Step-up substation monitoring and operation and maintenance system

Abstract

The present invention discloses a monitoring and operation and maintenance system for a booster station, which relates to the technical field of data management. By deploying multiple heterogeneous intelligent sensors inside the booster station, the operation status of equipment is monitored in real time, and an electromagnetic field sensor is used to sense the intensity of electromagnetic interference. When abnormal high-frequency pulses are detected, a data anomaly detection mode is triggered, and an error compensation value is calculated in combination with the degree of anomaly to correct the abnormal data and ensure the accuracy of the data. Based on the restored data, the equipment status is evaluated. If potential safety hazards are found, an intelligent warning is sent to the operation and maintenance personnel, and suggestions for adjusting the monitoring strategy or optimizing the shielding measures are provided. The present invention effectively improves the anti-interference ability and accuracy of the monitoring data of the booster station, reduces the risk of misjudgment in operation and maintenance caused by electromagnetic interference, enhances the reliability of intelligent monitoring in the ultra-high voltage power transmission scenario, and provides a strong guarantee for the safe and stable operation of the power system.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and more particularly to a monitoring and operation and maintenance system for step-up substations. Background Art

[0002] In modern power systems, as an important hub for power transmission and transformation, the operating status of step-up substations directly affects the safety and stability of the power grid. Traditional monitoring and operation and maintenance of step-up substations mainly rely on manual inspections and regular maintenance, which have problems such as poor real-time performance, lagging response, and complex fault troubleshooting. In recent years, with the development of Internet of Things, big data, and artificial intelligence technologies, intelligent monitoring and operation and maintenance systems have gradually been applied to step-up substation management. Through data collection, remote monitoring, and intelligent analysis, real-time monitoring and predictive maintenance of equipment status are achieved.

[0003] The existing technologies have the following deficiencies:

[0004] In step-up substations, strong electromagnetic interference (such as lightning strikes, electromagnetic pulses caused by high-voltage equipment failures, etc.) may cause abnormal drifts in the data collection of intelligent sensors, thereby affecting the accuracy of the monitoring system. Traditional filtering and error correction algorithms are difficult to cope with this high-intensity and instantaneous mutation interference, resulting in distorted equipment status assessment and even triggering incorrect operation and maintenance decisions. Especially in ultra-high voltage power transmission scenarios, this problem is more prominent. However, due to its low occurrence probability and difficulty in reproduction, existing systems often lack targeted optimization measures, affecting the reliability and accuracy of intelligent monitoring and operation and maintenance of step-up substations. Summary of the Invention

[0005] The purpose of the present invention is to provide a monitoring and operation and maintenance system for step-up substations to solve the deficiencies in the background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions: A monitoring and operation and maintenance system for step-up substations, including a data collection module, a data anomaly detection module, a multi-source data correction module, and an early warning and adjustment module;

[0007] Data collection module: A plurality of heterogeneous intelligent sensors are arranged in the step-up substation for real-time collection of equipment operation status data; among them, the electromagnetic interference intensity is real-time monitored through an electromagnetic field sensor, and when an abnormal high-frequency pulse is detected, the system is triggered to enter the data anomaly detection mode;

[0008] Data anomaly detection module: Analyze the correlation degree between the data collected by different types of sensors, judge the contingency of abnormal drifts occurring simultaneously in multiple sensors, and analyze the fluctuation of electromagnetic pulse interference in combination with the readings of the electromagnetic field sensor;

[0009] Multi-source data correction module: Combining the data anomaly detection results, evaluate the degree of data drift anomaly of intelligent sensors in the extreme electromagnetic pulse interference environment, and calculate the error compensation value based on the degree of drift anomaly to correct the intelligent sensor data;

[0010] Early warning adjustment module: After completing the data correction, evaluate the device status based on the restored data. If the extreme electromagnetic pulse interference causes potential safety hazards to the device, send a warning to the operation and maintenance personnel and provide adjustment suggestions, including adjusting the monitoring strategy or strengthening the shielding measures.

[0011] Preferably, the data acquisition module includes multiple heterogeneous intelligent sensors, and the intelligent sensors include temperature sensors, vibration sensors, optical sensors, current / voltage sensors, and electromagnetic field sensors, which are used to collect the temperature, vibration, discharge phenomenon, current, voltage, and electromagnetic interference intensity of the device respectively to improve the accuracy of data acquisition.

