Power frequency characteristic self-calibration method and system of lightning arrester monitoring device

Abstract

The invention discloses a power frequency characteristic self-calibration method and system for a lightning arrester monitoring device, and relates to the technical field of lightning arresters, and the method comprises the following specific steps: collecting the power frequency characteristic parameters of the lightning arrester monitoring device and the environmental parameters of the lightning arrester monitoring device in real time; self-adaptive self-calibration is carried out based on a self-adaptive self-calibration trigger mode by combining power frequency characteristic parameters and environmental parameters of the lightning arrester monitoring device and self-calibration historical data of lightning arrester monitoring devices of the same type; and verifying the self-adaptive self-calibration reference value, obtaining a verification deviation value according to a verification result of the self-adaptive self-calibration reference value, and performing internal self-calibration based on an internal self-calibration triggering model. According to the self-calibration method, self-adaptive self-calibration, internal self-calibration and external self-calibration can be used for mutual cross validation, and multi-level self-calibration can be carried out, so that more self-calibration means are provided, and the self-calibration precision is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of lightning arresters, in particular to a power frequency characteristic self-calibration method and system for a lightning arrester monitoring device. BACKGROUND

[0002] At present, due to the harmfulness of lightning weather, lightning arresters often need to be installed on buildings. The lightning arrester is an electrical appliance for protecting electrical equipment from high transient overvoltage and limiting the current flow time and also limiting the current flow amplitude. In order to monitor the working condition of the lightning arrester, a corresponding lightning arrester monitoring device needs to be set. The lightning arrester monitoring device is a device for monitoring the working state of the lightning arrester in real time.

[0003] In the operation of the power system, the lightning arrester as a key overvoltage protection device, its performance stability directly affects the safety of the power grid. In order to realize real-time monitoring of the state of the lightning arrester, the existing technology generally uses a lightning arrester monitoring device to collect leakage current, resistive component, full current phase and other power frequency characteristic parameters, and correct the data in combination with temperature, humidity, electromagnetic interference and other environmental factors. The current mainstream calibration methods include external calibration by periodically comparing standard sources manually, internal calibration based on built-in reference signals, and adaptive calibration with automatic compensation at fixed periods. Some systems also introduce a multiple regression model for trend prediction. These methods have been widely used in substations, transmission lines and traction power supply systems, forming the basic framework of lightning arrester state perception.

[0004] In the use process of the lightning arrester monitoring device, self-calibration needs to be performed according to the generated power frequency characteristics. The self-calibration method usually injects a standard signal with known accuracy, measures the value, compares the measured value with the standard value, and then performs self-calibration correction.

[0005] However, the above-mentioned existing calibration mechanism has obvious limitations: on the one hand, each calibration mode operates independently, lacks mutual verification and collaborative fusion mechanism, and it is difficult to find when a module deviates; on the other hand, the adaptive calibration mostly uses static threshold triggering, which cannot dynamically respond to environmental mutations (such as temperature difference exceeding 10℃, humidity rising by 30%) or power frequency characteristic drift (amplitude change >5%, phase shift >5°), resulting in that the calibration lags behind the real error development. In addition, the internal calibration relies on the injection of fixed standard sources, and the aging of elements over a long period of operation can easily cause the reference to be inaccurate; although the external calibration has high accuracy, it has low frequency and high cost, and is usually performed only once a year, which cannot cover the sudden degradation process. More importantly, the three types of calibration results are not weighted and fused, and the final output is still mainly based on a single mode, which does not fully utilize multi-source information to improve the overall calibration reliability.

[0006] The above self-calibration means is single, only by injecting a test signal, and then performing self-calibration, when a large error occurs during later verification, it is inconvenient to use other means for cross verification and reference, and the self-calibration accuracy is affected. Therefore, the power frequency characteristic self-calibration method and system of the lightning arrester monitoring device are proposed. SUMMARY

[0007] The purpose of the present application is to provide a power frequency characteristic self-calibration method and system of a lightning arrester monitoring device to solve the technical problem that the traditional calibration method is only single-point, periodic or static threshold triggering, lacks a multi-level collaborative calibration mechanism, and is difficult to achieve high-precision, continuous and reliable online measurement due to the drift of power frequency characteristic parameters caused by environmental changes and component aging in the long-term operation of the existing lightning arrester monitoring device.

[0008] To achieve the above purpose, the present application provides the following technical solutions: In a first aspect, the present application provides a power frequency characteristic self-calibration method of a lightning arrester monitoring device, comprising the following specific steps: Real-time collection of power frequency characteristic parameters of the lightning arrester monitoring device and environmental parameters of the lightning arrester monitoring device; Combined with the power frequency characteristic parameters and environmental parameters of the lightning arrester monitoring device and the self-calibration historical data of the same type of lightning arrester monitoring device, adaptive self-calibration is performed based on an adaptive self-calibration trigger mode to obtain an adaptive self-calibration reference value; Verification of the adaptive self-calibration reference value, and derivation of a verification deviation value according to the adaptive self-calibration reference value verification result, internal self-calibration based on an internal self-calibration trigger model; Verification of the internal self-calibration reference value, and derivation of a verification deviation value according to the internal self-calibration reference value verification result, external self-calibration based on an external self-calibration trigger model; Data interaction and fusion of the adaptive self-calibration reference, internal self-calibration reference value and external self-calibration reference value to obtain a final calibration value; Storage of the power frequency characteristic parameters of the lightning arrester monitoring device, the environmental parameters of the lightning arrester monitoring device, the adaptive self-calibration data, the internal self-calibration data, the external self-calibration data and the final calibration value.

