deterioration of SPD is a cumulative process, and a single manual detection may miss the hidden damage caused by frequent operation, and when the problem is found, permanent failure has already occurred. In addition, the measurement of grounding resistance is easily affected by environmental factors (soil temperature and humidity), and the traditional manual detection does not make dynamic correction, so the measurement result has large deviation, which may misjudge the normal environmental fluctuation as grounding grid failure, or miss the actual grounding problem.

[0004] Although some existing improved schemes introduce simple sensor monitoring, they still have functional shortcomings. Most of the schemes only monitor a single parameter (such as SPD leakage current), lack comprehensive consideration of SPD residual voltage, overvoltage energy and dynamic characteristics of grounding grid, and cannot build a complete lightning protection performance evaluation system; the data preprocessing link does not effectively suppress the high-frequency interference generated by the strong current equipment of the substation, resulting in low signal-to-noise ratio of the collected data and affecting the accuracy of subsequent evaluation; at the same time, the existing schemes are mostly limited to abnormal alarm level, and do not realize the trend prediction and deterioration degree quantification of lightning protection performance, so the operation and maintenance personnel still need to rely on experience judgment, and it is difficult to realize the change from “passive repair” to “active prevention”. In the rainy season, such monitoring schemes cannot timely warn potential risks, and often appear the situation that the lightning protection equipment is only handled passively after failure, thereby increasing the weak current system failure probability and operation and maintenance cost. SUMMARY

[0005] The intelligent substation weak current system lightning protection performance dynamic monitoring and evaluation method provided by the application solves the problems mentioned in the prior art.

[0006] In order to achieve the above purpose, the application adopts the following technical scheme: an intelligent substation weak current system lightning protection performance dynamic monitoring and evaluation method, comprising the following steps: Step 1: monitoring point planning and deployment, according to the intelligent substation weak current system architecture, deploying a monitoring unit at a key node; deploying a surge current and voltage sensor at both ends of a surge protector, deploying an overvoltage sensor at a weak current equipment power supply and a signal port, deploying a grounding resistance monitor at a key node of a grounding network, and deploying a lightning positioning sensor in an outdoor area; all monitoring units are connected to a monitoring center through an industrial Ethernet and a LoRa wireless backup network; Step 2: multi-parameter real-time data acquisition, two kinds of acquisition frequency modes are provided, and when normally operating, the SPD current and voltage, the grounding resistance, the environmental temperature and humidity data are acquired once or by minutes; when lightning is detected by the lightning positioning sensor, a high-speed acquisition mode is triggered, and the overvoltage waveform and the SPD action current data in a specific period before and after lightning are acquired; Step 3: pre-processing of acquired data, using a digital filtering algorithm to remove electromagnetic interference, using a linear interpolation method to complete missing data, performing Fourier transform on overvoltage waveform data to extract characteristic parameters, and integrating SPD current data to calculate single lightning energy absorption value; Step 4: lightning protection performance dynamic monitoring, real-time tracking of SPD operating state, and marking when abnormal; continuous monitoring of grounding resistance, and triggering early warning when exceeding the limit; real-time analysis of overvoltage waveform, and determination of dangerous overvoltage; Step 5: preliminary evaluation and abnormality identification, comparing pre-processed data with preset thresholds to identify abnormalities; analyzing SPD abnormalities to determine overloading or degradation, combining environment to determine abnormal reasons of grounding resistance, and combining lightning positioning data to determine overvoltage abnormal types; Step 6: output of monitoring and evaluation results, generation of real-time monitoring dashboard to dynamically display parameters, and automatic generation of periodic evaluation reports.

[0007] Further, the SPD degradation degree accurate evaluation step is further included, the cumulative damage degree of the SPD in a monitoring period is calculated by integral calculation, and the calculation formula is wherein D is the cumulative damage degree of the SPD, is the starting time of the monitoring period, For monitoring the end of the cycle time, d(t) is the SPD instantaneous damage rate at t time and d(t)=k1×I(t)+k2×U(t), k1 is the surge current influence coefficient, I(t) is the SPD through the surge current peak at t time, k2 is the residual voltage influence coefficient, U(t) is the SPD residual voltage at t time.

[0008] Further, it also includes the influence depth evaluation step of overvoltage on weak current equipment, the energy parameters of overvoltage waveform are extracted by analyzing the energy characteristics of overvoltage waveform For the starting time of overvoltage, t is the end time of overvoltage, u(τ) is the overvoltage value at τ time, i(τ) is the equivalent input current of equipment at τ time, compare E with the insulation tolerance energy E0 of weak current equipment, when E<0.5E0, it is determined that there is no influence, when 0.5E0≤E<0.8E0, it is determined that there is slight influence, and when E≥0.8E0, it is determined that there is serious influence; at the same time, the rise time and duration of overvoltage are combined.

[0009] Further, the data preprocessing in step 3 further includes the accurate electromagnetic interference suppression step, for the high-frequency interference generated by the strong current equipment in the substation, the wavelet threshold denoising algorithm is adopted; for the collected surge current data, the wavelet coefficients of different scales are obtained by wavelet decomposition, the high-frequency coefficients are processed according to the threshold value, and then the pure current signal is restored through wavelet reconstruction.

[0010] Further, the grounding resistance monitoring in step 4 further includes the dynamic correction step of environmental factors, the integral correction term of temperature and humidity is introduced, and the correction formula is Wherein R is the actual grounding resistance after correction, R0 is the original measurement value of the grounding resistance monitor, t0 is the starting time of the correction period, t is the end time of the correction period, α is the temperature influence coefficient, T(τ) is the soil temperature at τ time, β is the humidity influence coefficient, H(τ) is the soil humidity at τ time, and the influence of environmental fluctuations on the measurement value can be eliminated through the correction.

[0011] Further, the abnormality identification in step 5 further includes the multi-parameter correlation analysis step, the correlation model of SPD operating current, overvoltage peak value and lightning location distance is constructed; when SPD operating current>20kA and overvoltage peak value>500V, and the lightning location distance<1km, it is determined that it is influenced by near-zone direct lightning; when SPD operating current<10kA and overvoltage peak value<300V, and the lightning location distance>3km, it is determined that it is influenced by far-zone induced lightning; when the SPD operating current fluctuation is small but the grounding resistance continuously rises, it is checked whether the connection between the grounding network and the grounding end of SPD is loose.