[0012] Preferably, after analyzing the correlation degree between the data collected by different types of sensors, determine the frequency of data drift of different sensors within the same time window, and compare it with the historical data under normal working conditions to generate a data drift frequency anomaly index. The acquisition method of the data drift frequency anomaly index is: within a fixed time window, collect the time series data of different sensors: Let the data collected by sensors A and B be respectively: ; ; where: is the value collected by sensor A at time , is the value collected by sensor B at time , and n is the number of data points within the time window;

[0013] Calculate the Pearson correlation coefficient of sensors A and B under normal working conditions : ; where: and are the means of the historical data respectively, is the value collected by sensor A at time , is the value collected by sensor B at time , calculate the Pearson correlation coefficient of the current time window : ; where: and are the means of the current time window respectively;

[0014] Calculate the correlation deviation between the correlation coefficient of the current time window and the historical correlation coefficient : ; Define the data drift frequency as , the window ratio where the correlation deviation Δr exceeds the threshold θ: ; where: is the indicator function, if then take 1, otherwise take 0, m is the total number of historical time windows; for the calculated correlation deviation and the data drift frequency After performing weighted average summation calculation, the data drift frequency anomaly index is obtained.

[0015] Preferably, after analyzing the fluctuation situation of electromagnetic pulse interference, an electromagnetic pulse interference fluctuation index is generated. The acquisition method of the electromagnetic pulse interference fluctuation index is as follows:

[0016] Within the time window Q, the electromagnetic field intensity signal x(t) is collected, where: x(t) represents the electromagnetic field intensity at time t, and the sampling frequency is set as fs; applying the short-time Fourier transform to the collected electromagnetic field intensity signal, the calculation formula is: ; where: represents the time-frequency spectrum at time t and frequency f, x(c) represents the value of the electromagnetic field intensity at the sampling point index c, which is a discrete-time signal sequence obtained by discretizing the electromagnetic field intensity signal x(t) at the sampling frequency fs, is the window function, f is the frequency component, is the kernel function of the short-time Fourier transform;

[0017] Set the window length as W, calculate the time-varying power spectral density, and the expression is: ; where, represents the power spectrum at time t and frequency f; calculate the power spectrum energy per unit time, and the expression is: ; where, represents the electromagnetic field power at time t, take the maximum electromagnetic field power of the electromagnetic pulse as the peak power , and calculate the average power within the entire time window , calculate the electromagnetic pulse interference fluctuation index, and the expression is: ; in the formula, QSZ is the electromagnetic pulse interference fluctuation index.

[0018] Preferably, the multi-source data correction module: combining the data anomaly detection results, evaluating the degree of data drift anomaly of intelligent sensors in an extreme electromagnetic pulse interference environment, normalizing the data drift frequency anomaly index and the electromagnetic pulse interference fluctuation index so that they are both within [0, 1], and calculating the intelligent sensor data drift anomaly factor according to the normalized data drift frequency anomaly index and the electromagnetic pulse interference fluctuation index.

[0019] Preferably, compare the obtained intelligent sensor data drift anomaly factor with a threshold value preset according to historical data. If the intelligent sensor data drift anomaly factor is greater than or equal to the preset threshold value, it indicates that the intelligent sensor data drift anomaly degree is high in the extreme electromagnetic pulse interference environment, and the corresponding intelligent sensor data is classified as abnormal data, and data correction is required; if the intelligent sensor data drift anomaly factor is less than the preset threshold value, it indicates that the intelligent sensor data drift anomaly degree is low in the extreme electromagnetic pulse interference environment, and the corresponding intelligent sensor data is classified as normal data.

[0020] Preferably, in combination with the drift anomaly degree, calculate an error compensation value to correct the intelligent sensor data, specifically including: calculating the data drift amount based on historical data comparison to quantify the error offset, and calculating the sensor data drift amount : ; where: is the current sensor measurement value, is the reference value; the error compensation value is used to adjust the abnormal data. Let the error compensation value be C, and the calculation formula is: ; where: C is the error compensation value, λ is the drift degree correction coefficient, used to adjust the compensation amplitude: the calculation method of the drift degree correction coefficient: ; is the intelligent sensor data drift anomaly factor, is the maximum intelligent sensor data drift anomaly factor recorded in the historical data;

[0021] Use the compensation value C to correct the original sensor data: ; is the corrected sensor data, and perform a data deviation verification after correction: calculate the deviation between the corrected data and the historical data , and the expression is: ; is the reference value of the sensor data in the normal state. If is less than 5%: it indicates that the corrected data is credible and can be used continuously; if is greater than or equal to 5%: it indicates that the correction fails, and consider discarding the data or performing secondary correction.

[0022] Preferably, after the early warning adjustment module completes the data correction, analyze the device status. If it is found that the device operating status exceeds the safe range, send different levels of early warning information to the operation and maintenance personnel according to the abnormal level, and provide corresponding adjustment suggestions, including increasing the data acquisition frequency, optimizing the filtering strategy, strengthening electromagnetic shielding, optimizing the grounding system, or adjusting the sensor layout.