[0009] As a further improvement of the present application, the adaptive self-calibration trigger mode specifically includes: The environmental parameters include temperature parameters, humidity parameters and electromagnetic interference parameters, and temperature change thresholds, humidity change thresholds and electromagnetic interference change thresholds are set for the environmental parameters. When the real-time collected environmental parameters exceed any set corresponding threshold, adaptive self-calibration is triggered; Real-time monitoring of power frequency characteristic parameters, and setting the power frequency characteristic parameter deviation threshold, analyzing the power frequency characteristic parameter deviation every ten minutes, when the deviation exceeds the deviation threshold, triggering the adaptive self-calibration.

[0010] As a further improvement of the present application, the internal self-calibration trigger model is as follows: The internal self-calibration trigger model is divided into active internal self-calibration and passive internal self-calibration. Active internal self-calibration: set the internal self-calibration to be triggered actively when the arrester monitoring device is in low load operation every day, so as to perform internal self-calibration. Passive internal self-calibration: quantize the adaptive self-calibration reference value and the verification deviation value of the reference value, and record the adaptive self-calibration reference value as A, the verification deviation value of the reference value as a, set the passive trigger threshold of the internal self-calibration as Y, continuously capture the values of the last three A and a, and record them as A n and a n , A n-1 and a n-1 , and A n-2 and a n-2 , then substitute them into the passive trigger formula of the internal self-calibration to obtain the passive trigger judgment factor X of the internal self-calibration, and then judge the value size of X and Y, when X≥Y, the passive trigger of the internal self-calibration is triggered, and the internal self-calibration is performed, otherwise it is not triggered, wherein the passive trigger formula of the internal self-calibration is: X=|A n -a n |×λ1+|A n-1 -a n-1 |×λ2+|A n-2 -a n-2 |×λ3, wherein λ1, λ2, and λ3 are weight coefficients, λ1 is in the range of 0.3-0.5, λ2 is in the range of 0.2-0.3, and λ3 is in the range of 0.2-0.3.

[0011] As a further improvement of the present application, the external self-calibration trigger model is as follows: The external self-calibration trigger model is divided into active external self-calibration and passive external self-calibration. Active external self-calibration: every one to two years, professional personnel perform external self-calibration. Passive external self-calibration: set the deviation threshold of the internal self-calibration, real-time collect the reference value verification deviation value of the internal self-calibration, so as to determine the deviation, when the deviation is greater than the deviation threshold for three times in a row, the passive trigger of the external self-calibration is triggered, and the external self-calibration is performed.

[0012] As a further improvement of the application, the final calibration value = adaptive self-calibration reference value x a + internal self-calibration reference x b + external self-calibration reference x g, wherein a + b + g = 1, and the value range of a, b and g is dynamically adjusted.

[0013] As a further improvement of the application, the internal self-calibration specific steps are as follows: A stable power frequency voltage or current signal is generated by using a high-precision reference source to generate a standard signal; The standard signal is injected into the measurement loop through a switching circuit; The measurement data of the lightning arrester monitoring device and the standard signal are synchronously collected, and the amplitude deviation and phase deviation are calculated; Internal self-calibration is performed according to the amplitude deviation and phase deviation.

[0014] As a further improvement of the application, the external self-calibration specific steps are as follows: The standard voltage source and the standard current source are connected to the measurement input of the lightning arrester monitoring device through a special interface; The lightning arrester monitoring device is put into calibration mode through on-site operation or remote instruction, and the actual measurement is temporarily interrupted, and switched to calibration signal input; External self-calibration is performed; After calibration, a set of standard signals with known parameters are input, and the repeatability error and accuracy of the measurement value are calculated by repeating the measurement multiple times.

[0015] As a further improvement of the application, the adaptive self-calibration specific steps are as follows: A large amount of power frequency characteristic data of the lightning arrester monitoring device in normal state is collected, then the characteristic parameters are extracted, and a multiple linear regression model is established, the deviation between the current measurement data and the reference model is monitored in real time by inputting the characteristic parameters to the multiple linear regression model, and when the deviation exceeds the threshold value, the adaptive self-calibration process is triggered.

[0016] As a further improvement of the application, when the deviation of the calibration results of external self-calibration, internal self-calibration and adaptive self-calibration is > 5%, the reference value of external self-calibration is preferred, and fault troubleshooting is performed; If 3% < internal standard source and adaptive self-calibration deviation < 5%, the internal standard source result is used as the reference, and the retraining of the adaptive self-calibration trigger mode model is triggered at the same time; After external calibration, the reference parameters of the internal standard source and the adaptive self-calibration are forced to be synchronized to ensure the consistency of the calibration chain.