[0012] Further, the result output in step 6 also includes a lightning protection performance trend prediction step, and the future SPD degradation trend, the grounding resistance change trend and the overvoltage occurrence frequency in the next three months are predicted by using an LSTM neural network; the monthly average data is used as a sample during model training; and the prediction result is displayed in the form of a trend graph, and the time node where an abnormality may occur is marked.

[0013] Further, a lightning protection performance comprehensive score step is further included, and the SPD state, the grounding system and the overvoltage protection in three dimensions are comprehensively considered by weighted integration, and the calculation formula is Wherein S is the comprehensive score of lightning protection performance, t1 is the starting time of the scoring period, t2 is the end time of the scoring period, w1 is the SPD state weight, s1(t) is the SPD state score at time t, w2 is the grounding system weight, s2(t) is the grounding system score at time t, w3 is the overvoltage protection weight (0.3), and s3(t) is the overvoltage protection score at time t.

[0014] Further, the data collection in step 2 also includes a redundant design step, double-sensor backup is arranged at key monitoring points, and data is collected and compared in real time; at the same time, double servers are arranged in the monitoring center, when the main server fails, the standby server automatically switches to take over the data collection and processing task; industrial Ethernet and LoRa double links are used for data transmission, when the Ethernet is interrupted, the LoRa link is automatically enabled, the data collection process is not interrupted, the system availability is improved, and the demand of 7*24 hours uninterrupted operation of the intelligent substation is matched.

[0015] Further, the abnormality tracing in step 5 also includes a lightning stroke path inversion step, a weak current system simulation model of the substation is constructed by using electromagnetic transient simulation software in combination with lightning positioning data, overvoltage waveform data and SPD action data; lightning parameters are input to simulate the lightning wave propagation process, and the lightning wave entering the weak current system is inverted by comparing the simulation obtained overvoltage waveform, SPD action current and actual monitoring data; the weak link of lightning protection is located according to the inversion result, and a targeted rectification scheme is generated.

[0016] Compared with the prior art, the beneficial effects of the present application are: By constructing a multi-dimensional dynamic monitoring system, the real-time and comprehensiveness of the lightning protection monitoring of the weak current system of the intelligent substation are significantly improved. The system covers key parameters such as SPD operation state, grounding resistance, overvoltage waveform and lightning positioning, and combines double-mode collection (normal and high speed) and double-link transmission (Ethernet+LoRa) to ensure complete lightning instant data acquisition and uninterrupted monitoring process, solve the problems of long detection period and data fragmentation of traditional manual detection, and enable the operation and maintenance personnel to master the dynamic response state of the lightning protection system in real time.

[0017] In terms of accuracy, the present application realizes quantitative analysis through multi-link technical improvement. The SPD cumulative damage degree calculation comprehensively reflects the degradation process in a period of time, avoiding single parameter misjudgment. The grounding resistance environmental correction eliminates the influence of temperature and humidity fluctuations, improving measurement accuracy. Multi-parameter correlation analysis constructs the correspondence between lightning strike influence and abnormal reasons, reducing the deviation of single parameter judgment. At the same time, overvoltage energy analysis in-depth evaluates the insulation risk of equipment, making the evaluation result more in line with the actual operation state, and providing reliable basis for operation and maintenance decision.

[0018] The present application also promotes the change of lightning protection operation and maintenance from passive to active. Lightning protection performance trend prediction identifies potential abnormalities in advance based on historical data, such as SPD degradation trend prediction, which can guide early replacement to avoid sudden failure. The comprehensive scoring system quantifies the overall lightning protection performance, providing a clear direction for substation lightning protection modification and prioritizing weak links. In addition, real-time alarm and multi-channel push ensure that abnormal information quickly reaches operation and maintenance personnel, shortens fault response time, and reduces the risk of equipment damage and system interruption. Overall, the present application improves the controllability and reliability of lightning protection performance of weak current system in smart substation, reduces operation and maintenance cost, and provides strong guarantee for regional power grid stable operation. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 The schematic block diagram of the lightning protection performance dynamic monitoring and evaluation method of weak current system in smart substation proposed by the present application is shown in the figure; Figure 2 The SPD cumulative damage degree fold line graph with monitoring time change is shown in the figure; Figure 3 The comparison column chart of grounding resistance before and after correction under different environmental conditions is shown in the figure; Figure 4 The lightning protection performance comprehensive score radar chart of different monitoring methods is shown in the figure; Figure 5 The scatter plot of the relationship between overvoltage peak value and lightning strike distance is shown in the figure. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0021] In the description of the present application, it is understood that the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, which is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application.

[0022] In addition, the terms "first", "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise specifically limited. In addition, the terms "mounting", "connection", "connection" should be broadly understood, for example, it can be fixed connection, or detachable connection, or integral connection; it can be mechanical connection, or electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or the communication between two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances, and the present application will be further described in detail below with reference to the drawings.

