Remote transmission type power transmission line lightning arrester action frequency recording method

Abstract

The invention discloses a remote transmission type power transmission line lightning arrester action frequency recording method, which comprises the following steps: data acquisition: acquiring an impact current signal when a lightning arrester acts; data processing: transmitting the impact current signal to a microprocessor to obtain action characteristic information, and calculating the characteristic information according to a specific algorithm to count the number of actions; remote transmission preparation: sending the packaged data to a remote server and a monitoring center by using a wireless communication module; finally, the remote server adopts an edge computing method to analyze the transmission data and store the transmission data in a database; the purposes that operation and maintenance personnel remotely read the number of actions of the lightning arrester counter, field manual checking is not needed, regional limitation is broken through, the monitoring efficiency is improved, and convenient and real-time data support is provided for state evaluation and operation and maintenance decision making of the power transmission line lightning arrester are achieved.

Description

Technical Field

[0001] This invention belongs to the field of power transmission network technology, specifically relating to a method for recording the number of operations of a long-range surge arrester on a power transmission line. Background Technology

[0002] Transmission lines are often located in areas prone to lightning strikes, and both direct and induced lightning strikes can damage facilities in the area. Currently, the risk of lightning damage in the power grid is mainly concentrated on transmission lines. Lightning strikes can cause serious faults such as multiple lines tripping consecutively, simultaneous flashover of two circuits on the same tower, and two-phase flashover of transmission lines. Furthermore, with the increase in severe convective weather and the rapid development of the power grid, lightning faults occur frequently. Therefore, monitoring the performance status of surge arresters through live testing is particularly important. However, due to operational limitations, surge arresters are often difficult to test in a timely manner during power outages, and the intervals between periodic tests are long. If the nonlinear performance of a metal oxide surge arrester (AOM) deteriorates, the resistive current component under operating voltage will increase significantly, and the inflection point voltage will decrease; if the arrester is internally damp, the total current passing through it under operating voltage will also increase. According to the "Preventive Testing Procedures for Power Equipment (DL / T 596-1996)," measuring the resistive current and total current of the AOM under operating voltage is essential. Studies have found that the total leakage current of a zinc oxide surge arrester cannot fully reflect its insulation condition; the peak value of its resistive leakage current is the more important indicator of the insulation performance (for example, when the peak resistive current increases from 50μA to 250μA, the increase in the total current may only be a few percent).

[0003] Currently, international technologies for monitoring the total leakage current of AOM surge arresters include temperature-based measurement methods and the "TA" dual method. Domestically, the main surge arrester monitoring methods include the conventional compensation method, the fundamental wave method, the third harmonic method, and the total leakage current method. However, most current monitoring methods, both domestically and internationally, rely on on-site personnel for periodic inspections, making real-time monitoring and analysis impossible. The microprocessor in this invention is a 32-bit ARM Cortex-M4 core microprocessor. The CRC-16 algorithm is from Zhang Weifeng, *Communication Principles and Communication Technology* (M), Beijing: Electronic Industry Press, 2020. Summary of the Invention

[0004] The purpose of this invention is to solve the problem in the existing technology that most tests are conducted on-site by testing personnel on a regular basis, which cannot achieve real-time monitoring and analysis.