[0017] In the second aspect, the application provides a power frequency characteristic self-calibration system of a lightning arrester monitoring device, which implements the power frequency characteristic self-calibration method of the lightning arrester monitoring device as described in the preceding item, and the system comprises: The signal acquisition module is configured to acquire the power frequency characteristic parameters and the environmental parameters of the lightning arrester monitoring device. The self-calibration module is configured to perform adaptive self-calibration based on an adaptive self-calibration trigger mode in combination with the power frequency characteristic parameters and the environmental parameters of the lightning arrester monitoring device and the self-calibration historical data of the lightning arrester monitoring device of the same type, to obtain an adaptive self-calibration reference value. The verification and feedback module is configured to verify the adaptive self-calibration reference value, and obtain a verification deviation value according to the verification result of the adaptive self-calibration reference value, to perform internal self-calibration based on an internal self-calibration trigger model; verify the internal self-calibration reference value, and obtain a verification deviation value according to the verification result of the internal self-calibration reference value, to perform external self-calibration based on an external self-calibration trigger model; and perform data interaction and fusion on the adaptive self-calibration reference value, the internal self-calibration reference value and the external self-calibration reference value, to obtain a final calibration value. The storage module is configured to store the power frequency characteristic parameters of the lightning arrester monitoring device, the environmental parameters of the lightning arrester monitoring device, the adaptive self-calibration data, the internal self-calibration data, the external self-calibration data and the final calibration value.

[0018] Compared with the prior art, the technical effects of the present application are as follows: The present application combines the power frequency characteristic parameters and the environmental parameters of the lightning arrester monitoring device and the self-calibration historical data of the lightning arrester monitoring device of the same type, and performs adaptive self-calibration based on an adaptive self-calibration trigger mode. At this time, adaptive self-calibration, that is, regular self-calibration, is performed. Then, the adaptive self-calibration reference value is verified, and a verification deviation value is obtained according to the verification result of the adaptive self-calibration reference value. According to the verification deviation value, internal self-calibration is performed based on an internal self-calibration trigger model. At this time, internal self-calibration is performed. Then, the internal self-calibration reference value is verified, and a verification deviation value is obtained according to the verification result of the internal self-calibration reference value. According to the verification deviation value, external self-calibration is performed based on an external self-calibration trigger model. At this time, external self-calibration is performed. Finally, the self-calibration data is fused, to obtain a final calibration value. This design not only allows adaptive self-calibration, internal self-calibration and external self-calibration to cross-verify with each other, but also allows multi-level self-calibration. This design not only provides more self-calibration means, but also improves the self-calibration precision. Furthermore, by setting a temporary self-calibration strategy, when significant deviations occur in adaptive self-calibration, internal self-calibration, and external self-calibration, the external self-calibration reference value is temporarily prioritized for self-calibration. When significant deviations occur in internal self-calibration and adaptive self-calibration, the internal self-calibration reference value is temporarily prioritized, thereby determining the temporary availability of self-calibration. After external calibration, the internal standard source and the reference parameters of adaptive self-calibration are forcibly synchronized. After internal calibration, the reference parameters of adaptive self-calibration are forcibly synchronized to ensure the consistency of the calibration chain. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the power frequency characteristic self-calibration method of the surge arrester monitoring device of this application.

[0020] Figure 2 This is a schematic diagram of the power frequency characteristic self-calibration system of the surge arrester monitoring device of this application. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] This application provides, as follows: Figure 1 - Figure 2 The power frequency characteristic self-calibration method of the surge arrester monitoring device shown includes the following specific steps: Step 1: Real-time acquisition of power frequency characteristic parameters and environmental parameters of the surge arrester monitoring device, and formation of a historical database: Step 2: Combining the power frequency characteristic parameters and environmental parameters of the surge arrester monitoring device with the self-calibration historical data of similar surge arrester monitoring devices, perform adaptive self-calibration based on the adaptive self-calibration trigger mode: Step 3: Verify the adaptive self-calibration benchmark value, and based on the verification results, derive the verification deviation value. Then, perform internal self-calibration based on the internal self-calibration trigger model. Step 4: Verify the internal self-calibration benchmark value, and based on the verification results, obtain the verification deviation value. Then, perform external self-calibration based on the external self-calibration trigger model. Step 5: Perform data interaction and fusion between the adaptive self-calibration reference, the internal self-calibration reference value, and the external self-calibration reference value to obtain the final calibration value. Step 6: Store the power frequency characteristic parameters of the surge arrester monitoring device, the environmental parameters of the surge arrester monitoring device, the adaptive self-calibration data, the internal self-calibration data, the external self-calibration data, and the final calibration value.

[0023] First, combining the power frequency characteristic parameters and environmental parameters of the surge arrester monitoring device with the self-calibration historical data of similar surge arrester monitoring devices, adaptive self-calibration is performed based on the adaptive self-calibration trigger mode. This adaptive self-calibration is also known as routine self-calibration. Then, the adaptive self-calibration reference value is verified, and the verification deviation value is obtained based on the verification result. Based on the verification deviation value, internal self-calibration is performed based on the internal self-calibration trigger model. This internal self-calibration is then performed, and the internal self-calibration reference value is verified again. Based on the verification result, the verification deviation value is obtained, and based on the verification deviation value, external self-calibration is performed based on the external self-calibration trigger model. Finally, the self-calibration data is fused to obtain the final calibration value. This design not only allows for cross-verification of adaptive self-calibration, internal self-calibration, and external self-calibration, but also enables multi-level self-calibration, providing more self-calibration methods and improving self-calibration accuracy.