[0023] In the above technical solution, the technical effects and advantages provided by the present invention:

[0024] 1. Through four major modules of data acquisition, data anomaly detection, multi-source data correction, and warning adjustment, the present invention realizes precise monitoring, anomaly analysis, correction, and operation and maintenance optimization of intelligent sensor data in an electromagnetic interference environment. Aiming at the problem that traditional technologies are difficult to effectively cope with extreme electromagnetic pulse interference, the present invention uses an electromagnetic field sensor to real-time monitor the interference intensity, combines multi-sensor data fusion analysis to identify the causes of abnormal data drift, and calculates the sensor data drift anomaly factor through the data drift frequency anomaly index and the electromagnetic pulse interference fluctuation index to ensure the accuracy and credibility of data correction. In addition, through an error compensation strategy, the disturbed data is adaptively corrected to improve the accuracy of data recovery, thereby avoiding the distortion of equipment status evaluation or mis-triggering of protection mechanisms caused by incorrect data and enhancing the operation reliability of the step-up substation.

[0025] 2. The implementation of the present invention effectively solves the problems in traditional step-up substation monitoring and operation and maintenance, such as difficult elimination of high-intensity transient interference, low error correction accuracy, and high false judgment rate, improving the anti-interference ability and intelligent level of the system. By dynamically adjusting the data acquisition frequency, optimizing the filtering strategy, strengthening electromagnetic shielding, etc., the stability of the step-up substation monitoring system in a complex electromagnetic environment is improved, the precise evaluation of the equipment status is ensured, and the operation and maintenance personnel are notified in a timely manner through an intelligent warning mechanism to prevent the abnormal situation of the equipment from evolving into a serious fault. The present invention is applicable to ultra-high voltage power transmission scenarios and can be widely used in the intelligent operation and maintenance of power systems, enhancing the safety of the power grid and realizing precise, intelligent, and efficient management of step-up substations. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings.

[0027] Figure 1 It is a system module diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0029] For the embodiments, please refer to Figure 1As shown in the figure, the step-up substation monitoring and operation and maintenance system in this embodiment includes a data acquisition module, a data anomaly detection module, a multi-source data correction module, and an early warning and adjustment module;

[0030] Data acquisition module: Multiple heterogeneous intelligent sensors are set up in the step-up substation to collect equipment operation status data in real time; among them, the electromagnetic interference intensity is monitored in real time through an electromagnetic field sensor, and when an abnormal high-frequency pulse is detected, the system is triggered to enter the data anomaly detection mode;

[0031] Data anomaly detection module: Analyze the correlation between the data collected by different types of sensors, judge the contingency of simultaneous abnormal drift of multiple sensors, and analyze the fluctuation of electromagnetic pulse interference in combination with the readings of the electromagnetic field sensor;

[0032] Multi-source data correction module: Combine the data anomaly detection results, evaluate the abnormal degree of data drift of intelligent sensors in an extreme electromagnetic pulse interference environment, and calculate the error compensation value in combination with the abnormal degree of drift to correct the intelligent sensor data;

[0033] Early warning and adjustment module: After the data correction is completed, evaluate the equipment status according to the restored data. If there are potential safety hazards in the equipment due to extreme electromagnetic pulse interference, send a warning to the operation and maintenance personnel and provide adjustment suggestions, including adjusting the monitoring strategy or strengthening the shielding measures.

[0034] To ensure the comprehensiveness and reliability of the data, the data acquisition module uses the following types of heterogeneous intelligent sensors to collect information on different physical quantities in the step-up substation:

[0035] Temperature sensor: Installed on key equipment such as main transformers, circuit breakers, and busbars to collect temperature data on the surface of the equipment and the surrounding environment in real time to monitor the heat dissipation of the equipment and abnormal heating.

[0036] Vibration sensor: Deployed on the transformer shell, cooling system, and circuit breaker mechanism components to detect mechanical vibration characteristics and identify possible looseness, wear, or abnormal operating conditions inside the equipment.

[0037] Optical sensor (visible light + infrared): Installed at key equipment and connection points to monitor abnormal discharge phenomena, insulation damage, and local overheating on the surface of the equipment through imaging technology.

[0038] Current / voltage sensor: Used to monitor the current and voltage status of busbars, cables, and transformers in real time to judge the stability of power transmission and whether there are abnormal power fluctuations.

[0039] Electromagnetic Field Sensor: This sensor is one of the key innovations of the present invention and is mainly used to monitor the electromagnetic environment inside the step-up substation in real time, especially for rapid detection and early warning of instantaneous high-intensity electromagnetic pulses (such as lightning strikes, arc discharges, switch operations, etc.).

[0040] The electromagnetic field sensor uses a high-sensitivity Hall effect probe or a wide-band electromagnetic wave receiving module and can detect electromagnetic wave signals in the range from low frequency (50Hz - 60Hz power frequency electromagnetic field) to high frequency (transient pulses in the MHz - GHz range). The specific working process is as follows:

[0041] The sensor continuously monitors the environmental electromagnetic field intensity and records the background electromagnetic noise reference value; a dynamic threshold is set to enable the system to distinguish normal electromagnetic field fluctuations (such as normal electromagnetic radiation during equipment operation) from abnormal interference signals.

