Substation micrometeorological monitoring system and early warning method

Abstract

The invention provides a substation micrometeorological monitoring system and an early warning method. A monitoring device is provided with a six-element meteorological sensor, a lightning sensor, a data acquisition preprocessing module, an edge calculation module, a communication module, a power supply module and an anti-surge module. Local micro-meteorological data of a transformer substation are comprehensively acquired through a six-element meteorological sensor and a lightning sensor, and the meteorological data of the transformer substation are analyzed, processed and protected through a data acquisition preprocessing module, an edge calculation module, a communication module, a power supply module and an anti-surge module; the early warning system realizes comprehensive meteorological monitoring and lightning early warning of the transformer substation through five steps of data acquisition, data preprocessing, feature extraction, data fusion and early warning triggering. According to the substation micro-meteorological monitoring device and the early warning system, traditional six-element meteorological monitoring is expanded, extreme weather such as storm, rainstorm and thunder is warned in real time, the disaster prevention capability of the substation is improved, real-time linkage management is achieved, and the safe operation of the substation is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of substation monitoring systems, and in particular to a substation micro-meteorological monitoring system and an early warning method. Background Art

[0002] At present, the micro-meteorological monitoring devices in substations are mainly used to monitor six basic meteorological parameters, including ambient temperature, humidity, wind speed, wind direction, air pressure and rainfall. After these data are collected by sensors, they are usually uploaded to remote servers through protocols for further processing and analysis. However, the monitoring objects are single and the data collection has no edge-side analysis and prediction. The current monitoring devices mainly focus on conventional meteorological parameters and lack early warning monitoring of extreme meteorological events such as lightning. Under lightning weather conditions, the equipment in substations is extremely susceptible to damage from lightning strikes. Therefore, there is a lack of early warning mechanism for lightning events, which makes the substations weak in defense against lightning disasters. The existing monitoring devices are installed However, it only collects data and uploads the data to the server, relying on the back-end server for data analysis and processing. This method has certain limitations, especially in situations where the budget is limited and high-performance servers cannot be configured, resulting in slow system response and high cost, and it is unable to process and predict sudden meteorological events in real time, reducing the substation's response capability in extreme weather. For this reason, the applicant proposes a substation micro-meteorological monitoring system and early warning method, which expands the traditional six-element meteorological monitoring, provides real-time early warnings for extreme weather such as storms, rainstorms, and lightning, improves the substation's disaster prevention capabilities, and performs real-time linkage management to enhance the substation's safe operation. Summary of the invention

[0003] In order to solve the above technical problems, the present invention proposes a substation micro-meteorological monitoring system and early warning method. The device is equipped with six-element meteorological sensors, lightning sensors, data acquisition and preprocessing modules, edge computing modules, communication modules, power supply modules and surge protection modules; the early warning system realizes comprehensive meteorological monitoring and lightning early warning of substations through five steps of data acquisition, data preprocessing, feature extraction, data fusion and early warning triggering; the substation micro-meteorological monitoring device and early warning system expand the traditional six-element meteorological monitoring, and provide real-time early warnings for extreme weather such as storms, rainstorms, and lightning, thereby improving the disaster prevention capabilities of substations, real-time linkage management, and improving the safe operation of substations.

[0004] To achieve the above object, the technical solution adopted by the present invention is:

[0005] A substation micro-meteorological monitoring system and early warning method, comprising a six-element meteorological sensor, a lightning sensor, a data acquisition preprocessing module, an edge computing module, a communication module, a power supply module and an anti-surge module, characterized in that: the six-element meteorological sensor provided in the substation micro-meteorological monitoring device and the early warning system is used to collect conventional meteorological data such as ambient temperature, humidity, wind speed, wind direction, air pressure and rainfall; the lightning sensor monitors the occurrence of lightning activities through changes in the electric field, and the lightning sensor can monitor the rate of change of the electric field in real time, sense the approach of lightning in advance, and provide an early warning signal for the system; the data acquisition preprocessing module converts the signals of the six-element meteorological sensor and the lightning sensor into digital data, and performs preliminary signal processing; the edge computing module performs real-time local processing in the device. Real-time data analysis, combined with the data from lightning sensors and six-element sensors, determines whether there is a lightning risk through algorithms, and predicts other meteorological elements. The edge computing module is also responsible for executing the lightning warning algorithm and promptly feeding back the processing results; the communication module uploads the collected and analyzed data to the central server through a private communication protocol, and can also feed back real-time warning information to the substation control system to trigger the corresponding emergency response mechanism; the power supply module uses an efficient AC-DC converter to convert the conventional voltage of the substation into a stable low-voltage direct current supply device; the surge protection module prevents surge voltage in the power system from causing serious damage to the equipment under extreme weather conditions such as lightning; and prevents instantaneous surges in the power supply voltage from affecting the normal operation of the equipment.

[0006] Furthermore, the specific working steps of the substation micro-meteorological monitoring device and early warning system are as follows:

[0007] Step 1: Data collection;

[0008] Through the six-element meteorological sensors and lightning sensors data collection, each sensor will then convert the physical signals it senses into analog signals, and then convert the analog signals into digital signals for subsequent data processing; after the data conversion is completed, the collected data timestamps will be synchronized to ensure that the data collected from each sensor can be arranged in chronological order, providing a time reference for subsequent data analysis;

[0009] Step 2: Data preprocessing;

[0010] Data preprocessing is performed through the data acquisition preprocessing module to ensure data quality, remove noise and outliers, and provide accurate and reliable input data for subsequent analysis; the data preprocessing module first cleans and denoises the data, including low-pass filtering to remove high-frequency noise and retain important low-frequency information such as electric field signals; moving average filtering to smooth the data of meteorological sensors, reduce sudden instantaneous fluctuations, and obtain smoother signals; outlier detection processing, according to the sensor's range and expected value, set a reasonable threshold to eliminate obvious outliers or noise;

[0011] Step 3: Feature extraction;

[0012] The feature extraction and data fusion steps are that the edge computing module obtains data from the data acquisition and preprocessing modules and extracts key features, and fuses these features to improve the accuracy of the warning. The feature extraction includes the electric field change rate, electric field strength, meteorological element characteristics, wind speed change rate, pressure gradient and temperature anomaly; specifically:

[0013] Electric field change rate: monitor the change of electric field through lightning sensor and calculate the electric field change rate;

[0014] The calculation formula is:

[0015]

[0016] in: is the rate of change of electric field;

[0017] E(t) is the electric field strength at the current moment;

[0018] E(t-Δt) is the electric field strength at the previous moment;

[0019] Δt is the sampling time interval;

[0020] Rapidly changing electric fields usually indicate that lightning activity is approaching; the electric field strength represents the intensity of lightning, and the electric field strength data is collected by lightning sensors; the electric field strength is defined as the maximum value of the electric field strength within a time window:

[0021]

[0022] Where: [t1, t2] is the start and end time of the analysis window;

[0023] Characteristics of meteorological elements; including conventional meteorological data such as wind speed, air pressure, temperature, and their rate of change; lightning is usually accompanied by strong winds and rapidly changing air pressure;

[0024] Wind speed change rate; rapid changes in wind speed may indicate the occurrence of severe convective weather, thereby increasing the possibility of lightning; its calculation formula is:

[0025]

[0026] Where: V w (t) is the wind speed at the current moment;

[0027] Δt is the sampling time interval;

[0028] Pressure gradient; the gradient change of pressure can indicate the development trend of thunderstorm weather; its calculation formula is:

[0029] ΔP=P(t)-P(t-Δt)

[0030] Where: ΔP is the pressure gradient;

[0031] P(t) is the air pressure at the current moment;

[0032] Temperature anomalies: Thunderstorms are more likely to develop under high temperature conditions. The system determines abnormal conditions by calculating the degree of temperature deviation:

[0033] T anomaly =T(t)-μ T

[0034] Where: T(t) is the temperature at the current moment;

[0035] μ T is the mean temperature in historical data;

[0036] Step 4: Data fusion;

[0037] The edge computing module integrates the collected lightning sensor data, wind speed data, air pressure data, and temperature data to form a comprehensive lightning risk assessment score. After data fusion, the lightning risk score R is calculated by weighted average. 雷电 , and conduct risk level assessment based on preset thresholds;

[0038] Step 5: Early warning triggering;

[0039] When the risk level is medium or high, the system automatically triggers an alert and performs the following responses:

[0040] 1) Send warning signals; send warning information to the substation control center through the communication module to remind the staff to take protective measures; provide detailed risk analysis reports, including trend forecasts of lightning activities, potential threats to substation operations from the current meteorological environment, and recommended emergency treatment plans;

[0041] 2) Start equipment protection: For key equipment in the substation, the system can automatically start protection measures to avoid equipment damage or system failure caused by lightning strikes; the linkage mechanism includes:

[0042] Automatic power-off protection: When the risk reaches a high risk level, the system automatically disconnects the power supply of non-essential power equipment to reduce the threat of lightning strikes to operating equipment; with the authorization of the power dispatching center, the system cuts off the power supply line threatened by lightning through the circuit breaker;

[0043] High-voltage protection starts; the system automatically activates the lightning protection device; cooperates with the surge protector in the line to reduce the damage that may be caused by lightning current;

[0044] Start the environmental protection device; in view of the special environment of the substation, the system will trigger the protection mode under high-risk lightning conditions;

[0045] The system will automatically activate the fire alarm system's standby mode; at this point, the fire alarm system begins to enhance monitoring of the substation area;

[0046] Increase the frequency of fire detection; the detection frequency of fire alarm sensors is increased to twice that of normal mode to quickly capture possible signs of fire; increase vigilance in areas where fire hazards may exist.

[0047] Furthermore, the low-pass filtering process in the second working step of the substation micro-meteorological monitoring device and early warning system is specifically as follows:

[0048] To remove high-frequency noise from sensor data, the system uses a low-pass filter to retain low-frequency signals related to lightning. The low-pass filter processing formula is:

[0049] y[n]=α·x[n]+(1-α)·y[n-1]

[0050] Where: y[n] is the output of the nth sampling point after filtering;

[0051] x[n] is the value of the original signal at the nth sampling point;

[0052] α: filter coefficient, the value range is 0<α<1, which determines the smoothness of the filter.

[0053] Furthermore, the moving average filtering process in the second working step of the substation micro-meteorological monitoring device and early warning system is specifically as follows:

[0054] Moving average filtering smoothes instantaneous mutation signals by taking the average value of the data in the window; the formula is:

[0055]

[0056] Where: N is the window size;

[0057] y[n] is the output of the nth sampling point after filtering;

[0058] x[ni] is the i-th sampling point of the original signal in the window.

[0059] Furthermore, the abnormal value detection process in the second working step of the substation micro-meteorological monitoring device and early warning system is specifically as follows:

[0060] Outlier detection is to detect outliers in sensor data. Data judged as outliers will be marked and removed. The system uses the statistical 3σ rule, and the formula is:

[0061]

[0062] The outlier judgment conditions are:

[0063] x i is considered an outlier if and only if |x i -μ|>3σ

[0064] Where: μ is the mean of the data;

[0065] σ is the standard deviation of the data;

[0066] x i is the ith data point.

[0067] Furthermore, the lightning risk score R in step 4 of the substation micro-meteorological monitoring device and early warning system is 雷电 Specifically:

[0068] According to the weight of the sensor, combined with the characteristics of electric field change rate, wind speed, air pressure, etc., a weighted average is calculated to measure the lightning risk. The calculation formula is:

[0069]

[0070] Where: R 雷电 Score lightning risk;

[0071] w1, w2, w3, and w4 are weight coefficients of each feature, representing the degree of influence of each data on lightning risk assessment;

[0072] is the rate of change of electric field;

[0073] E(t) is the electric field intensity;

[0074] V w is the wind speed;

[0075] P is the air pressure.