[0024] This invention, by constructing a three-level linkage "adaptive-internal-external" composite self-calibration system, achieves for the first time closed-loop calibration of surge arrester monitoring devices under complex operating conditions. By introducing an adaptive self-calibration mechanism jointly driven by environmental parameters (temperature, humidity, electromagnetic interference) and power frequency characteristic parameters (leakage current amplitude, resistive component, phase), real-time perception and dynamic response to measurement drift are achieved, significantly improving calibration timeliness. An innovative passive triggering model for internal self-calibration is designed, using weighted calculation of three consecutive sampling results to determine whether triggering is necessary, avoiding false alarms due to occasional fluctuations and improving calibration stability. External self-calibration serves as the highest-level reference, correcting embedded standard source drift and algorithm deviation, forming a top-level correction mechanism. The three types of calibration results are weighted and fused to generate the final calibration value. The weights can be dynamically adjusted according to the equipment's service life and the degree of environmental change. In daily operation, adaptive calibration is the primary method, while after major overhauls, external calibration takes the lead, achieving intelligent allocation. The entire system supports long-term unattended operation, and when deployed at key nodes of the power system, it can reduce the frequency of manual maintenance by more than 70%, effectively reducing operation and maintenance costs. Tests show that after calibration using this method, the repeatability error of leakage current measurement decreased from 4.2% to 0.48%, and the phase deviation decreased from 6° to 0.8°, meeting the requirements of IEC 60990 standard and suitable for high-reliability scenarios such as UHV substations and rail transit traction substations.

[0025] Furthermore, the specific details of the adaptive self-calibration trigger mode are as follows: Environmental parameters include temperature, humidity, and electromagnetic interference. Thresholds for temperature, humidity, and electromagnetic interference changes are set for these parameters. When the real-time collected environmental parameters exceed any of the set thresholds, adaptive self-calibration is triggered. The system monitors changes in power frequency characteristic parameters in real time and sets a deviation threshold for these parameters. It analyzes the deviation of power frequency characteristic parameters every ten minutes. When the deviation exceeds the threshold, it triggers an adaptive self-calibration mechanism. This design allows the adaptive self-calibration to combine changes in power frequency characteristic parameters with changes in environmental parameters, thereby performing routine self-calibration and achieving dynamic self-calibration.

[0026] Furthermore, the specific details of the internal self-calibration trigger model are as follows: The internal self-calibration trigger model is divided into active internal self-calibration and passive internal self-calibration. Active internal self-calibration: When the surge arrester monitoring device is running at low load every day, the internal self-calibration is actively triggered to perform internal self-calibration; Passive internal self-calibration: The adaptive self-calibration baseline value and the verification deviation value of the baseline value are quantified. The adaptive self-calibration baseline value is recorded as A, and the verification deviation value of the baseline value is recorded as a. The passive trigger threshold for internal self-calibration is set to Y. The values ​​of A and a are captured three times most recently and recorded as A. n and a n A n-1 and a n-1 And A n-2 and a n-2 Then, substituting these values ​​into the internal self-calibration passive trigger formula yields the internal self-calibration passive trigger judgment factor X. The values ​​of X and Y are then compared. If X ≥ Y, internal self-calibration is passively triggered, thus performing internal self-calibration; otherwise, it is not triggered. The internal self-calibration passive trigger formula is: X = |A n -a n |×λ1+|A n-1 -a n-1 |×λ2+|A n-2 -a n-2 |×λ3, where λ1 ranges from 0.3 to 0.5, λ2 ranges from 0.2 to 0.3, and λ3 ranges from 0.2 to 0.3; Y can be set to 5%. When the value of X is greater than or equal to Y, internal self-calibration is performed passively. Data captured three times consecutively can avoid external self-calibration being directly triggered by a change in a single data point. Furthermore, the triggering conditions can be adjusted according to the actual situation by adjusting the weights.

[0027] Furthermore, the specific details of the external self-calibration trigger model are as follows: The external self-calibration trigger model is divided into active external self-calibration and passive external self-calibration. Active external self-calibration: External self-calibration is performed by professionals every one to two years; Passive external self-calibration: Set the deviation threshold for internal self-calibration, collect the internal self-calibration benchmark value in real time to verify the deviation value, and thus determine the deviation situation. When the deviation exceeds the threshold three times in a row, external self-calibration is passively triggered to perform external self-calibration. This allows for the use of external self-calibration to verify and further self-calibrate internal and adaptive self-calibration, thereby ensuring calibration accuracy. The deviation threshold here can be set to 2%. When the deviation exceeds 2% three times in a row, external self-calibration is passively triggered.

[0028] Furthermore, the final calibration value = adaptive self-calibration reference value × α + internal self-calibration reference × β + external self-calibration reference × γ, where α + β + γ = 1, and the values ​​of α, β, and γ are dynamically adjusted. For example, under normal circumstances, α is 0.6, β is 0.25, and γ is 0.25. When the surge arrester monitoring device has undergone a major overhaul, within one week, α is 0.5, β is 0.2, and γ is 0.3. These values ​​can be dynamically adjusted according to the actual situation.

[0029] Furthermore, the specific steps for internal self-calibration are as follows: Step 11: Use a high-precision reference source to generate a stable power frequency voltage or current signal, thereby generating a standard signal; Step 12: Inject the standard signal into the measurement circuit through the switching circuit; Step 13: Synchronously acquire measurement data and standard signals from the surge arrester monitoring device, and calculate amplitude deviation and phase deviation; Step 14: Perform internal self-calibration based on the amplitude deviation and phase deviation.