[0042] Analyze the real-time electromagnetic waveform through fast Fourier transform, extract the spectral characteristics, and identify abnormal high-frequency electromagnetic pulses; combine short-time energy analysis to calculate the instantaneous intensity and duration of the electromagnetic pulse, and determine whether it reaches the interference level;

[0043] If a large-amplitude electric or magnetic field transition occurs within a short period (e.g., less than 1 millisecond), and the frequency falls within a specific range (such as 1MHz - 100MHz generated by lightning strikes, kHz - MHz caused by equipment switch operations), it is initially judged as a high-frequency interference event.

[0044] To prevent misjudgment, the electromagnetic field sensor cross-compares the signal anomaly situation with data from other sensors such as temperature, vibration, and current; for example, if an optical sensor detects an arc discharge or a current sensor detects an instantaneous current fluctuation at the same time as the electromagnetic interference occurs, the interference source can be further confirmed and marked.

[0045] When the electromagnetic field sensor detects an abnormal high-frequency pulse and after signal verification, the system automatically enters the data anomaly detection mode and performs the following operations:

[0046] Store the original data of all sensors at the current moment and mark this moment as an abnormal moment to prevent subsequent calculation errors based on abnormal data.

[0047] Increase the data acquisition frequency and improve the data update rate to more accurately capture the abnormal trend.

[0048] Send an electromagnetic interference early warning to the monitoring center or maintenance personnel, providing information such as the interference type, intensity, and affected range.

[0049] Provide basic data for subsequent abnormal data correction and equipment status evaluation to ensure that the system can automatically or manually intervene to handle data drift problems that may be caused by electromagnetic interference.

[0050] Data anomaly detection module: When the booster station is in normal operation, the data of various sensors should conform to certain physical laws. For example, temperature changes should be related to load conditions, current and voltage fluctuations should conform to power transmission characteristics, and vibration sensor signals should correspond to the working status of mechanical equipment. This module compares the data acquisition timestamps of different sensors to ensure that the data is in the same time window during analysis to avoid incorrect association judgments caused by data acquisition delays.

[0051] Sliding window statistical analysis is used to calculate the trend and amplitude of data changes of each sensor within a period of time (such as 1 second, 5 seconds, 1 minute), and establish a time series curve; for example, if the values ​​of the temperature sensor and the vibration sensor rise synchronously and the trend is consistent with the historical operating mode, the data is considered normal; but if the temperature data suddenly increases and the vibration data does not change significantly, there may be a sensor false alarm or a sudden abnormal event.

[0052] Calculate the correlation coefficient between different types of sensor data, for example: temperature sensor vs. load current sensor → temperature changes are usually closely related to load conditions; vibration sensor vs. equipment switch status sensor → when the equipment is turned on or off, the vibration should change accordingly; optical sensor (infrared temperature) vs. physical temperature sensor → monitor whether the temperature rise is reflected at the same time. If the abnormal change of a sensor cannot find a reasonable corresponding relationship in the data of other sensors, it may be due to the sensor itself, rather than external interference.

[0053] When multiple sensors are determined to be abnormal, it is necessary to further determine whether the probability of their simultaneous occurrence is accidental or is affected by common external factors (such as electromagnetic interference). This module uses the following methods to evaluate the randomness of abnormal drift:

[0054] Calculate the frequency of data drift in different sensors within the same time window and compare it with historical data under normal working conditions; if multiple sensors drift significantly at the same time within a short period of time (milliseconds or seconds), and the amplitude is far beyond the historical fluctuation range, it indicates that the abnormal drift may not be a random phenomenon, but is affected by external interference.

[0055] If the abnormal sensors are distributed in multiple areas of the entire booster station (for example, different parts of the main transformer, busbar, circuit breaker and other equipment), it may be a systemic problem, such as external interference; if the abnormality is only concentrated in multiple sensors of a single device, it may be a fault of the device itself, rather than a station-wide interference.

[0056] If the data drift is sudden (such as an instantaneous data jump), it may be caused by a short-term strong electromagnetic pulse; if the data drift occurs gradually (such as a slow offset within a few minutes or longer), it is more likely to be caused by sensor aging or environmental factors (such as temperature, humidity).

[0057] After analyzing the correlation degree between the data collected by different types of sensors, determine the frequency of data drift of different sensors within the same time window, and compare it with the historical data under normal working conditions to generate an abnormal index of data drift frequency. The method for obtaining the abnormal index of data drift frequency is as follows:

[0058] Within a fixed time window (such as T = 10 seconds), collect the time series data of different sensors: Let the data collected by sensors A and B be respectively: ; ; where: is the value collected by sensor A at time , is the value collected by sensor B at time , and n is the number of data points within the time window (such as 10 points are collected per second, then n = 100).