[0076] Furthermore, the risk level classification in the working step 4 of the substation micro-meteorological monitoring device and early warning system is specifically as follows:

[0077]

[0078] Among them: Low risk: Lightning activities are far away, the electric field changes slightly, and the equipment does not need special protection;

[0079] Medium risk: Lightning activity may be approaching, the electric field changes greatly, and the substation needs to be vigilant;

[0080] High risk: Lightning is about to occur or has occurred, and the system should take emergency measures immediately.

[0081] The benefits of this application are:

[0082] 1. The substation micro-meteorological monitoring device and early warning system can conduct comprehensive meteorological monitoring and lightning early warning. By integrating lightning sensors, it can expand the traditional six-element meteorological monitoring, provide real-time warnings for extreme weather such as lightning, and improve the disaster prevention capabilities of substations;

[0083] 2. The substation micro-meteorological monitoring device and early warning system use edge computing to improve real-time performance and reduce costs. Edge computing analysis is performed at the data collection point, which not only improves the real-time performance of data processing, but also reduces dependence on the central server and reduces the overall operating cost of the substation;

[0084] 1.3. The substation micro-meteorological monitoring device and early warning system have the function of linkage management, which comprehensively improves the safety of substation operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Figure 1 It is a schematic diagram of the system architecture of the present invention;

[0086] Figure 2 Schematic diagram of the working process of the system of the present invention. DETAILED DESCRIPTION

[0087] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments:

[0088] like Figure 1-2 As shown, a substation micro-meteorological monitoring system and early warning method are shown, including a six-element meteorological sensor, a lightning sensor, a data acquisition preprocessing module, an edge computing module, a communication module, a power supply module and a surge protection module. Figure 1As shown, the six-element meteorological sensors set in the substation micro-meteorological monitoring device and early warning system are used to collect conventional meteorological data such as ambient temperature, humidity, wind speed, wind direction, air pressure and rainfall; the lightning sensor monitors the occurrence of lightning activities through changes in the electric field. The lightning sensor can monitor the electric field change rate in real time, sense the approach of lightning in advance, and provide early warning signals for the system; the data acquisition preprocessing module converts the signals of the six-element meteorological sensors and lightning sensors into digital data, and performs preliminary signal processing; the edge computing module performs real-time data analysis locally on the device, combines the data of the lightning sensor and the six-element sensor, and determines whether there is a lightning risk through an algorithm, and predicts other meteorological elements, such as wind Speed, rainfall, high temperature and lightning, etc. The edge computing module is also responsible for executing the lightning warning algorithm and promptly feeding back the processing results; the communication module uploads the collected and analyzed data to the central server through a private communication protocol, and can also feed back real-time warning information to the substation control system to trigger the corresponding emergency response mechanism; the power supply module uses an efficient AC-DC converter to convert the conventional voltage of the substation into a stable low-voltage DC power supply device, and converts 220V AC voltage into 24V DC voltage; the surge protection module prevents surge voltage in the power system from causing serious damage to the equipment under extreme weather conditions such as lightning; prevents instantaneous surges in power supply voltage from affecting the normal operation of the equipment.