[0005] To solve the above problems, the present invention provides a method for recording the number of operations of a long-range transmission line surge arrester, comprising: a method for recording the number of operations of a long-range transmission line surge arrester, characterized in that it includes: S1: Data Acquisition; A Rogowski coil current sensor is connected in series with the grounding lead of the surge arrester on the transmission line to collect the impulse current signal when the surge arrester operates. The impulse current signal includes: the amplitude of the operating current, ranging from 100A to 200kA, the voltage change slope greater than or equal to 10kV / μs, and the current duration less than or equal to 10ms. This is used to determine the occurrence of the operation and to record the timestamp of each operation. S2: Data processing; The impact current signal is transmitted to a microprocessor, where it undergoes preprocessing including filtering, amplification, and baseline correction to remove noise interference and obtain action characteristic information. The number of actions is then calculated and statistically analyzed using a specific algorithm, which is stored in local memory. This specific algorithm includes: Using a shake-resistant counting method: Logic: Set the anti-shake time window, with a value range of 200ms±50ms, which can be adaptively adjusted. The time window is shortened to 100ms in areas with high lightning strike density and extended to 300ms in areas with low lightning strike density. Multiple triggers within the same window are considered as one valid action. Function: Count the number of valid actions by time period, including day, month, and year, obtain trend reports, and record the timestamp, peak current, voltage jump slope, current duration, and leakage current parameters for each valid action; Abnormal event marking: Combining historical data on the number of actions, the following formula is used: Characteristic value = Amplitude of operating current × Duration of current + Slope of voltage jump × Threshold of jump The mutation threshold is set to 10kV. By comparing the 3σ deviation between the characteristic value and the historical mean, abnormal actions are determined. Through statistical testing, abnormal high-frequency actions are identified, namely, arrester aging or system abnormality, triggering an early warning. σ is the standard deviation of historical data, and the calculation period is monthly. S3: Remote transmission preparation; The action count data and auxiliary information are encapsulated using the MQTT lightweight communication protocol or the DL / T 634.5104 power protocol, in the format of: device ID + timestamp + data type + check code. The check code is generated using the CRC-16 algorithm. S4: Data transmission; Using a wireless communication module that supports NB-IoT or 4G networks, when the signal strength is <-100dBm, it automatically switches to LoRa spread spectrum communication to send the encapsulated data to the remote server and monitoring center. S5: Receiving and displaying; The remote server uses edge computing to build a remote server at the lightning arrester counter terminal to receive data, parse it and store it in the database. Edge computing employs a sliding window + local threshold judgment strategy, marking abnormal data before uploading it to a remote server. Specific methods include: The sliding window divides time into consecutive 5-minute segments, each segment serving as an independent data processing unit. The local threshold is dynamically generated based on the average number of actions during the same time period over the past 7 days, calculated as follows: Local threshold = 7-day average × 1.2 Within each 5-minute window, the actual number of surge arresters is counted in real time. When the actual number of surge arresters exceeds the local threshold, it indicates that the surge arresters are operating abnormally frequently and there is a equipment fault, including: internal component short circuits causing malfunctions or abnormally strong lightning activity in the area. The number of surge arresters, time, and local threshold are marked as abnormal data and uploaded to the remote server through the communication module. When the actual number of surge arresters is less than or equal to the local threshold, it is considered a normal fluctuation, and the data is only stored locally and not uploaded to the remote server. The monitoring center reads data from the database and presents it to the maintenance personnel in an intuitive form, such as charts and reports.

[0006] In the preferred method, the auxiliary information includes: timestamp, latitude and longitude, line name, phase, and tower number.

[0007] In the preferred approach, the σ principle includes: determining the outcome by establishing a statistical model based on historical data; Based on the action data of the past year, the mean μ and standard deviation σ of each parameter were calculated using statistical methods. For the number of actions, when the mean number of actions within a consecutive week exceeds μ+2σ, it is considered to be abnormally frequent, indicating abnormally enhanced regional lightning activity or that the surge arrester itself is too sensitive to normal voltage fluctuations. If it is lower than μ-2σ, it indicates that the surge arrester is faulty and cannot respond to lightning strikes normally, including: internal component open circuit causing current to be unable to pass through the counting device. Regarding the amplitude of the operating current, if the amplitude of a certain operating current is greater than μ+3σ, it indicates that the surge arrester has encountered a powerful lightning strike far exceeding the design standard or that its current limiting capacity has decreased, and its performance status needs to be checked immediately; if the amplitude of a certain operating current is less than μ-3σ, it indicates that the surge arrester has poor conduction performance and cannot effectively discharge lightning current.

[0008] In the preferred embodiment, the data types in step S3 include: Numerical data includes: number of actions, current amplitude, and voltage value; Time-based data includes the precise time when each action occurred; Status data includes: whether the surge arrester is currently in normal operation, abnormal alarm, or fault state; text data includes: equipment model and manufacturer information. Statistical analysis data includes: the monthly average number of actions and the standard deviation of current amplitude.