[0059] Calculate the Pearson correlation coefficient of sensors A and B under normal working conditions : ; where: and are the means of the historical data respectively, is the value collected by sensor A at time , is the value collected by sensor B at time . Calculate the Pearson correlation coefficient of the current time window: ; where: and are the means of the current time window respectively;

[0060] Calculate the correlation deviation between the correlation coefficient of the current time window and the historical correlation coefficient : ; if Δr is small (such as <0.1), it indicates that the correlation of sensor data remains basically stable and the drift is not obvious; if Δr is large (such as >0.5), it indicates that the correlation of sensor data has changed significantly and abnormal drift may occur.

[0061] Define the data drift frequency as , which represents the proportion of windows in the most recent m time windows (such as 60 windows in the most recent 10 minutes) where the correlation deviation Δr exceeds a certain threshold θ (such as 0.3): ; where: is an indicator function. If it takes 1, otherwise it takes 0. m is the total number of historical time windows (for example, if sampling is done 60 times in 10 minutes, then m = 60);

[0062] For the calculated correlation deviation and the data drift frequency After performing a weighted average summation calculation, the data drift frequency anomaly index DDFAI is obtained.

[0063] If DDFAI < 0.2, it indicates that the data is stable and there is no obvious drift; if 0.2 ≤ DDFAI < 0.5, it indicates that there may be slight drift and observation is needed; if DDFAI ≥ 0.5, it indicates that there is serious data drift, which may be caused by electromagnetic interference and measures need to be taken.

[0064] After discovering anomalies in the data of multiple sensors, this module will combine the readings of the electromagnetic field sensors to analyze whether the electromagnetic pulse is the main cause of the data drift.

[0065] Obtain the time series data of the electromagnetic field sensor and compare it with the anomaly occurrence times of other sensors; if the electromagnetic field intensity fluctuates violently (such as suddenly increasing to the kV / m level) immediately before the data drift of multiple sensors, it indicates that the data anomaly is related to the electromagnetic pulse.

[0066] Through the spectrum analysis function of the electromagnetic field sensor, extract the frequency range of the interference signal and compare it with the characteristics of common interference sources: Lightning strike interference → has more high-frequency components (MHz - GHz), short duration (<1ms), and steep waveform; Switching operation interference → mainly has a mutated waveform in the kHz - MHz range and may last for dozens of milliseconds; Arc discharge → has a longer duration, a relatively wide signal frequency, and is accompanied by abnormal data from the infrared optical sensor. If the interference spectrum characteristics match the known types of electromagnetic pulses, it can be confirmed that the data drift is caused by electromagnetic interference.

[0067] Statistically analyze the relationship between the electromagnetic pulse intensity and the data drift amplitude of the sensor, and analyze whether the degree of data anomaly shows a linear or non-linear correlation with the electromagnetic field change; for example: if the electromagnetic field fluctuation is small (such as <100V / m), but the data shows a large drift, it may be a sensor failure rather than interference; if the electromagnetic field suddenly jumps to the kV / m level and the data of multiple sensors mutates simultaneously, it can be judged that the data anomaly is caused by the electromagnetic pulse.

[0068] After analyzing the fluctuation situation of the electromagnetic pulse interference, an electromagnetic pulse interference fluctuation index is generated. The method for obtaining the electromagnetic pulse interference fluctuation index is as follows:

[0069] Within the time window Q, collect the electromagnetic field intensity signal x(t), where: x(t) represents the electromagnetic field intensity at time t (unit: V / m). Let the sampling frequency be fs (e.g., 10 kHz).

[0070] Apply the short-time Fourier transform to the collected electromagnetic field intensity signal. The calculation formula is: ; where: represents the time-frequency spectrum at time t and frequency f, x(c) represents the value of the electromagnetic field intensity at the moment when the sampling point index is c, and it is a discrete-time signal sequence obtained by discretizing the electromagnetic field intensity signal x(t) at the sampling frequency fs. That is, , where, is the sampling period, is the window function (such as Hanning window, Hamming window), f is the frequency component, is the kernel function of the short-time Fourier transform.

[0071] Set the window length to W (e.g., 10 ms, corresponding to W = 0.01×fs sampling points). Set the window overlap rate (e.g., 50%). Calculate the number of windows M: ; where the window step size = W×(1 - overlap rate), and N is the total number of data points of the signal. Calculate the time-varying power spectral density, and the expression is: ; where, represents the power spectrum at time t and frequency f; calculate the power spectrum energy per unit time, and the expression is: ; where, represents the electromagnetic field power at time t, and take the maximum electromagnetic field power of the electromagnetic pulse as the peak power , and calculate the average power within the entire time window , calculate the electromagnetic pulse interference fluctuation index, and the expression is: ; in the formula, QSZ is the electromagnetic pulse interference fluctuation index.