[0089] like Figure 2 As shown, the specific working steps of the substation micro-meteorological monitoring device and early warning system are as follows:

[0090] Step 1: Data collection;

[0091] Through the six-element meteorological sensors and lightning sensors data collection, each sensor will then convert the physical signals it senses into analog signals, and then convert the analog signals into digital signals for subsequent data processing; after the data conversion is completed, the collected data timestamps will be synchronized to ensure that the data collected from each sensor can be arranged in chronological order, providing a time reference for subsequent data analysis;

[0092] Step 2: Data preprocessing;

[0093] Data preprocessing is performed through the data acquisition preprocessing module to ensure data quality, remove noise and outliers, and provide accurate and reliable input data for subsequent analysis; the data preprocessing module first cleans and denoises the data, including low-pass filtering to remove high-frequency noise and retain important low-frequency information such as electric field signals; moving average filtering to smooth the data of meteorological sensors, reduce sudden instantaneous fluctuations, and obtain smoother signals; outlier detection processing, according to the sensor's range and expected value, set a reasonable threshold to eliminate obvious outliers or noise;

[0094] The low-pass filtering process shown is specifically:

[0095] To remove high-frequency noise from sensor data, the system uses a low-pass filter to retain low-frequency signals related to lightning. The low-pass filter processing formula is:

[0096] y[n]=α·x[n]+(1-α)·y[n-1]

[0097] Where: y[n] is the output of the nth sampling point after filtering;

[0098] x[n] is the value of the original signal at the nth sampling point;

[0099] α: filter coefficient, the value range is 0<α<1, which determines the smoothness of the filter;

[0100] The moving average filtering process shown is specifically as follows:

[0101] Moving average filtering smoothes instantaneous mutation signals by taking the average value of the data in the window; the formula is:

[0102]

[0103] Where: N is the window size;

[0104] y[n] is the output of the nth sampling point after filtering;

[0105] x[ni] is the i-th sampling point of the original signal in the window;

[0106] The outlier detection process shown is specifically as follows:

[0107] Outlier detection is to detect outliers in sensor data. Data judged as outliers will be marked and removed. The system uses the statistical 3σ rule, and the formula is:

[0108]

[0109] The outlier judgment conditions are:

[0110] x i is considered an outlier if and only if |x i -μ|>3σ

[0111] Where: μ is the mean of the data;

[0112] σ is the standard deviation of the data;

[0113] x i is the i-th data point;

[0114] Step 3: Feature extraction;

[0115] The feature extraction and data fusion steps are that the edge computing module obtains data from the data acquisition and preprocessing modules and extracts key features, and fuses these features to improve the accuracy of the warning. The feature extraction includes the electric field change rate, electric field strength, meteorological element characteristics, wind speed change rate, pressure gradient and temperature anomaly; specifically:

[0116] Electric field change rate: monitor the change of electric field through lightning sensor and calculate the electric field change rate;

[0117] The calculation formula is:

[0118]

[0119] in: is the rate of change of electric field;

[0120] E(t) is the electric field strength at the current moment;

[0121] E(t-Δt) is the electric field strength at the previous moment;

[0122] Δt is the sampling time interval;

[0123] Rapidly changing electric fields usually indicate that lightning activity is approaching; the electric field strength represents the intensity of lightning, and the electric field strength data is collected by lightning sensors; the electric field strength is defined as the maximum value of the electric field strength within a time window:

[0124]

[0125] Where: [t1, t2] is the start and end time of the analysis window;

[0126] Characteristics of meteorological elements; including conventional meteorological data such as wind speed, air pressure, temperature, and their rate of change; lightning is usually accompanied by strong winds and rapidly changing air pressure;

[0127] Wind speed change rate; rapid changes in wind speed may indicate the occurrence of severe convective weather, thereby increasing the possibility of lightning; its calculation formula is:

[0128]

[0129] Where: V w (t) is the wind speed at the current moment;

[0130] Δt is the sampling time interval;

[0131] Pressure gradient; the gradient change of pressure can indicate the development trend of thunderstorm weather; its calculation formula is:

[0132] ΔP=P(t)-P(t-Δt)

[0133] Where: ΔP is the pressure gradient;

[0134] P(t) is the air pressure at the current moment;

[0135] Temperature anomalies: Thunderstorms are more likely to develop under high temperature conditions. The system determines abnormal conditions by calculating the degree of temperature deviation:

[0136] T anomaly =T(t)-μ T

[0137] Where: T(t) is the temperature at the current moment;