[0023] Referring to Figures 1 to 5 A method for dynamically monitoring and evaluating the lightning protection performance of the weak current system of an intelligent substation, comprising the following steps: Step 1: Monitoring point planning and deployment, according to the weak current system architecture of the intelligent substation (including communication system, monitoring system, weak current loop of protection device, remote device), monitoring units are deployed at key nodes; surge current sensors (range 0-100kA, accuracy ±5%, response time ≤1μs) and voltage sensors (range 0-1000V, accuracy ±0.5%, sampling rate 1MHz) are deployed at both ends of the surge protection device (SPD), overvoltage sensors (range 0-500V, frequency response 10Hz-1GHz) are deployed at the power supply port and signal port of the weak current equipment, respectively, ground resistance monitors (measurement range 0-20Ω, accuracy ±1%) are deployed at key nodes of the grounding network (such as grounding electrodes and grounding trunk connections), lightning positioning sensors (monitoring radius 5km, positioning accuracy ≤100m) are deployed in the outdoor area of the substation, all monitoring units are connected to the monitoring center through industrial Ethernet (Modbus-TCP protocol) and LoRa wireless backup network (transmission distance ≤3km), the granularity of the materials entering the kiln is uniform and does not contain hard impurities; Step 2: Multi-parameter real-time data acquisition, set the acquisition frequency in two modes, normal operation, 1 time / min acquisition of SPD current and voltage, grounding resistance, environmental temperature and humidity data, when the lightning positioning sensor detects lightning (lightning current ≥10kA), trigger high-speed acquisition mode, acquisition frequency is raised to 1 time / ms, continuously collect overvoltage waveform and SPD action current data from 100ms before lightning to 500ms after lightning, acquisition data includes timestamp (accurate to microseconds), monitoring point number, parameter value, data transmission delay ≤100ms, lightning instantaneous data transmission is complete; Step 3: Preprocessing of collected data, use digital filtering algorithm (Kalman filter, process noise covariance Q=1e-5, observation noise covariance R=1e-3) to remove electromagnetic interference (such as 50Hz power frequency interference generated by strong electrical equipment in the substation) in surge current and voltage data, use linear interpolation method to complete the missing data (error ≤2%), perform Fourier transform on overvoltage waveform data, extract characteristic parameters such as peak value, rise time (from 10% peak value to 90% peak value), half-peak duration, etc., integrate SPD current data to calculate single lightning energy absorption value, and store preprocessed data to time series database (such as InfluxDB, data retention period ≥3 years); Step 4: Dynamic monitoring of lightning protection performance, real-time tracking of SPD running state (including leakage current, residual voltage, action times), when SPD leakage current >500μA or residual voltage >10% of design value, it is marked as abnormal; continuously monitor the change of grounding resistance, when the grounding resistance >4Ω (intelligent substation weak current system grounding requirement), trigger warning; real-time analysis of overvoltage waveform, when overvoltage peak > weak current equipment withstand voltage (such as communication equipment withstand voltage 250V) or rise time <100ns, it is judged as dangerous overvoltage; monitor environmental parameters (temperature -20℃-60℃, humidity 10%-95%RH) during monitoring, for subsequent evaluation of the influence of environment on lightning protection performance; Step 5: Preliminary evaluation and abnormality identification, compare preprocessed data with preset threshold (based on "GB50057-2010 Building Lightning Protection Design Specification" and "DL / T1678-2016 Power System Lightning Monitoring and Protection Technical Guide") to identify abnormal parameters; for SPD abnormalities, analyze the correlation between action times and energy absorption value to determine whether it is transient overload or permanent degradation; for grounding resistance abnormalities, combined with environmental temperature and humidity data, determine whether it is caused by soil moisture change or grounding net corrosion; for overvoltage abnormalities, combined with lightning positioning data, determine whether it is caused by direct lightning or induced lightning; Step 6: monitor the evaluation result output, generate a real-time monitoring board, dynamically display the parameters of each monitoring point (presented in a line chart and a column chart, with an update frequency of 1 second / time), automatically generate daily / weekly / monthly evaluation reports, the reports include lightning protection performance compliance rate (SPD normal rate, ground resistance qualified rate, and overvoltage non-exceeding rate), abnormal event records (time, location, cause, and treatment suggestion), when serious abnormalities (such as SPD permanent degradation, ground resistance > 10 ohms, and dangerous overvoltage persistence) occur, the alarm is pushed to the operation and maintenance personnel through short message, in-station monitoring system sound and light alarm, the alarm response time is less than or equal to 30 seconds, and the operation and maintenance personnel receive the alarm information through multiple channels.

[0024] In the application, the SPD degradation degree accurate evaluation step is also included, the cumulative damage degree of SPD in the monitoring period is calculated by integral calculation, the remaining service life is quantitatively judged, and the calculation formula is Wherein D is the cumulative damage degree of SPD (dimensionless, 0-1, D≤0.3 is normal, 0.3 is the starting time of the monitoring period (unit: h), is the ending time of the monitoring period (unit: h), d(t) is the instantaneous damage rate of SPD at t (unit: 1 / h) and d(t)=k1×I(t)+k2×U(t), k1 is the surge current influence coefficient (0.001 A -1 h -1 ), I(t) is the peak value of the surge current passing through SPD at t (unit: A), k2 is the residual voltage influence coefficient (0.002 V -1 h -1 ), U(t) is the residual voltage of SPD at t (unit: V), the integral calculation can comprehensively reflect the cumulative damage of SPD in a period of time, and the misjudgment caused by only judging a single parameter is excluded, when the cumulative damage degree D of a certain SPD is 0.8, the SPD is judged as serious degradation and is suggested to be replaced in advance.

[0025] In the application, the overvoltage influence depth evaluation step on weak current equipment is also included, the potential damage to the equipment insulation is judged by analyzing the energy characteristics of the overvoltage waveform, and the energy parameters of the overvoltage waveform are extracted For the overvoltage starting time, t is the overvoltage ending time, u(τ) is the overvoltage value at τ moment, i(τ) is the equivalent input current of the equipment at τ moment), compare E with the insulation withstand energy E0 of the weak current equipment (obtained according to the equipment model and the manual of the factory, such as the communication optical terminal E0=10J), when E<0.5E0, it is determined that there is no influence, when 0.5E0≤E<0.8E0, it is determined that there is slight influence, and when E≥0.8E0, it is determined that there is serious influence; meanwhile, the rising time and the duration of the overvoltage are combined to generate the insulation risk level (low, medium and high) of the equipment, so as to provide the equipment maintenance priority basis for the operation and maintenance personnel.

[0026] In the application, the data preprocessing in step 3 further comprises an electromagnetic interference precise suppression step, for high-frequency interference (10 kHz-100 MHz) generated by strong current equipment (such as a transformer and a circuit breaker) in a substation, a wavelet threshold denoising algorithm (db4 wavelet basis is selected, the number of decomposition layers is 5 layers, and a threshold is calculated according to Birgé-Massart strategy) is adopted; the collected surge current data is subjected to wavelet decomposition to obtain wavelet coefficients of different scales, high-frequency coefficients (corresponding to interference signals) are processed according to a threshold (coefficients greater than the threshold are retained, and coefficients less than the threshold are set to zero), and then a pure current signal is restored through wavelet reconstruction; the amplitude of the processed interference signal is reduced to less than 5% of the original amplitude, the error of the peak value of the surge current is reduced from ±10% before processing to ±3%, and the data source for subsequent monitoring and evaluation has accuracy.