[0072] If QSZ < 0.3: The electromagnetic interference fluctuation is small and has little impact on the system. If 0.3 ≤ QSZ < 0.7: The electromagnetic interference fluctuation is obvious, and the influence range needs to be monitored. If QSZ ≥ 0.7: The electromagnetic interference fluctuation is severe, which may affect the sensor data or system stability. It is recommended to take protective measures (such as shielding, filtering).

[0073] Multi-source data correction module: Combine the data anomaly detection results, evaluate the abnormal degree of data drift of intelligent sensors in an extreme electromagnetic pulse interference environment, normalize the data drift frequency anomaly index and the electromagnetic pulse interference fluctuation index so that they are both within [0,1], and calculate the intelligent sensor data drift anomaly factor according to the normalized data drift frequency anomaly index and the electromagnetic pulse interference fluctuation index.

[0074] For example, the present invention can calculate the abnormal factor of intelligent sensor data drift using the following formula, and the calculation expression is: ; In the formula, is the abnormal factor of intelligent sensor data drift, is the abnormal index of data drift frequency, QSZ is the electromagnetic pulse interference fluctuation index, are the weight coefficients of the abnormal index of data drift frequency and the electromagnetic pulse interference fluctuation index (which can be optimized according to experimental experience or machine learning), and are all greater than 0.

[0075] Compare the obtained abnormal factor of intelligent sensor data drift with the threshold preset according to historical data. If the abnormal factor of intelligent sensor data drift is greater than or equal to the preset threshold, it indicates that the abnormal degree of intelligent sensor data drift in the extreme electromagnetic pulse interference environment is high, and the corresponding intelligent sensor data is classified as abnormal data and needs data correction; if the abnormal factor of intelligent sensor data drift is less than the preset threshold, it indicates that the abnormal degree of intelligent sensor data drift in the extreme electromagnetic pulse interference environment is low, and the corresponding intelligent sensor data is classified as normal data.

[0076] Combined with the abnormal degree of drift, calculate the error compensation value and correct the intelligent sensor data, which specifically includes:

[0077] Calculate the data drift amount based on historical data comparison to quantify the error offset, and calculate the sensor data drift amount : ; Among them: is the current sensor measurement value, is the reference value (obtained from historical data, other redundant sensors or prediction models).

[0078] The error compensation value is used to adjust the abnormal data to make it close to the true value. Let the error compensation value be C, and the calculation formula is: ; Among them: C is the error compensation value, λ is the drift degree correction coefficient (0 < λ ≤ 1), which is used to adjust the compensation amplitude: the calculation method of the drift degree correction coefficient: ; is the abnormal factor of intelligent sensor data drift, is the maximum abnormal factor of intelligent sensor data drift recorded in historical data.

[0079] Use the compensation value C to correct the original sensor data: ; is the corrected sensor data, if Beyond the normal operating range: It indicates that the data drift is too severe and may have failed, so the data is discarded or manual intervention is requested. If the C calculation is unstable (e.g., multiple sensors are abnormal): The historical mean value is used as the final correction value.

[0080] To ensure the reliability of the corrected data, it is necessary to verify the deviation of the corrected data: Calculate the deviation between the corrected data and the historical data , and the expression is: ; is the reference value of the sensor data under normal conditions. If is less than 5%: It indicates that the corrected data is credible and can be used continuously. If is greater than or equal to 5%: It indicates that the correction fails, and data discarding or secondary correction is considered.

[0081] For the corrected data, if its credibility is high, it can be used to update the historical database to improve the accuracy of subsequent corrections.

[0082] Early warning adjustment module: After completing the data correction, evaluate the device status based on the restored data. If the extreme electromagnetic pulse interference poses a safety hazard to the device, send an early warning to the operation and maintenance personnel and provide adjustment suggestions, including adjusting the monitoring strategy or strengthening the shielding measures.

[0083] Check whether the corrected data covers all key monitoring points (such as main transformers, circuit breakers, busbars, cables, etc.). Ensure that the data is smooth and there is no missing data. If there is still data anomaly, request manual review or supplement with redundant data.

[0084] Compare the corrected data with the historical operation data to judge whether the numerical fluctuation is reasonable. If the data still shows abnormal deviation (such as the device temperature changes abnormally large in a short time), re-evaluate the correction strategy to avoid the impact of incorrect data on the status evaluation results.

[0085] According to the restored data, check the following key device operation parameters:

[0086] Main transformer: Whether the winding temperature, oil temperature, current, and voltage are normal; whether there is abnormal vibration or electromagnetic noise. Circuit breaker: Whether the opening and closing status is abnormal and whether the contacts have abnormal heating. Busbar and cable: Whether there are voltage fluctuations, current overloads, or overheating of cable joints. Electromagnetic environment: Whether there is still high-intensity interference that may cause protection misoperation or measurement errors.