[0138] μ T is the mean temperature in historical data;

[0139] Step 4: Data fusion;

[0140] The edge computing module integrates the collected lightning sensor data, wind speed data, air pressure data, and temperature data to form a comprehensive lightning risk assessment score. After data fusion, the lightning risk score R is calculated by weighted average. 雷电 , and conduct risk level assessment based on preset thresholds;

[0141] The lightning risk score R shown 雷电 Specifically:

[0142] According to the weight of the sensor, combined with the characteristics of electric field change rate, wind speed, air pressure, etc., a weighted average is calculated to measure the lightning risk. The calculation formula is:

[0143]

[0144] Where: R 雷电 Score lightning risk;

[0145] w1, w2, w3, and w4 are weight coefficients of each feature, representing the degree of influence of each data on lightning risk assessment;

[0146] is the rate of change of electric field;

[0147] E(t) is the electric field intensity;

[0148] V w is the wind speed;

[0149] P is the air pressure;

[0150] The risk level classification shown is as follows:

[0151]

[0152] Among them: Low risk: Lightning activities are far away, the electric field changes slightly, and the equipment does not need special protection;

[0153] Medium risk: Lightning activity may be approaching, the electric field changes greatly, and the substation needs to be vigilant;

[0154] High risk: Lightning is about to occur or has occurred, and the system should take emergency measures immediately;

[0155] Step 5: Early warning triggering;

[0156] When the risk level is medium or high, the system automatically triggers an alert and performs the following responses:

[0157] 1) Send warning signals; send warning information to the substation control center through the communication module to remind the staff to take protective measures; provide detailed risk analysis reports, including trend forecasts of lightning activities, potential threats to substation operations from the current meteorological environment, and recommended emergency treatment plans;

[0158] 2) Start equipment protection: For key equipment in the substation, the system can automatically start protection measures to avoid equipment damage or system failure caused by lightning strikes; the linkage mechanism includes:

[0159] Automatic power-off protection: When the risk reaches a high risk level, the system automatically disconnects the power supply of non-essential power equipment to reduce the threat of lightning strikes to operating equipment; with the authorization of the power dispatching center, the system cuts off the power supply line threatened by lightning through the circuit breaker;

[0160] High-voltage protection starts; the system automatically activates the lightning protection device; cooperates with the surge protector in the line to reduce the damage that may be caused by lightning current;

[0161] Start the environmental protection device; in view of the special environment of the substation, the system will trigger the protection mode under high-risk lightning conditions;

[0162] The system will automatically activate the fire alarm system's standby mode; at this point, the fire alarm system begins to enhance monitoring of the substation area;

[0163] Increase the frequency of fire detection; the detection frequency of fire alarm sensors is increased to twice that of normal mode to quickly capture possible signs of fire; increase vigilance in areas where fire hazards may exist.

[0164] The above description is only a preferred embodiment of the present invention and does not constitute any other form of limitation to the present invention. Any modification or equivalent change made based on the technical essence of the present invention still falls within the scope of protection required by the present invention.

Claims

1. A substation micro-meteorological monitoring system, comprising a six-element meteorological sensor, a lightning sensor, a data acquisition preprocessing module, an edge computing module, a communication module, a power supply module and a surge protection module, characterized in that: The six-element meteorological sensors set in the substation micro-meteorological monitoring device and early warning system are used to collect conventional meteorological data such as ambient temperature, humidity, wind speed, wind direction, air pressure and rainfall; the lightning sensor monitors the occurrence of lightning activities through changes in the electric field. The lightning sensor can monitor the rate of change of the electric field in real time, sense the approach of lightning in advance, and provide early warning signals for the system; the data acquisition preprocessing module converts the signals of the six-element meteorological sensors and lightning sensors into digital data, and performs preliminary signal processing; The edge computing module performs real-time data analysis locally on the device, combines the data from the lightning sensor and the six-element sensor, determines whether there is a lightning risk through an algorithm, and makes predictions about other meteorological elements. The edge computing module is also responsible for executing the lightning warning algorithm and promptly feeding back the processing results; the communication module uploads the collected and analyzed data to the central server through a private communication protocol, and can also feed back real-time warning information to the substation control system to trigger the corresponding emergency response mechanism; the power supply module uses an efficient AC-DC converter to convert the conventional voltage of the substation into a stable low-voltage direct current supply device; the surge protection module prevents surge voltages in the power system from causing serious damage to the equipment under extreme weather conditions such as lightning; and prevents instantaneous surges in the power supply voltage from affecting the normal operation of the equipment.