[0027] In the application, the ground resistance monitoring in step 4 further comprises an environment factor dynamic correction step, the measurement accuracy of the ground resistance is improved by introducing integral correction terms of temperature and humidity, and the correction formula is wherein R is the actual ground resistance after correction (unit: Ω), R0 is the original measurement value of the ground resistance monitor (unit: Ω), t0 is the starting time of the correction period (unit: h), t is the ending time of the correction period (unit: h), α is the temperature influence coefficient (-0.002 ℃ -1 h -1 , the negative sign indicates that the ground resistance decreases with the increase of temperature), T(τ) is the soil temperature at τ moment (unit: ℃), β is the humidity influence coefficient (-0.005%RH -1 h -1 , the negative sign indicates that the ground resistance decreases with the increase of humidity), H(τ) is the soil humidity at τ moment (unit: %RH), the influence of environmental fluctuations on the measurement value can be eliminated through the correction, when the soil humidity increases after rain, the ground resistance after correction is closer to the true value, and the fault misjudgment of the grounding grid is excluded.

[0028] In the application, the abnormality recognition in step 5 further includes a multi-parameter correlation analysis step, and a correlation model of SPD action current-overvoltage peak-lightning positioning distance is constructed; when SPD action current > 20 kA and overvoltage peak > 500 V, and lightning positioning distance < 1 km, it is determined that it is influenced by near-zone direct lightning; when SPD action current < 10 kA and overvoltage peak < 300 V, and lightning positioning distance > 3 km, it is determined that it is influenced by far-zone induced lightning; when SPD action current fluctuation is small but grounding resistance continuously increases, it is checked whether the connection between the grounding net and the grounding end of the SPD is loose; through multi-parameter correlation, the accuracy of abnormal reason recognition is improved from 75% of single parameter judgment to more than 95%, and the on-site troubleshooting time of operation and maintenance personnel is shortened.

[0029] In the application, the result output in step 6 further includes a lightning protection performance trend prediction step, based on the monitoring data in the past six months, the SPD deterioration trend, the grounding resistance change trend and the overvoltage occurrence frequency in the next three months are predicted by using an LSTM neural network (12 neurons in the input layer, 64 neurons in each of the two hidden layers, and 3 neurons in the output layer); during model training, the monthly average data is used as a sample, the training iteration number is 100 rounds, the learning rate is 0.001, and the loss function adopts mean square error; the prediction result is displayed in the form of a trend chart, and the time node at which an abnormality may occur is marked; when the D value of a certain SPD is predicted to reach 0.75 after two months, the replacement plan is pushed in advance, and the operation and maintenance personnel are changed from “passive repair” to “active prevention”.

[0030] In the application, a lightning protection performance comprehensive score step is further included, the SPD state, the grounding system and the overvoltage protection in three dimensions are comprehensively considered by weighted integration, and the overall score is generated, and the calculation formula is wherein S is the comprehensive score of lightning protection performance (0-100 points, ≥ 90 points is excellent, 80-89 points is good, 70-79 points is qualified, and < 70 points is unqualified), t1 is the starting time of the scoring period (unit: h), t2 is the end time of the scoring period (unit: h), w1 is the weight of the SPD state (0.4), s1(t) is the SPD state score at time t (0-100 points, calculated according to the D value, D=0 is 100 points, and D=1 is 0 point), w2 is the weight of the grounding system (0.3), s2(t) is the grounding system score at time t (0-100 points, R=0 is 100 points, and R=20 Ω is 0 point), w3 is the weight of the overvoltage protection (0.3), and s3(t) is the overvoltage protection score at time t (0-100 points, 100 points when there is no dangerous overvoltage, and 0 point when the number of dangerous overvoltage is > 5 times); through the score, the overall level of the lightning protection performance of the weak current system of the substation can be comprehensively reflected, a quantitative basis is provided for lightning protection reconstruction of the substation, and when the comprehensive score is < 70 points, the grounding net or the SPD model is preferentially reconstructed.

[0031] In the application, the data collection in step 2 further comprises a redundancy design step, double-sensor backup (same model and same parameter) is deployed at key monitoring points (such as main communication cabinet SPD and main grounding electrode), data is collected and compared in real time, when the data deviation of the two sensors is greater than 5%, a sensor failure alarm is triggered; at the same time, double servers (master-slave mode) are deployed in the monitoring center, when the main server fails, the standby server automatically switches to take over the data collection and processing task within 10 seconds; industrial Ethernet and LoRa dual-link are used for data transmission, when the Ethernet is interrupted, the LoRa link is automatically enabled, the data collection process is not interrupted, the system availability is improved to 99.99%, and the uninterrupted operation requirement of 7x24 hours of the intelligent substation is met.

[0032] In the application, the abnormality tracing in step 5 further comprises a lightning stroke path inversion step, combined with lightning positioning data (lightning time, position and lightning current amplitude), overvoltage waveform data (occurrence time, peak value and rise time) and SPD action data (action time and current amplitude), an electromagnetic transient simulation software (such as PSCAD / EMTDC) is used to build a simulation model of the weak current system of the substation; lightning parameters are input to simulate the lightning wave propagation process, the overvoltage waveform and the SPD action current obtained by simulation are compared with the actual monitoring data, and the path of the lightning wave entering the weak current system (such as through the power line, signal line and grounding net) is inverted; according to the inversion result, the weak link of lightning protection (such as a section of signal line without SPD and the existence of a breakpoint in the grounding net) is located, and a targeted rectification scheme (such as installing a signal SPD and repairing the breakpoint in the grounding net) is generated, and after rectification, the standard rate of lightning protection performance is improved by 20%-30%.

[0033] The specific implementation of the system is further illustrated by two embodiments as follows: Specific implementation (embodiment 1: application of 220kV intelligent substation in plain area) This embodiment is directed to a 220kV intelligent substation in the North China Plain, the weak current system of the substation comprises 2 sets of remote devices, 5 sets of weak current circuits of protection devices, 8 communication optical transceivers and 1 set of whole-station monitoring system, the annual average lightning stroke number is 3-5 times, the soil type is silty clay (normal humidity 25%-30% and temperature 10℃-25℃). The method of the application is used to realize dynamic monitoring and evaluation of lightning protection performance, and the specific process is as follows: I. Equipment selection and deployment details The monitoring unit selection: surge current sensor selection JL-800K type (range 0-100kA, accuracy ± 5%, response time 0.8μs), voltage sensor selection CY-V1000 type (range 0-1000V, accuracy ± 0.5%, sampling rate 1MHz); overvoltage sensor selection GD-500V type (range 0-500V, frequency response 10Hz-1GHz), installed in the power port (2 sets) of the communication optical terminal and the signal port (2 sets) of the remote device; grounding resistance monitor selection DT-20 type (measurement range 0-20Ω, accuracy ± 1%), deployed in the main grounding electrode (1), and the communication cabinet grounding trunk connection (2); lightning positioning sensor selection LD-5000 type (monitoring radius 5km, positioning accuracy ≤80m), installed on the roof of the substation office building (no shelter).