[0087] Combining historical data with the rated parameters of the device, analyze whether the restored data exceeds the normal range. Use the trend analysis method to judge whether the anomaly continues to increase and whether it may cause device failure or safety hazards. If it is found that the data fluctuates violently, it may be that the electromagnetic pulse interference has not been completely eliminated or the device is damaged, resulting in abnormal measurement values.

[0088] Calculate the abnormal influence level: Mild abnormality (such as short-term interference, and the equipment operation is still within the normal range): It can be continuously observed and no measures need to be taken temporarily. Moderate abnormality (such as voltage and current exceeding the standard but not exceeding the protection threshold): It is recommended to adjust the monitoring strategy, increase the data acquisition frequency, and closely monitor the change trend. Severe abnormality (such as overheating of the main transformer, abnormal discharge of the cable, and severe current fluctuation): Immediately send a warning to the operation and maintenance personnel and take protective measures.

[0089] If it is found that the equipment parameters exceed the safe operation range or there is a continuous abnormal trend, trigger the warning mechanism. The warning information includes: abnormal equipment, abnormal parameters, possible risks, and recommended measures to be taken.

[0090] Warning level classification: Low-level warning (yellow): Slight abnormality is detected, but the equipment operation is still stable. It is recommended to strengthen data monitoring. Medium-level warning (orange): The equipment status is abnormal and may affect the operation. It is recommended that the operation and maintenance personnel check as soon as possible. High-level warning (red): There are potential safety hazards, such as equipment overheating, arc discharge, etc. It is recommended to check immediately and take measures.

[0091] If it is found that the monitoring data is still affected by electromagnetic interference, the following measures can be taken:

[0092] Increase the data acquisition frequency: Increase the sampling frequency of key equipment to capture more detailed changes. Optimize the data filtering strategy: Adopt more advanced signal processing algorithms to reduce the influence of electromagnetic interference. Enable redundant sensors: Enable backup sensors at key measurement points to ensure data reliability. Increase data comparison: Cross-compare the current data with historical data and data of adjacent equipment to improve the monitoring accuracy.

[0093] Strengthen shielding and protection measures: If it is confirmed that the electromagnetic pulse interference is still strong and affects the equipment operation, it is necessary to further optimize the protection strategy: Enhance physical shielding: Increase electromagnetic shielding measures for key equipment, such as metal shielding covers, Faraday cages, etc. Optimize the grounding system: Check and improve the equipment grounding to reduce the coupling path of electromagnetic interference. Increase filtering devices: Install EMI filters at the sensor or cable ports to reduce high-frequency interference. Adjust the sensor layout: Move the sensors vulnerable to interference away from high electromagnetic radiation areas, such as transformers, high-voltage switches, etc.

[0094] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application.

Claims

1. The booster station monitoring and operation system is characterized by: It includes data acquisition module, data anomaly detection module, multi-source data correction module and early warning adjustment module; Data acquisition module: multiple heterogeneous intelligent sensors are set up in the booster station to collect equipment operation status data in real time; among them, the electromagnetic interference intensity is monitored in real time through the electromagnetic field sensor, and when an abnormal high-frequency pulse is detected, the system is triggered to enter the data anomaly detection mode; Data anomaly detection module: Analyzes the correlation between data collected by different types of sensors, determines the contingency of abnormal drift of multiple sensors at the same time, and analyzes the fluctuation of electromagnetic pulse interference in combination with the readings of electromagnetic field sensors; After analyzing the correlation between the data collected by different types of sensors, the frequency of data drift of different sensors in the same time window is determined, and compared with the historical data under normal working conditions to generate the data drift frequency anomaly index. The method for obtaining the data drift frequency anomaly index is as follows: within a fixed time window, collect time series data of different sensors: Assume that the data collected by sensors A and B are: ; ;in: is sensor A at time The collected values, is sensor B at time The collected values, n is the number of data points in the time window; Calculate the Pearson correlation coefficient of sensors A and B under normal operating conditions : ;in: and are the means of historical data, is sensor A at time The collected values, is sensor B at time Collected values, calculate the Pearson correlation coefficient of the current time window : ;in: and are the means of the current time window respectively; Calculate the correlation deviation between the correlation coefficient of the current time window and the historical correlation coefficient ; Define the data drift frequency as , correlation bias The proportion of windows exceeding the threshold θ: ;in: is an indicator function, if If , it takes 1, otherwise it takes 0, m is the total number of historical time windows; the calculated correlation deviation and data drift frequency The data drift frequency anomaly index is obtained after weighted average summation calculation; Multi-source data correction module: Combined with the data anomaly detection results, it evaluates the degree of data drift anomaly of smart sensors in extreme electromagnetic pulse interference environments, and calculates the error compensation value based on the degree of drift anomaly to correct the smart sensor data; Early warning and adjustment module: After completing data correction, the device status is evaluated based on the restored data. If extreme electromagnetic pulse interference causes a safety hazard to the equipment, an early warning is sent to the operation and maintenance personnel, and adjustment suggestions are provided, including adjusting the monitoring strategy or strengthening shielding measures.