[0009] The beneficial effects of this invention are as follows: By deploying sensors at the surge arresters of transmission lines to collect action signals, which are then processed by a microprocessor and transmitted remotely via a wireless communication module, maintenance personnel can remotely read the number of times the surge arrester counter has been activated without the need for on-site manual inspection. This breaks through geographical limitations, improves monitoring efficiency, and provides convenient and real-time data support for the status assessment and maintenance decisions of surge arresters of transmission lines. Attached Figure Description

[0010] Figure 1 This is a schematic diagram of the working principle of a remote transmission line surge arrester operation count recording device. Detailed Implementation

[0011] Example 1: The purpose of this invention is to develop a remote-type surge arrester operation count recording device, such as... Figure 1 As shown, it is connected to the surge arresters in each designated lightning strike area. The backend server, connected to the remote transmission device via a mobile network, is used to receive real-time data such as the tower number and number of lightning strikes corresponding to the surge arresters in each lightning strike area. The server is equipped with a prediction model. By automatically reading the collected parameters corresponding to the surge arresters in each lightning strike area into the prediction model, the model calculates and outputs the lightning strike current. The tower condition is evaluated based on the obtained lightning strike current. Specific methods include: S1: Data Acquisition; A Rogowski coil current sensor is connected in series with the grounding lead of the surge arrester on the transmission line to collect the impulse current signal when the surge arrester operates. The impulse current signal includes: the amplitude of the operating current, ranging from 100A to 200kA, the voltage change slope greater than or equal to 10kV / μs, and the current duration less than or equal to 10ms. This is used to determine the occurrence of the operation and to record the timestamp of each operation. Use voltage transformers to collect voltage fluctuation data; S2: Data processing; The impact current signal is transmitted to a microprocessor, where it undergoes preprocessing including filtering, amplification, and baseline correction to remove noise interference and obtain action characteristic information. The number of actions is then calculated and statistically analyzed using a specific algorithm, which is stored in local memory. This specific algorithm includes: Using a shake-resistant counting method: Logic: Set the anti-shake time window, with a value range of 200ms±50ms, which can be adaptively adjusted. The time window is shortened to 100ms in areas with high lightning strike density and extended to 300ms in areas with low lightning strike density. Multiple triggers within the same window are considered as one valid action; to avoid duplicate counting due to signal oscillation. Functions: Counts the number of valid actions by time period (day, month, year) and generates trend reports; records the timestamp, peak current, voltage surge slope, current duration, and leakage current parameters for each action; used for fault tracing. Abnormal event marking: Combining historical data on the number of actions, the following formula is used: Characteristic value = Amplitude of operating current × Duration of current + Slope of voltage jump × Threshold of jump The mutation threshold is set to 10kV. By comparing the 3σ deviation between the characteristic value and the historical mean, abnormal actions are determined. Through statistical testing, abnormal high-frequency actions are identified, namely, arrester aging or system abnormality, triggering an early warning. σ is the standard deviation of historical data, and the calculation period is monthly. S3: Remote transmission preparation; The action count data and auxiliary information are encapsulated using the MQTT lightweight communication protocol or the DL / T 634.5104 power protocol, in the format of: device ID + timestamp + data type + checksum. The checksum is generated using the CRC-16 algorithm to prepare for long-distance transmission. S4: Data transmission; The system utilizes a wireless communication module that supports NB-IoT or 4G networks. When the signal strength is less than -100dBm, it automatically switches to LoRa spread spectrum communication to send the encapsulated data to the remote server and monitoring center. During communication, the system automatically adjusts the transmission power and rate based on the signal strength and network conditions to ensure stable data transmission. S5: Receiving and displaying; The remote server employs edge computing, setting up a remote server at the surge arrester counter terminal to receive, parse, and store data in a database. (A surge arrester counter is a high-voltage electrical device that monitors the discharge action of a surge arrester. It consists of a nonlinear resistor, an electromagnetic counter, etc. It records the number of discharges, monitors the operating status, and activates the counter through current. Surge arrester counters are a commonly used device in power systems.) Edge computing employs a sliding window + local threshold judgment strategy, marking abnormal data before uploading it to a remote server. Specific methods include: The sliding window divides the time into continuous 5-minute segments, with each segment serving as an independent data processing unit. This allows for timely capture of changes in the surge arrester's actions within a short period and comprehensive analysis of the action trends over a certain time span. The local threshold is dynamically generated based on the average number of actions during the same time period over the past 7 days. The calculation method is: Local threshold = Average of the past 7 days × 1.2. For example, if the average number of surge arrester activations in a certain area between 10:00 AM and 10:05 AM over the past 7 days is 5, then the local threshold for that time period on that day is 5 × 1.2 = 6. Within each 5-minute window, the actual number of surge arrester activations is counted in real time. If the actual number of activations exceeds the local threshold (e.g., 8 activations exceeding the threshold by 6), it indicates abnormally frequent surge arrester activations and potential equipment malfunctions, including internal component short circuits causing malfunctions or unusually strong lightning activity in the area. The activation count, time, and local threshold are marked as abnormal data and uploaded to a remote server via the communication module for further analysis and processing by maintenance personnel. When the actual number of activations is less than or equal to the local threshold (e.g., 5 activations less than or equal to the threshold by 6), it is considered normal fluctuation, and the data is stored locally without being uploaded to the remote server. This reduces data transmission volume, lowers server load, and improves system efficiency. The monitoring center reads data from the database and presents it to maintenance personnel in an intuitive format such as charts and reports, allowing them to view the number of times the surge arresters on the transmission lines have operated and their historical records at any time, providing a basis for equipment maintenance and fault analysis.