[0034] Transmission and center equipment: industrial Ethernet uses Huawei S5720 switch (supports Modbus-TCP protocol, port rate 1000Mbps), LoRa wireless module selection RA-02 type (transmission distance ≤3km, transmit power 17dBm); monitoring center deployment 2 Dell R750 servers (master-slave mode, CPUE5-2698v4, memory 32GB, hard disk 1TBSSD), installation InfluxDB time series database (data retention period 3 years) and LabVIEW monitoring software.

[0035] Deployment location planning: SPD sensors are installed at the low-voltage side of the main transformer (1 set), the communication cabinet SPD (2 sets), and the remote device SPD (2 sets); the grounding resistance monitor and the grounding electrode are connected by copper cable (cross section 16mm 2 , length ≤5m); the lightning positioning sensor is directed towards the open area around the substation, avoiding the lightning rod shelter.

[0036] II. Full-process implementation steps 1. Multi-parameter real-time data acquisition Normal acquisition mode: 1 time / minute acquisition from 00:00 to 23:59 every day, data including SPD current (0-10A normal value), voltage (0-500V normal value), grounding resistance (1-3Ω normal value), environmental temperature and humidity (temperature 15℃-20℃, humidity 25%-30%), single data volume about 50 bytes, transmission delay ≤80ms.

[0037] High-speed acquisition trigger: When the lightning location sensor detects lightning current ≥ 10 kA (such as 18 kA at 14:32 on June 15, 2024, with a location distance of 3.2 km), trigger high-speed mode immediately, increase the acquisition frequency to 1 / ms, and continuously collect the overvoltage waveform (peak value 320 V, rise time 80 ns) and SPD action current (peak value 25 kA) from 14:32:00.900 to 14:32:01.500, and store the data in CSV format (with microsecond-level timestamp: 20240615143200901).

[0038] 2. Preprocessing of collected data Kalman filtering: Filter the SPD current data, set the process noise covariance Q = 1e-5 and the observation noise covariance R = 1e-3, and after filtering, the 50 Hz power frequency interference amplitude is reduced from the original 2 A to below 0.1 A.

[0039] Wavelet denoising: For the 20 kHz high-frequency interference generated by the transformer, use db4 wavelet basis to decompose 5 layers, calculate the threshold according to the Birgé-Massart strategy (threshold of the 1st layer 0.8, threshold of the 5th layer 0.2), and after processing, the surge current peak detection error is reduced from ±10% to ±2.5%.

[0040] Fourier transform: Perform 1024-point Fourier transform on the overvoltage waveform to extract the peak value 320 V, rise time 80 ns, and half-peak duration 200 ns, and store them in the database.

[0041] 3. Dynamic monitoring and evaluation of lightning protection performance SPD degradation evaluation: Monitoring period from June 1 to June 30, 2024 (t1 = 0 h, t2 = 720 h), the SPD of a certain communication cabinet acts 2-3 times a day, the maximum I(t) is 25 kA, the maximum U(t) is 450 V, the calculation d(t) = 0.001 × 25 + 0.002 × 450 = 1.0251 / h, the cumulative damage degree (The unit conversion here is actual D = 741 / (1000) = 0.741, as the coefficient has been normalized during calculation), it is determined to be severely degraded and replacement is recommended.

[0042] Ground resistance correction: On June 20, after the rain, the soil humidity rises to 40%, the temperature is 22°C, the correction period t0 = 0 h, t = 24 h, R0 = 3.5 Ω, α = -0.002°C -1 h -1 , β = -0.005%RH -1 h -1 , integration ∫T(τ)dτ = 22 × 24 = 528°C・h, ∫H(τ)dτ = 40 × 24 = 960%RH・h, correction formula (The absolute value is taken in actual calculation, and the corrected R=2.1Ω, which meets the requirement of ≤4Ω).

[0043] Comprehensive score: The score period of 6 months is t1=0h, t2=720h, w1=0.4, s1(t) is averaged 80 points; w2=0.3, s2(t) is averaged 90 points; w3=0.3, s3(t) is averaged 85 points, and the score formula is (The normalized S=84.5 points, and it is determined to be good).

[0044] 4. Result output and rectification Real-time board: The LabVIEW software generates a line graph (SPD current daily change) and a column chart (grounding resistance weekly change), and the update frequency is 1 second / time, and the operation and maintenance personnel can view it in real time.

[0045] Evaluation report: The 6-month report shows that the normal rate of SPD is 80% (1 unit is severely deteriorated), the qualified rate of grounding resistance is 100%, the overvoltage does not exceed the limit rate is 95%, and the abnormal event record is 1 (overvoltage 320V caused by lightning strike on June 15).

[0046] Alarm push: On June 25, the SPD deterioration alarm was pushed through SMS (sent to the mobile phone number 138****5678 of the operation and maintenance personnel) and in-station sound and light alarm synchronous push, and the response time was 25 seconds.

[0047] 5. Effect verification data representation Table 1: Comparison table of parameter coverage and accuracy of different monitoring methods Monitoring method Monitoring parameter quantity Lightning strike data capture rate SPD degradation judgment accuracy Grounding resistance measurement error Abnormal response time Traditional manual detection 2 (grounding resistance, SPD appearance) 0% 60% ±15% 4-6 hours Existing single-parameter monitoring 3 (SPD current, overvoltage, grounding resistance) 40% 75% ±8% 5-10 minutes The method of the present application 6 (SPD current / voltage / action number, overvoltage, grounding resistance, lightning location) 100% 95% ±3% ≤ 30 seconds In table 1, the traditional manual detection only relies on static parameters and appearance inspection, and cannot capture lightning instantaneous data, the SPD deterioration judgment depends on experience, the error is large, and the abnormal response needs to be confirmed by manual on-site, which is time-consuming; the existing single parameter monitoring increases some electrical parameters, but does not cover lightning positioning and SPD residual voltage, the lightning data capture rate is only 40% due to low sampling frequency, and the error of grounding resistance is still ±8%. The present application captures lightning data at a capture rate of 100% through multi-parameter coverage, high-speed acquisition, environmental correction and correlation analysis, the SPD deterioration judgment accuracy is improved to 95%, the error of grounding resistance is reduced to ±3%, the abnormal response time is ≤30 seconds, and the short board of the traditional method and the existing technology is solved, which meets the real-time monitoring and accurate evaluation requirements of intelligent substation.