2. The booster station monitoring and operation and maintenance system according to claim 1 is characterized in that: The data acquisition module includes multiple heterogeneous intelligent sensors, including temperature sensors, vibration sensors, optical sensors, current / voltage sensors and electromagnetic field sensors, which are used to respectively collect the temperature, vibration, discharge phenomenon, current, voltage and electromagnetic interference intensity of the equipment to improve the accuracy of data acquisition.

3. The booster station monitoring and operation and maintenance system according to claim 1 is characterized in that: After analyzing the fluctuation of electromagnetic pulse interference, an electromagnetic pulse interference fluctuation index is generated. The method for obtaining the electromagnetic pulse interference fluctuation index is as follows: In the time window Q, collect the electromagnetic field strength signal ,in: Represents the electromagnetic field strength at time t, and the sampling frequency is assumed to be fs; the short-time Fourier transform is applied to the collected electromagnetic field strength signal, and the calculation formula is: ;in: represents the time-frequency spectrum at time t and frequency f, Indicates the electromagnetic field strength value at the sampling point index c, which is the electromagnetic field strength signal The discrete-time signal sequence obtained by discretization at the sampling frequency fs is: is the window function, f is the frequency component, is the kernel function of short-time Fourier transform; Set the window length to W and calculate the time-varying power spectral density. The expression is: ;in, Represents the power spectrum at time t and frequency f; calculates the power spectrum energy per unit time, the expression is: ;in, represents the electromagnetic field power at time t, and the maximum electromagnetic field power of the electromagnetic pulse is taken as the peak power , and calculate the average power in the entire time window , calculate the electromagnetic pulse interference fluctuation index, the expression is: ; Where QSZ is the electromagnetic pulse interference fluctuation index.

4. The booster station monitoring and operation and maintenance system according to claim 3 is characterized in that: Multi-source data correction module: Combined with the data anomaly detection results, the data drift anomaly degree of the intelligent sensor in the extreme electromagnetic pulse interference environment is evaluated, and the data drift frequency anomaly index and the electromagnetic pulse interference fluctuation index are normalized so that they are both between [0,1]. The data drift anomaly factor of the intelligent sensor is calculated based on the normalized data drift frequency anomaly index and the electromagnetic pulse interference fluctuation index.

5. The booster station monitoring and operation and maintenance system according to claim 4 is characterized in that: The acquired smart sensor data drift anomaly factor is compared with a threshold value pre-set according to historical data. If the smart sensor data drift anomaly factor is greater than or equal to the pre-set threshold value, it indicates that the smart sensor data drift anomaly degree in the extreme electromagnetic pulse interference environment is high, and the corresponding smart sensor data is classified as abnormal data, requiring data correction; if the smart sensor data drift anomaly factor is less than the pre-set threshold value, it indicates that the smart sensor data drift anomaly degree in the extreme electromagnetic pulse interference environment is low, and the corresponding smart sensor data is classified as normal data.

6. The booster station monitoring and operation and maintenance system according to claim 5 is characterized in that: Combined with the degree of drift anomaly, the error compensation value is calculated to correct the smart sensor data, including: calculating the data drift amount based on historical data comparison to quantify the error offset, and calculating the sensor data drift amount ;in: is the current sensor measurement value, is the reference value; the error compensation value is used to adjust the abnormal data. Assuming the error compensation value is C, the calculation formula is: ; Where: C is the error compensation value, λ is the drift correction coefficient, which is used to adjust the compensation amplitude: The calculation method of the drift correction coefficient is: ; is the data drift anomaly factor of the smart sensor, is the maximum smart sensor data drift anomaly factor recorded in historical data; Use the compensation value C to correct the original sensor data: ; For the corrected sensor data, perform the correction data deviation verification: calculate the deviation between the corrected data and the historical data , the expression is: ; is the reference value of the sensor data under normal conditions. Less than 5%: This indicates that the calibration data is reliable and can be used continuously. Greater than or equal to 5%: This indicates that the calibration has failed and the data should be discarded or recalibrated.

7. The booster station monitoring and operation and maintenance system according to claim 6, characterized in that: After completing the data correction, the early warning adjustment module analyzes the equipment status. If it is found that the equipment operating status exceeds the safe range, different levels of early warning information are sent to the operation and maintenance personnel according to the abnormality level, and corresponding adjustment suggestions are provided, including increasing the data collection frequency, optimizing the filtering strategy, strengthening electromagnetic shielding, optimizing the grounding system or adjusting the sensor layout.

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