[0030] A stable power frequency voltage or current signal is generated using a precision resistor divider or capacitor divider, with amplitude and phase accuracy meeting the calibration requirement of ≤0.1% error. Without affecting the normal operation of the surge arrester monitoring device, a standard signal is injected into the leakage current loop via a switching circuit. Measurement data from the surge arrester monitoring device and the standard signal are simultaneously acquired, and the current measurement error and resistive current phase error are calculated. Calibration coefficients (such as gain coefficient and phase compensation angle) are generated based on the deviation values, and the measurement results are automatically corrected through software algorithms, or hardware circuit parameters are adjusted. Internal self-calibration can also employ other existing self-calibration methods.

[0031] Furthermore, the specific steps for external self-calibration are as follows: Step 21: Connect the standard voltage source and standard current source to the measurement input terminal of the surge arrester monitoring device through a dedicated interface; Step 22: Through on-site operation or remote command, put the surge arrester monitoring device into calibration mode, temporarily interrupt the actual measurement, and switch to calibration signal input; Step 23: Perform external self-calibration: Step 24: After calibration, input a set of standard signals with known parameters, repeat the measurement at least 3 times, and calculate the repeatability error and accuracy of the measured values.

[0032] The simulated system power frequency voltage and simulated surge arrester leakage current are connected to the measurement input terminal of the surge arrester monitoring device through a dedicated interface to ensure electrical isolation and safety. The surge arrester monitoring device is put into calibration mode via on-site operation or remote command, temporarily interrupting actual measurements and switching to calibration signal input. Amplitude calibration: Input 1mA, 10mA, and 100mA standard currents to verify the linearity of the surge arrester monitoring device and correct nonlinear errors. Phase calibration: Inject the resistive current phase of the simulated surge arrester with a known phase difference, calibrating the phase measurement accuracy to meet a phase error ≤1°. Power loss calibration: Simulate the active power loss of the surge arrester using a standard power source to verify the accuracy of the power calculation algorithm. After calibration, input a set of standard signals with known parameters and repeat the measurement at least three times, calculating the repeatability error of the measured values ​​≤0.5% and the accuracy to ensure effective calibration. External self-calibration can also employ other existing self-calibration methods.

[0033] Furthermore, the specific steps of adaptive self-calibration are as follows: Step 31: Collect a large amount of power frequency characteristic data of the surge arrester monitoring device under normal conditions, then extract the characteristic parameters, and establish a multiple linear regression model by inputting the characteristic parameters into the multiple linear regression model; Step 32: Monitor the deviation between the current measurement data and the benchmark model in real time. When the deviation exceeds the threshold, trigger the adaptive self-calibration process.

[0034] The system collects leakage current waveforms from the surge arrester monitoring device under normal conditions for one week, extracts the fundamental amplitude, harmonic components, and phase angle, and establishes a multiple linear regression model. This model can fuse multiple input features using existing technologies. It monitors the deviation between the current measurement data and the benchmark model in real time. When the amplitude deviation >5% or the phase deviation >5°, an adaptive self-calibration process is triggered. Temperature compensation: A temperature-error relationship model is established to automatically adjust the measured values ​​according to changes in ambient temperature. For every 10°C increase in temperature, the leakage current may increase by 2% to 5%. Drift correction: Calibration coefficients are updated in real time to compensate for drift errors caused by component aging. As operational data accumulates, the algorithm automatically optimizes the benchmark model, improving calibration accuracy and adapting to parameter changes during long-term operation. The adaptive self-calibration can also employ other existing self-calibration methods.

[0035] Furthermore, when the deviation of the calibration results of external self-calibration, internal self-calibration and adaptive self-calibration is >5%, the reference value of external self-calibration shall be used first, and troubleshooting shall be performed. If the deviation between the internal standard source and the adaptive self-calibration is greater than 3%, the result of the internal standard source shall prevail, and the model in the adaptive self-calibration trigger mode shall be retrained. After external calibration, the reference parameters of the internal standard source and the adaptive self-calibration are forcibly synchronized to ensure the consistency of the calibration chain. By setting a temporary self-calibration strategy, if there are large deviations in adaptive self-calibration, internal self-calibration and external self-calibration, the external self-calibration reference value is temporarily prioritized for self-calibration. If there are large deviations in internal self-calibration and adaptive self-calibration, the internal self-calibration base value is temporarily prioritized, thereby determining the temporary availability of self-calibration. After external calibration, the reference parameters of the internal standard source and the adaptive self-calibration are forcibly synchronized. After internal calibration, the reference parameters of the adaptive self-calibration are forcibly synchronized to ensure the consistency of the calibration chain.

[0036] To address the conflicting results from multiple calibration sources, this invention establishes a three-tiered calibration priority and fusion feedback mechanism. When the deviation between the three calibration results exceeds 5%, it is considered a serious inaccuracy. The external calibration result is prioritized as the true benchmark, and an "excessive calibration difference" alarm is automatically reported, triggering the equipment inspection process. When the deviation between the internal standard source and the adaptive self-calibration output is between 3% and 5%, the algorithm is considered to be slowly degrading. The internal calibration result is retained, and model retraining is triggered to update the regression coefficients. After external calibration is completed, the system broadcasts the calibration benchmark, forcibly refreshing the calibration parameters in the internal standard source and the adaptive self-calibration, forming a unified benchmark chain. In practical applications, the calibration consistency tolerance bandwidth can be set to ±2%.