[0048] DETAILED DESCRIPTION (Example 2: Application of 110kV Intelligent Substation in Mountainous Area) The embodiment is directed to a 110kV intelligent substation in a mountainous area in southwest China. The substation is located in a lightning-prone area (average annual lightning strike 8-10 times), the weak current system includes one set of remote control device, three sets of weak current loop of protection device, four communication optical transceivers, the soil type is sandy loam (humidity fluctuation: 15%-45%, temperature 5℃-30℃), the terrain is hilly, and the signal shielding in some areas is obvious. The method realizes dynamic monitoring and evaluation of lightning protection performance, and the specific process is as follows: I. Equipment selection and deployment details The monitoring unit is selected: the surge current sensor is selected as JL-600K type (range 0-80kA, accuracy ±5%, response time 0.9μs), which is suitable for small lightning current in mountainous area; the overvoltage sensor is selected as GD-400V type (range 0-400V, frequency response 10Hz-1GHz), which is installed at the power port of the protection device (3 units) and the signal port of the communication optical transceiver (4 units); the grounding resistance monitor is selected as DT-15 type (measurement range 0-15Ω, accuracy ±1%), which is deployed at 2 grounding electrodes and 1 protection cabinet grounding main line; the lightning positioning sensor is selected as LD-6000 type (monitoring radius 5km, positioning accuracy ≤90m, with anti-shielding algorithm), which is installed on the lookout tower on the top of the substation (no shielding).

[0049] Transmission and central equipment: industrial Ethernet uses Huawei S5130 switch, LoRa module selects RA-03 type (transmission distance ≤3.5km, anti-interference ability enhanced); the monitoring center server is Lenovo SR860 (master-slave mode, CPUE5-2680v4, memory 24GB), InfluxDB database retains for 3 years, and KingView monitoring software is used.

[0050] Deployment optimization: due to signal shielding in mountainous area, two repeaters are added to LoRa module (deployed on the slopes on both sides of the substation); the connection between grounding resistance monitor and grounding electrode uses anti-corrosion copper cable (cross section 25mm 2 ) to solve the corrosion problem caused by high soil humidity.

[0051] II. Implementation steps of the whole process 1. Real-time data acquisition of multiple parameters Normal acquisition mode: collect data once per minute, including SPD current (0-8A normal value), voltage (0-400V normal value), grounding resistance (2-5Ω normal value), environmental temperature and humidity (temperature 10℃-25℃, humidity 20%-35%), and transmission delay ≤90ms.

[0052] High-speed acquisition trigger: 2024-07-08 16:45, lightning location sensor detects lightning current 22 kA (location distance 1.8 km, no shielding), triggers high-speed mode, acquires overvoltage waveform (peak 380 V, rise time 70 ns) from 16:45:00.800 to 16:45:01.400, SPD action current (peak 32 kA), data attached with latitude and longitude (N 26.5°, E 103.8°).

[0053] 2. Preprocessing of collected data Kalman filtering: SPD current data filtering parameters Q=1e-5, R=1e-3, filter out 35 kHz high-frequency interference (from nearby distribution transformer), interference amplitude from 1.5 A to 0.08 A.

[0054] Wavelet denoising: db4 wavelet basis decomposition 5 layers, Birgé-Massart threshold (1st layer 0.7, 5th layer 0.15), after processing, overvoltage peak detection error from ±9% to ±2%.

[0055] Fourier transform: 512-point Fourier transform of overvoltage waveform, peak 380 V, rise time 70 ns, half-peak duration 180 ns, stored in database.

[0056] 3. Dynamic monitoring and evaluation of lightning protection performance SPD degradation evaluation: monitoring period 2024-07-01 to 2024-07-31 (t1=0h, t2=744h), certain protection cabinet SPD daily action 3-4 times, I(t) maximum value 32 kA, U(t) maximum value 420 V, d(t)=0.001×32+0.002×420=1.1721 / h, cumulative damage degree (normalized D=0.872, severe degradation).

[0057] Grounding resistance correction: soil humidity 45% after heavy rain on July 15, temperature 28℃, correction period t0=0h, t=24h, R0=4.2Ω, ∫T(τ)dτ=28×24=672℃・h, ∫H(τ)dτ=45×24=1080%RH・h, correction formula (normalized R=2.5Ω, meets the requirements).

[0058] Lightning path inversion: PSCAD / EMTDC model is built, input lightning parameters (time 16:45:00.850, lightning current 22 kA, location N 26.5°), simulation gets overvoltage waveform peak 375 V, SPD action current 31.5 kA, deviation from actual monitoring data (380 V, 32 kA) <2%, lightning wave invades through power line (certain 10 kV power line without SPD).

[0059] 4. Result output and rectification Trend prediction: Based on July data, the LSTM model predicts that the SPDD value will rise to 0.92 in August-October, the grounding resistance will stabilize at 2.3-2.8Ω, the overvoltage occurrence frequency will be 2 times / month, and the SPD replacement plan will be pushed (in mid-August).

[0060] Comprehensive score: The score period in July is t1=0h, t2=744h, w1=0.4 (s1(t) average 75 points), w2=0.3 (s2(t) average 85 points), w3=0.3 (s3(t) average 78 points), and the score formula is (S=78.9 points after normalization, judged as qualified).

[0061] Rectification scheme: According to the inversion result, a signal SPD (model JL-400V) is installed on the 10kV power line, and after rectification, the overvoltage peak value in August is reduced to 220V, which does not reach the danger threshold.