[0012] The auxiliary information includes: timestamp, latitude and longitude, line name, phase, and tower number.

[0013] The σ principle includes: determining this by establishing a statistical model based on historical data; Based on the action data of the past year, the mean μ and standard deviation σ of each parameter were calculated using statistical methods. For the number of actions, when the mean number of actions within a consecutive week exceeds μ+2σ, it is considered to be abnormally frequent, indicating abnormally enhanced regional lightning activity or that the surge arrester itself is too sensitive to normal voltage fluctuations. If it is lower than μ-2σ, it indicates that the surge arrester is faulty and cannot respond to lightning strikes normally, including: an open circuit in internal components causing current to be unable to pass through the counting device. Regarding the amplitude of the operating current, if the amplitude of a certain operating current is greater than μ+3σ, it indicates that the surge arrester has encountered a powerful lightning strike far exceeding the design standard or that its current limiting capacity has decreased, and its performance status needs to be checked immediately; if the amplitude of a certain operating current is less than μ-3σ, it indicates that the surge arrester has poor conduction performance and cannot effectively discharge lightning current.

[0014] The data types in step S3 include numerical data, such as the number of actions, current amplitude, and voltage value, which visually present the operating status of the surge arrester in specific numerical form; time-based data, such as the precise time of each action, used to analyze the temporal distribution pattern of lightning strike events; status-based data, such as whether the surge arrester is currently in normal operation, abnormal alarm, or fault state, which helps maintenance personnel quickly understand the overall status of the equipment; text-based data, such as descriptive content like equipment model and manufacturer information; and statistical analysis data, which are the results obtained after statistical calculation of various types of data over a period of time, such as the monthly average of the number of actions and the standard deviation of the current amplitude, used to assess the long-term stability and trend of the equipment.

[0015] Example 2: The prediction model is expressed through a multi-parameter regression model. The M-order polynomial function of the input x is constructed using a polynomial fitting method, so that the established polynomial function can approximately represent the relationship between the input x and the output y, that is, the relationship between the input parameters and the output lightning current on the tower state.

[0016] The relationship between input x and output y is constructed using a multivariate fitting method, that is, a multivariate regression model is established to achieve the prediction function. The prediction model here is expressed by a multivariate parametric regression equation as follows: Where M represents the number of parameters in the prediction model (including M+1 parameters), and represents the highest order; wi is the fitting parameter; b is a constant, and the input to the fitting problem can be expressed as: in, The target output is a polynomial function: By performing regression on the data input into D, the fitting parameters wi and constant b are obtained. This invention transforms the polynomial fitting problem into an optimization problem by using a loss / error function for evaluation, thereby determining the best fitting parameters and achieving accurate evaluation of the prediction model. The server in this invention also includes an error evaluation module, which evaluates the accuracy of the prediction model to obtain the best fitting parameters wi and constant b. For the polynomial fitting problem, although the prediction model is a linear function containing (M + 1) fitting parameters, the input x is not linear. When the server, through the set lightning damage area analysis module, finds that the output current value of the prediction model is greater than a set threshold, it combines the current data calculated by the prediction model to promptly pay special attention to the operating status of the surge arresters in each area. By establishing a lightning strike frequency calculation module in the server, combined with the divided lightning strike areas, the lightning strike frequency of each tower in each area is calculated, and areas with high lightning strike frequencies are promptly identified.