[0037] In existing technologies, surge arrester monitoring devices operate long-term in substations with strong electromagnetic interference and drastic temperature and humidity changes. Components in their measurement circuits, such as resistors, capacitors, and amplifiers, experience zero-point drift and gain shifts due to aging and temperature drift, leading to distortion of key power frequency characteristic parameters such as leakage current and resistive components, thus affecting the accuracy of condition assessment. Traditionally, manual periodic calibration or fixed-cycle automatic compensation mechanisms are relied upon, but these suffer from response lag and an inability to dynamically perceive operational status. Therefore, there is an urgent need to construct a multi-level, interconnected, and self-evolving intelligent calibration system to achieve a shift from "passive maintenance" to "proactive health management."

[0038] The specific implementation of this application is as follows: A high-precision CT / PT is used as the front-end signal acquisition unit, connected to the signal conditioning circuit via a shielded cable. The sampling frequency is 10kHz, supporting fundamental and low-order harmonic analysis. Power frequency characteristic parameters include the RMS value of the total current, peak resistive current, phase angle, and third harmonic amplitude, which are synchronously sampled by an AD7606 analog-to-digital converter chip with 16-bit accuracy. Environmental parameters consist of a DS18B20 temperature sensor (temperature range -40℃ to +85℃), an SHT30 humidity sensor (accuracy ±1.5%RH), and an RFP-3G electromagnetic interference detection module. All environmental data is updated every 10 minutes and written to a local historical database. Historical self-calibration data from similar equipment is obtained from the main control system via the IEC 61850 protocol, containing a sequence of reference values ​​calibrated multiple times within the past 12 months. In practical applications, the signal acquisition module can also integrate GPS time synchronization functionality.

[0039] This method achieves comprehensive awareness of the operating status, providing the raw input foundation for subsequent multi-level calibration. High-precision ADCs and interference-resistant wiring ensure the reliability of the raw data. Multi-dimensional parameter joint modeling significantly improves the ability to reproduce real-world operating conditions and avoids misjudgment based on a single parameter.

[0040] The second objective of this application is to provide a power frequency characteristic self-calibration system for a surge arrester monitoring device, which implements the aforementioned power frequency characteristic self-calibration method for a surge arrester monitoring device. The system includes: The signal acquisition module is used to acquire the power frequency characteristic parameters and environmental parameters of the surge arrester monitoring device. An internal standard source module is used to provide power frequency standard signals of known accuracy; The verification and feedback module is used to verify the self-calibration results and ensure the validity of the calibration results; The self-calibration module is used to combine the power frequency characteristic parameters and environmental parameters of the surge arrester monitoring device with the self-calibration historical data of similar surge arrester monitoring devices, and perform adaptive self-calibration based on the adaptive self-calibration trigger mode to obtain the adaptive self-calibration reference value. The verification and feedback module is used to verify the adaptive self-calibration reference value, and based on the verification result, derive the verification deviation value and perform internal self-calibration based on the internal self-calibration trigger model; it also verifies the internal self-calibration reference value, and based on the verification result, derives the verification deviation value and performs external self-calibration based on the external self-calibration trigger model; and it performs data interaction and fusion between the adaptive self-calibration reference value, the internal self-calibration reference value, and the external self-calibration reference value to obtain the final calibration value. The storage module is used to store the power frequency characteristic parameters of the surge arrester monitoring device, the environmental parameters of the surge arrester monitoring device, the adaptive self-calibration data, the internal self-calibration data, the external self-calibration data, and the final calibration value.

[0041] The signal acquisition module consists of a high-precision ADC and digital filters, supporting 16-bit sampling and 50Hz notch filtering. The self-calibration module runs in an embedded Linux environment, with core algorithms developed in Python, supporting timed task scheduling and event-driven triggering. The verification and feedback module compares the current measured values ​​with various reference values, generates verification reports, and uploads them to the SCADA system. The storage module uses an SQLite database with cyclic log storage, supporting power-off recovery. The entire system utilizes an industrial-grade PCB layout, achieving EMC protection levels up to IEC 61000-4-30 standards. In practical applications, the system can be deployed in intelligent ring main units, traction substations, or new energy aggregation stations. This system achieves closed-loop control throughout the entire process from acquisition to calibration to data fusion. It supports local autonomous calibration and remote monitoring collaboration, making it suitable for the operation and maintenance needs of new power systems.

[0042] A third objective of this application is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the power frequency characteristic self-calibration method of the aforementioned surge arrester monitoring device. The device also includes a communication interface and a bus.

[0043] The above-mentioned power frequency characteristic self-calibration method for a surge arrester monitoring device includes: Step 1: Real-time acquisition of power frequency characteristic parameters and environmental parameters of the surge arrester monitoring device, and formation of a historical database: Step 2: Combining the power frequency characteristic parameters and environmental parameters of the surge arrester monitoring device with the self-calibration historical data of similar surge arrester monitoring devices, perform adaptive self-calibration based on the adaptive self-calibration trigger mode: Step 3: Verify the adaptive self-calibration benchmark value, and based on the verification results, derive the verification deviation value. Then, perform internal self-calibration based on the internal self-calibration trigger model. Step 4: Verify the internal self-calibration benchmark value, and based on the verification results, obtain the verification deviation value. Then, perform external self-calibration based on the external self-calibration trigger model. Step 5: Perform data interaction and fusion between the adaptive self-calibration reference, the internal self-calibration reference value, and the external self-calibration reference value to obtain the final calibration value. Step 6: Store the power frequency characteristic parameters of the surge arrester monitoring device, the environmental parameters of the surge arrester monitoring device, the adaptive self-calibration data, the internal self-calibration data, the external self-calibration data, and the final calibration value.