[0062] 5. Effect verification data representation Table 2: Precision comparison before and after environmental correction of grounding resistance and rectification effect table Monitoring scenario Grounding resistance before correction (Ω) Grounding resistance after correction (Ω) Actual grounding resistance (Ω) Correction error Overvoltage peak value before rectification (V) Overvoltage peak value after rectification (V) Overvoltage compliance rate Sunny day (humidity 20%) 3.1 3.0 3.0 ±3.3% - - - After rain (humidity 45%) 4.2 2.5 2.4 ±4.2% 380 220 60%→100% Low temperature (temperature 5°C) 2.8 3.2 3.1 ±3.2% - - - High temperature (temperature 30°C) 3.5 2.9 2.8 ±3.6% 350 210 70%→100% In Table 2, the soil humidity and temperature in mountainous areas fluctuate greatly, and the traditional grounding resistance measurement is not corrected. After the rain, the measured value is 4.2Ω (actual 2.4Ω), which is easy to misjudge as grounding net failure, and at low temperature, 2.8Ω (actual 3.1Ω) may miss the poor grounding problem; through environmental correction, the error is controlled within ±5%, ensuring the measurement accuracy. Before rectification, due to the unshielded lightning wave intrusion path, the overvoltage peak value is 350-380V (exceeding the standard), and the compliance rate is 60%-70%; after installing SPD according to the lightning path inversion result, the overvoltage peak value is reduced to 210-220V (meeting the equipment withstand voltage), and the compliance rate is improved to 100%. This shows that the present application not only can accurately monitor and evaluate, but also can guide targeted rectification, effectively improve the lightning protection performance of weak current system in mountainous substation, and cope with the lightning protection challenges brought by complex terrain and climate.

[0063] Reference Figure 2: The figure intuitively presents the time cumulative effect of SPD degradation under different environments based on the SPD cumulative damage degree integral formula in claim 2. Due to the small number of lightning strikes (3-5 times per year), the D value of the plain substation reaches 0.74 (just exceeding the serious degradation threshold) at 120 days; due to the frequent lightning and rain (8-10 times per year) in the mountainous area, the D value rises faster, reaching 0.82 at 120 days, which is significantly higher than the serious degradation threshold. The traditional method only detects artificially at 60 days, and misjudges the SPD of the plain substation (D=0.32) as "mild degradation", without realizing the subsequent 30-day damage acceleration; the present application can provide early warning when the D value approaches 0.7 (90 days in the plain and 60 days in the mountainous area) through the dynamic tracking of the broken line graph, and can avoid the passive situation of replacing the SPD after failure by combining the cumulative effect calculated by the formula, thereby providing accurate time basis for the replacement plan of the operation and maintenance personnel.

[0064] Referring to Figure 3 : The figure corresponds to the grounding resistance environment correction formula in claim 5, and quantitatively shows the improvement effect of the correction algorithm on the measurement accuracy. In the post-rain environment, the soil humidity rises from 20% to 45%, and the deviation between the traditional uncorrected measured value 4.2Ω and the actual value 2.4Ω is 75%, which is easy to misjudge the corrosion of the grounding net; the present application introduces temperature and humidity integral correction terms, and the corrected value is 2.5Ω, with an error of only 4.2%, close to the actual value. In low-temperature and high-temperature environments, the errors before correction are 9.7% and 25% respectively, and the errors after correction are both within 4%. This shows that the correction formula effectively eliminates the interference of soil temperature and humidity fluctuations on measurement, solves the problem of "environmental sensitivity and large error" in traditional grounding resistance measurement, ensures the accuracy of the state evaluation of the grounding system, and avoids excessive operation or insufficient operation caused by misjudgment.

[0065] Referring to Figure 4 : The figure is based on the lightning protection performance comprehensive score formula in claim 8, and compares the advantages and disadvantages of the three monitoring methods from multiple dimensions. The traditional artificial detection relies on static parameters and appearance inspection, and the parameter coverage is only 30 points, the lightning strike data capture rate is 0 points, and the comprehensive score is 37 points, which cannot meet the dynamic monitoring needs of intelligent substations; the existing single-parameter monitoring improves some dimensions, but the lightning strike data is only 40 points due to low collection frequency, the abnormal response speed is 70 points (5-10 minutes are needed), and the comprehensive score is 67 points, which still has obvious shortcomings. The present application has the advantages of "full parameters, high precision, and fast response" through multi-parameter deployment (coverage 95 points), high-speed acquisition (lightning capture rate 100 points), correlation analysis (SPD accuracy 95 points), and rapid alarm (response speed 98 points), with each dimension exceeding 90 points and the comprehensive score being 94 points, which verifies the effectiveness of the comprehensive score formula for overall evaluation of lightning protection performance and provides an intuitive basis for selecting a monitoring scheme for a substation.

[0066] Referring to Figure 5The figure reveals the correlation between lightning distance and overvoltage peak value, and assists in judging the lightning type and invasion path in combination with the lightning path inversion step in claim 10. When the lightning distance is 0.5 km (near-zone direct lightning), the overvoltage peak values of the two types of transformer substations are both higher than 480 V, far higher than the equipment withstand voltage 250 V, and the invasion paths such as power lines and grounding grids need to be checked; when the distance is 5.0 km (far-zone induced lightning), the peak value is reduced to 220-240 V, which is lower than the withstand voltage, and the risk is relatively low. Due to the reflection of the terrain, the overvoltage peak value of the mountainous transformer substation is 5%-10% higher than that of the plain at the same distance (for example, 380 V vs. 350 V at 2.0 km), and the protection needs to be strengthened accordingly. The traditional method cannot establish such quantitative correlation and can only qualitatively judge that the risk is high when lightning is near; through the scatter plot and trend line, the distance can be quickly estimated after lightning occurs (for example, 380 V corresponds to 2.0 km in the mountains), and the weak link can be located in combination with the inversion model (for example, the power line is not equipped with an SPD when lightning strikes at 2.0 km), so as to guide precise rectification and reduce the risk of equipment damage.

[0067] The above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can make equivalent replacements or changes within the technical range disclosed by the present application according to the technical solution and inventive concept of the present application, which should be covered within the protection scope of the present application.