2. The early warning method of a substation micro-meteorological monitoring system according to claim 1 is characterized by: The specific working steps of the substation micro-meteorological monitoring device and early warning system are as follows: Step 1: Data collection; Through the six-element meteorological sensors and lightning sensors data collection, various sensors will then convert the physical signals they perceive into analog signals, and then convert the analog signals into digital signals for subsequent data processing; After the data conversion is completed, the timestamps of the collected data are synchronized to ensure that the data collected from each sensor can be arranged in chronological order, providing a time reference for subsequent data analysis; Step 2: Data preprocessing; Data preprocessing is performed through the data acquisition preprocessing module to ensure data quality, remove noise and outliers, and provide accurate and reliable input data for subsequent analysis; The data preprocessing module first cleans and denoises the data, including low-pass filtering to remove high-frequency noise and retain important low-frequency information such as electric field signals; Moving average filtering is used to smooth the data of meteorological sensors, reduce sudden instantaneous fluctuations, and obtain smoother signals. Outlier detection is used to set reasonable thresholds based on the sensor's range and expected value to eliminate obvious outliers or noise. Step 3: Feature extraction; The feature extraction and data fusion steps are that the edge computing module obtains data from the data acquisition and preprocessing modules and extracts key features, and fuses these features to improve the accuracy of the warning. The feature extraction includes the electric field change rate, electric field strength, meteorological element characteristics, wind speed change rate, pressure gradient and temperature anomaly; specifically: Electric field change rate: monitor the change of electric field through lightning sensor and calculate the electric field change rate; The calculation formula is: in: is the rate of change of electric field; E(t) is the electric field strength at the current moment; E(t-Δt) is the electric field strength at the previous moment; Δt is the sampling time interval; Rapidly changing electric fields usually indicate that lightning activity is approaching; the electric field strength represents the intensity of lightning, and the electric field strength data is collected by lightning sensors; the electric field strength is defined as the maximum value of the electric field strength within a time window: Where: [t1, t2] is the start and end time of the analysis window; Characteristics of meteorological elements; including conventional meteorological data such as wind speed, air pressure, temperature, and their rate of change; lightning is usually accompanied by strong winds and rapidly changing air pressure; Wind speed change rate; rapid changes in wind speed may indicate the occurrence of severe convective weather, thereby increasing the possibility of lightning; its calculation formula is: Where: V w (t) is the wind speed at the current moment; Δt is the sampling time interval; Pressure gradient; the gradient change of pressure can indicate the development trend of thunderstorm weather; its calculation formula is: ΔP=P(t)-P(t-Δt) Where: ΔP is the pressure gradient; P(t) is the air pressure at the current moment; Temperature anomalies: Thunderstorms are more likely to develop under high temperature conditions. The system determines abnormal conditions by calculating the degree of temperature deviation: T anomaly =T(t)-μ T Where: T(t) is the temperature at the current moment; μ T is the mean temperature in historical data; Step 4: Data fusion; The edge computing module integrates the collected lightning sensor data, wind speed data, air pressure data, and temperature data to form a comprehensive lightning risk assessment score. After data fusion, the lightning risk score R is calculated by weighted average. 雷电 , and conduct risk level assessment based on preset thresholds; Step 5: Early warning triggering; When the risk level is medium or high, the system automatically triggers an alert and performs the following responses: 1) Send warning signals; send warning information to the substation control center through the communication module to remind the staff to take protective measures; provide detailed risk analysis reports, including trend forecasts of lightning activities, potential threats to substation operations from the current meteorological environment, and recommended emergency treatment plans; 2) Start equipment protection: For key equipment in the substation, the system can automatically start protection measures to avoid equipment damage or system failure caused by lightning strikes; the linkage mechanism includes: Automatic power-off protection: When the risk reaches a high risk level, the system automatically disconnects the power supply of non-essential power equipment to reduce the threat of lightning strikes to operating equipment; with the authorization of the power dispatching center, the system cuts off the power supply line threatened by lightning through the circuit breaker; High-voltage protection starts; the system automatically activates the lightning protection device; cooperates with the surge protector in the line to reduce the damage that may be caused by lightning current; Start the environmental protection device; in view of the special environment of the substation, the system will trigger the protection mode under high-risk lightning conditions; The system will automatically activate the fire alarm system's standby mode; at this point, the fire alarm system begins to enhance monitoring of the substation area; Increase the frequency of fire detection; the detection frequency of fire alarm sensors is increased to twice that of normal mode to quickly capture possible signs of fire; increase vigilance in areas where fire hazards may exist.