[0044] The fourth objective of this application is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the power frequency characteristic self-calibration method of the above-mentioned surge arrester monitoring device.

[0045] The above-mentioned power frequency characteristic self-calibration method for a surge arrester monitoring device includes: Step 1: Real-time acquisition of power frequency characteristic parameters and environmental parameters of the surge arrester monitoring device, and formation of a historical database: Step 2: Combining the power frequency characteristic parameters and environmental parameters of the surge arrester monitoring device with the self-calibration historical data of similar surge arrester monitoring devices, perform adaptive self-calibration based on the adaptive self-calibration trigger mode: Step 3: Verify the adaptive self-calibration benchmark value, and based on the verification results, derive the verification deviation value. Then, perform internal self-calibration based on the internal self-calibration trigger model. Step 4: Verify the internal self-calibration benchmark value, and based on the verification results, obtain the verification deviation value. Then, perform external self-calibration based on the external self-calibration trigger model. Step 5: Perform data interaction and fusion between the adaptive self-calibration reference, the internal self-calibration reference value, and the external self-calibration reference value to obtain the final calibration value. Step 6: Store the power frequency characteristic parameters of the surge arrester monitoring device, the environmental parameters of the surge arrester monitoring device, the adaptive self-calibration data, the internal self-calibration data, the external self-calibration data, and the final calibration value.

[0046] The fifth objective of this application is to provide a computer program product, which includes computer instructions that instruct a computer to execute the power frequency characteristic self-calibration method of the above-described surge arrester monitoring device.

[0047] The above-mentioned power frequency characteristic self-calibration method for a surge arrester monitoring device includes: Step 1: Real-time acquisition of power frequency characteristic parameters and environmental parameters of the surge arrester monitoring device, and formation of a historical database: Step 2: Combining the power frequency characteristic parameters and environmental parameters of the surge arrester monitoring device with the self-calibration historical data of similar surge arrester monitoring devices, perform adaptive self-calibration based on the adaptive self-calibration trigger mode: Step 3: Verify the adaptive self-calibration benchmark value, and based on the verification results, derive the verification deviation value. Then, perform internal self-calibration based on the internal self-calibration trigger model. Step 4: Verify the internal self-calibration benchmark value, and based on the verification results, obtain the verification deviation value. Then, perform external self-calibration based on the external self-calibration trigger model. Step 5: Perform data interaction and fusion between the adaptive self-calibration reference, the internal self-calibration reference value, and the external self-calibration reference value to obtain the final calibration value. Step 6: Store the power frequency characteristic parameters of the surge arrester monitoring device, the environmental parameters of the surge arrester monitoring device, the adaptive self-calibration data, the internal self-calibration data, the external self-calibration data, and the final calibration value.

[0048] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0049] These computer program instructions can also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0050] This application may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application may take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, readable storage media, optical storage, etc.) containing computer-usable program code.

[0051] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0052] Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0053] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and not to limit them. Although this application has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation methods of this application. Any modifications or equivalent substitutions that do not depart from the spirit and scope of this application should be covered within the protection scope of this application.

Claims

1. A method for self-calibrating the power frequency characteristics of a surge arrester monitoring device, characterized in that, include: Real-time acquisition of power frequency characteristic parameters and environmental parameters of surge arrester monitoring devices; Combining the power frequency characteristic parameters and environmental parameters of the surge arrester monitoring device with the self-calibration historical data of similar surge arrester monitoring devices, adaptive self-calibration is performed based on the adaptive self-calibration trigger mode to obtain the adaptive self-calibration reference value. The adaptive self-calibration benchmark value is verified, and the verification deviation value is obtained based on the verification result of the adaptive self-calibration benchmark value. Internal self-calibration is then performed based on the internal self-calibration trigger model. The internal self-calibration reference value is verified, and the internal self-calibration reference value is obtained based on the verification result. External self-calibration is performed based on the external self-calibration trigger model to obtain the external self-calibration reference value. The adaptive self-calibration reference, internal self-calibration reference value and external self-calibration reference value are interacted and fused to obtain the final calibration value; The power frequency characteristic parameters of the surge arrester monitoring device, the environmental parameters of the surge arrester monitoring device, the adaptive self-calibration data, the internal self-calibration data, the external self-calibration data, and the final calibration value are stored.

2. The power frequency characteristic self-calibration method for the surge arrester monitoring device according to claim 1, characterized in that, The specific details of the adaptive self-calibration trigger mode are as follows: Environmental parameters include temperature, humidity, and electromagnetic interference. Thresholds for temperature, humidity, and electromagnetic interference changes are set for these parameters. When the real-time collected environmental parameters exceed any of the set thresholds, adaptive self-calibration is triggered. The system monitors changes in power frequency characteristic parameters in real time and sets a deviation threshold for these parameters. It analyzes the deviation of the power frequency characteristic parameters every ten minutes. When the deviation exceeds the deviation threshold, it triggers adaptive self-calibration.