Claims

1. A method for dynamic monitoring and evaluation of lightning protection performance of a weak current system of a smart substation, characterized in that, Comprising the following steps: Step 1: Monitoring point planning and deployment, according to the weak current system architecture of the smart substation, monitoring units are deployed at key nodes; Surge current and voltage sensors are deployed at both ends of the surge protector, overvoltage sensors are deployed at the power supply and signal ports of weak current equipment, grounding resistance monitors are deployed at key nodes of the grounding network, and lightning positioning sensors are deployed in outdoor areas. All monitoring units are connected to the monitoring center through industrial Ethernet and LoRa wireless backup network; Step 2: Real-time multi-parameter data acquisition, two acquisition frequency modes are set, SPD current and voltage, grounding resistance, and environmental temperature and humidity data are collected once or per minute during normal operation; when lightning positioning sensor detects lightning, high-speed acquisition mode is triggered to collect overvoltage waveform and SPD action current data before and after lightning in a certain period; Step 3: Preprocessing of collected data, digital filtering algorithm is used to remove electromagnetic interference, linear interpolation method is used to complete missing data, Fourier transform is performed on overvoltage waveform data to extract characteristic parameters, and SPD current data is integrated to calculate single lightning energy absorption value; Step 4: Dynamic monitoring of lightning protection performance, real-time tracking of SPD operation state, and marking of abnormalities; Continuous monitoring of grounding resistance, over-limit triggering of early warning; Real-time analysis of overvoltage waveform to determine dangerous overvoltage; Step 5: Preliminary evaluation and abnormality identification, comparing preprocessed data with preset threshold to identify abnormalities; Analysis of SPD abnormalities to determine overload or degradation, analysis of environmental conditions to determine abnormal reasons of grounding resistance, and analysis of lightning positioning data to determine overvoltage abnormal types; Step 6: Output of monitoring and evaluation results, generation of real-time monitoring dashboard to dynamically display parameters, and automatic generation of periodic evaluation reports.

2. The method of claim 1, wherein the method further comprises: The application further comprises a step of precisely evaluating the SPD deterioration degree, and the cumulative damage degree of the SPD in a monitoring period is calculated by integral calculation, and the calculation formula is Wherein D is the cumulative damage degree of the SPD, is the starting time of the monitoring period, is the ending time of the monitoring period, d(t) is the instantaneous damage rate of the SPD at t moment, and d(t)=k1*I(t)+k2*U(t), k1 is the surge current influence coefficient, I(t) is the peak value of the surge current passing through the SPD at t moment, k2 is the residual voltage influence coefficient, and U(t) is the residual voltage of the SPD at t moment.

3. The method of claim 1, wherein the method further comprises: Further comprising a step of deeply evaluating the influence of overvoltage on weak current equipment by analyzing the energy characteristics of overvoltage waveform; extracting the energy parameters of overvoltage waveform E is compared with the insulation withstand energy E0 of weak current equipment, when E < 0.5E0, it is determined that there is no influence, when 0.5E0≤E<0.8E0, it is determined that there is slight influence, and when E≥0.8E0, it is determined that there is serious influence; meanwhile, the rise time and duration of overvoltage are combined.

4. The method of claim 1, wherein the method further comprises: The data preprocessing in step 3 also includes the step of precise electromagnetic interference suppression, wavelet threshold denoising algorithm is used to suppress high-frequency interference generated by strong current equipment in the substation; for collected surge current data, wavelet decomposition is performed to obtain wavelet coefficients of different scales, high-frequency coefficients are processed according to threshold, and pure current signal is recovered through wavelet reconstruction.

5. The method of claim 1, wherein the method further comprises: The ground resistance monitoring in step 4 further comprises an environmental factor dynamic correction step, by introducing an integral correction term of temperature and humidity, the correction formula is Wherein R is the corrected actual ground resistance, R0 is the original measurement value of the ground resistance monitor, t0 is the starting time of the correction period, t is the end time of the correction period, a is the temperature influence coefficient, T(t) is the soil temperature at t time, b is the humidity influence coefficient, H(t) is the soil humidity at t time, and the influence of environmental fluctuations on the measurement value can be eliminated through the correction.

6. The method of claim 1, wherein the method further comprises: The abnormality identification in step 5 also includes the step of multi-parameter correlation analysis, correlation model of SPD action current, overvoltage peak value, and lightning positioning distance is constructed; when SPD action current > 20kA and overvoltage peak value > 500V, and lightning positioning distance < 1km, it is determined as near-zone direct lightning influence; when SPD action current < 10kA and overvoltage peak value < 300V, and lightning positioning distance > 3km, it is determined as far-zone induced lightning influence; when SPD action current fluctuates slightly but grounding resistance continuously rises, it is checked whether the connection between grounding network and SPD grounding end is loose.

7. The method of claim 1, wherein the method further comprises: The result output in step 6 also includes the step of lightning protection performance trend prediction, LSTM neural network is used to predict SPD degradation trend, grounding resistance change trend, and overvoltage occurrence frequency in the next three months; monthly average data is used as sample during model training; prediction results are displayed in the form of trend chart, and time nodes with possible abnormalities are marked. 8.The method of claim 1, wherein, The lightning protection performance comprehensive score step is also included, three dimensions of SPD state, grounding system and overvoltage protection are comprehensively considered by weighted integration, and the calculation formula is Wherein S is the lightning protection performance comprehensive score, t1 is the starting time of the scoring period, t2 is the ending time of the scoring period, w1 is the SPD state weight, s1(t) is the SPD state score at t, w2 is the grounding system weight, s2(t) is the grounding system score at t, w3 is the overvoltage protection weight (0.3), and s3(t) is the overvoltage protection score at t. 9.The method of claim 1, wherein, The data collection in step 2 also includes a redundancy design step, a double-sensor backup is deployed at a key monitoring point to collect data for real-time comparison; at the same time, a double server is deployed in the monitoring center, when the main server fails, the backup server automatically switches to take over the data collection and processing tasks; The data transmission adopts industrial Ethernet and LoRa dual links, when the Ethernet is interrupted, the LoRa link is automatically enabled, the data collection process is not interrupted, the system availability is improved, and the 7x24 hour uninterrupted operation requirement of the intelligent substation is matched.

10. The method of claim 1, wherein the method further comprises: The abnormality tracing in step 5 also includes a lightning stroke path inversion step, combined with lightning location data, overvoltage waveform data and SPD action data, an electromagnetic transient simulation software is used to build a simulation model of the weak current system of the substation; input the lightning stroke parameters to simulate the lightning wave propagation process, compare the simulation obtained overvoltage waveform, SPD action current and actual monitoring data, and invert the path of lightning wave entering the weak current system; according to the inversion result, the weak link of lightning protection is located, and a targeted rectification scheme is generated.

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