3. The early warning method of a substation micro-meteorological monitoring system according to claim 2 is characterized by: The low-pass filtering process in the second working step of the substation micro-meteorological monitoring device and early warning system is specifically as follows: To remove high-frequency noise from sensor data, the system uses a low-pass filter to retain low-frequency signals related to lightning. The low-pass filter processing formula is: y[n]=α·x[n]+(1-α)·y[n-1] Where: y[n] is the output of the nth sampling point after filtering; x[n] is the value of the original signal at the nth sampling point; α: filter coefficient, the value range is 0<α<1, which determines the smoothness of the filter.

4. The early warning method of a substation micro-meteorological monitoring system according to claim 2 is characterized by: The moving average filtering process in the second working step of the substation micro-meteorological monitoring device and early warning system is specifically as follows: Moving average filtering smoothes instantaneous mutation signals by taking the average value of the data in the window; the formula is: Where: N is the window size; y[n] is the output of the nth sampling point after filtering; x[ni] is the i-th sampling point of the original signal in the window.

5. The early warning method of a substation micro-meteorological monitoring system according to claim 2 is characterized by: The abnormal value detection process in the second working step of the substation micro-meteorological monitoring device and early warning system is specifically as follows: Outlier detection is to detect outliers in sensor data. Data judged as outliers will be marked and removed. The system uses the statistical 3σ rule, and the formula is: The outlier judgment conditions are: x i is considered an outlier if and only if |x i -μ|>3σ Where: μ is the mean of the data; σ is the standard deviation of the data; x i is the ith data point.

6. The early warning method of a substation micro-meteorological monitoring system according to claim 2 is characterized by: The lightning risk score R in the fourth working step of the substation micro-meteorological monitoring device and early warning system 雷电 Specifically: According to the weight of the sensor, combined with the characteristics of electric field change rate, wind speed, air pressure, etc., a weighted average is calculated to measure the lightning risk. The calculation formula is: Where: R 雷电 Score lightning risk; w1, w2, w3, and w4 are weight coefficients of each feature, representing the degree of influence of each data on lightning risk assessment; is the rate of change of electric field; E(t) is the electric field intensity; V w is the wind speed; P is the air pressure.

7. The early warning method of a substation micro-meteorological monitoring system according to claim 2 is characterized by: The risk level classification in the working step 4 of the substation micro-meteorological monitoring device and early warning system is specifically as follows: Among them: Low risk: Lightning activities are far away, the electric field changes slightly, and the equipment does not need special protection; Medium risk: Lightning activity may be approaching, the electric field changes greatly, and the substation needs to be vigilant; High risk: Lightning is about to occur or has occurred, and the system should take emergency measures immediately.

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