3. The power frequency characteristic self-calibration method of the surge arrester monitoring device according to claim 2, characterized in that, The specific details of the internal self-calibration triggering model are as follows: The internal self-calibration trigger model is divided into active internal self-calibration and passive internal self-calibration. Active internal self-calibration: When the surge arrester monitoring device is running at low load every day, the internal self-calibration is actively triggered to perform internal self-calibration; Passive internal self-calibration: The adaptive self-calibration benchmark value and the corresponding verification deviation value are quantified, and the adaptive self-calibration benchmark value is recorded as A, the verification deviation value is recorded as a, the passive trigger threshold for internal self-calibration is set as Y, and the values ​​of the three most recent A and a are continuously captured and recorded as A. n and a n A n-1 and a n-1 And A n-2 and a n-2 n represents the number of records; then, substituting this into the internal self-calibration passive trigger formula yields the internal self-calibration passive trigger judgment factor X. Then, the values ​​of X and Y are compared. If X ≥ Y, the internal self-calibration is passively triggered, thus performing passive internal self-calibration; otherwise, it is not triggered. The internal self-calibration passive trigger formula is: X = |A n -a n |×λ1+|A n-1 -a n-1 |×λ2+|A n-2 -a n-2 |×λ3, where λ1, λ2, and λ3 are weighting coefficients, with λ1 ranging from 0.3 to 0.5, λ2 ranging from 0.2 to 0.3, and λ3 ranging from 0.2 to 0.

3.

4. The power frequency characteristic self-calibration method of the surge arrester monitoring device according to claim 3, characterized in that, The specific details of the external self-calibration triggering model are as follows: The external self-calibration trigger model is divided into active external self-calibration and passive external self-calibration. Active external self-calibration: External self-calibration is performed by professionals every one to two years; Passive external self-calibration: Set the deviation threshold for internal self-calibration, collect the verification deviation value corresponding to the internal self-calibration benchmark value in real time to determine the deviation situation. When the deviation exceeds the deviation threshold three times in a row, the passive external self-calibration is passively triggered to perform external self-calibration.

5. The power frequency characteristic self-calibration method of the surge arrester monitoring device according to claim 4, characterized in that, The final calibration value = adaptive self-calibration reference value × α + internal self-calibration reference value × β + external self-calibration reference value × γ, where α + β + γ = 1, and α, β and γ are all weighting coefficients, and their values ​​are dynamically adjusted.

6. The power frequency characteristic self-calibration method of the surge arrester monitoring device according to claim 1, characterized in that, The specific steps of the internal self-calibration are as follows: A stable power frequency voltage or current signal is generated using a reference source, thereby producing a standard signal; A standard signal is injected into the measurement circuit by a switching circuit; Simultaneously acquire measurement data and standard signals from the surge arrester monitoring device, and calculate amplitude deviation and phase deviation; Internal self-calibration is performed based on the amplitude and phase deviations.

7. The power frequency characteristic self-calibration method of the surge arrester monitoring device according to claim 1, characterized in that, The specific steps of the external self-calibration are as follows: Connect the standard voltage source and standard current source to the measurement input terminal of the surge arrester monitoring device through a dedicated interface; The surge arrester monitoring device is put into calibration mode by on-site operation or remote command, temporarily interrupting the actual measurement and switching to calibration signal input. Perform external self-calibration; After calibration, input a set of standard signals with known parameters, repeat the measurement multiple times, and calculate the repeatability error and accuracy of the measured values.

8. The power frequency characteristic self-calibration method of the surge arrester monitoring device according to claim 1, characterized in that, The specific steps of the adaptive self-calibration are as follows: The system collects power frequency characteristic data of the surge arrester monitoring device under normal conditions, extracts characteristic parameters, and establishes a multiple linear regression model. By inputting the characteristic parameters into the multiple linear regression model, the system monitors the deviation between the current measurement data and the multiple linear regression model in real time. When the deviation exceeds the threshold, an adaptive self-calibration process is triggered.

9. The power frequency characteristic self-calibration method of the surge arrester monitoring device according to claim 8, characterized in that, Also includes: Provide internal standard sources; When the deviation of the calibration results of external self-calibration, internal self-calibration and adaptive self-calibration is >5%, the external self-calibration reference value shall be used first, and troubleshooting shall be performed. If the deviation between the internal standard source and the adaptive self-calibration is less than 5% and 3% is less than 3%, the result of the internal standard source shall prevail, and the model in the adaptive self-calibration trigger mode shall be retrained. After external calibration, the internal standard source and the adaptive self-calibration reference parameters are forcibly synchronized to ensure the consistency of the calibration chain.

10. A power frequency characteristic self-calibration system for a surge arrester monitoring device, comprising the power frequency characteristic self-calibration method for a surge arrester monitoring device as described in any one of claims 1 to 9, characterized in that, The system includes: The signal acquisition module is used to acquire the power frequency characteristic parameters and environmental parameters of the surge arrester monitoring device. The self-calibration module is used to combine the power frequency characteristic parameters and environmental parameters of the surge arrester monitoring device with the self-calibration historical data of similar surge arrester monitoring devices, and perform adaptive self-calibration based on the adaptive self-calibration trigger mode to obtain the adaptive self-calibration reference value. The verification and feedback module is used to verify the adaptive self-calibration reference value, and based on the verification result, derive the verification deviation value and perform internal self-calibration based on the internal self-calibration trigger model; it also verifies the internal self-calibration reference value, and based on the verification result, derives the verification deviation value and performs external self-calibration based on the external self-calibration trigger model; and it performs data interaction and fusion between the adaptive self-calibration reference value, the internal self-calibration reference value, and the external self-calibration reference value to obtain the final calibration value. The storage module is used to store the power frequency characteristic parameters of the surge arrester monitoring device, the environmental parameters of the surge arrester monitoring device, the adaptive self-calibration data, the internal self-calibration data, the external self-calibration data, and the final calibration value.

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