[0017] This invention calculates the frequency of lightning strikes based on collected server data, combines the output value of the prediction model with the frequency of lightning strikes, analyzes high lightning damage areas, and provides auxiliary decision-making suggestions for tower grounding modification and transmission line inspection.

[0018] The mobile network provided by this invention includes a hybrid networking module, a low-power wide-area network (LoRa) module, and a narrowband Internet of Things (NB-IoT) module. Considering the complex environment of the transmission lines where surge arresters are located, the long transmission distances and large bandwidths, and the fact that some lines may traverse sparsely populated areas, many monitoring devices cannot reliably and promptly transmit data, resulting in unbalanced communication network signals and greatly increasing the difficulty and intensity of inspections.

[0019] Based on the linear topology of the detection nodes installed on the tower, this invention constructs a multi-layered heterogeneous self-optimizing wireless network algorithm, forming a wireless communication network. This network can automatically select a multi-hop wireless network or a public network based on the signal strength at the monitoring end, transmitting the collected data to the remote end. The NB-IoT module in the hybrid networking module can directly access the mobile communication network in areas with good network signals where the surge arrester is located, communicating with existing public network base stations and interacting with the server.

[0020] When the surge arrester is in an area with poor network signal, it first interacts and stores data with a remote transmission device equipped with a LoRa module through the LoRa module in the hybrid networking module, and then collects data through the NB-IoT module and sends the data to the next-level server equipped with the NB-IoT module, thus realizing data transmission from an area with poor network signal to an area with good network signal.

[0021] This invention establishes a server to collect data such as the number of lightning strikes at the front end of the surge arrester, and then calculates the output current and lightning strike frequency to accurately assess the operating status of the surge arrester corresponding to each tower, thereby reducing the disasters caused by lightning strikes.

[0022] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the present invention. Various changes and modifications can be made to the present invention without departing from its spirit and scope. All such changes and modifications fall within the scope of the present invention as claimed, which is defined by the appended claims and their equivalents.

Claims

1. A method for recording the number of operations of a long-range surge arrester on a transmission line, characterized in that, include: S1: Data Acquisition; A Rogowski coil current sensor is connected in series with the grounding lead of the surge arrester on the transmission line to collect the impulse current signal when the surge arrester operates. The impulse current signal includes: the amplitude of the operating current, ranging from 100A to 200kA, the voltage change slope greater than or equal to 10kV / μs, and the current duration less than or equal to 10ms. This is used to determine the occurrence of the operation and to record the timestamp of each operation. S2: Data processing; The impact current signal is transmitted to a microprocessor, where it undergoes preprocessing including filtering, amplification, and baseline correction to remove noise interference and obtain action characteristic information. The number of actions is then calculated and statistically analyzed using a specific algorithm, which is stored in local memory. This specific algorithm includes: Using a shake-resistant counting method: Logic: Set the anti-shake time window, with a value range of 200ms±50ms, which can be adaptively adjusted. The time window is shortened to 100ms in areas with high lightning strike density and extended to 300ms in areas with low lightning strike density. Multiple triggers within the same window are considered as one valid action. Function: Count the number of valid actions by time period, including day, month, and year, obtain trend reports, and record the timestamp, peak current, voltage jump slope, current duration, and leakage current parameters for each valid action; Abnormal event marking: Combining historical data on the number of actions, the following formula is used: Characteristic value = Amplitude of operating current × Duration of current + Slope of voltage jump × Threshold of jump The mutation threshold is set to 10kV. By comparing the 3σ deviation between the characteristic value and the historical mean, abnormal actions are determined. Through statistical testing, abnormal high-frequency actions are identified, namely, arrester aging or system abnormality, triggering an early warning. σ is the standard deviation of historical data, and the calculation period is monthly. S3: Remote transmission preparation; The action count data and auxiliary information are encapsulated using the MQTT lightweight communication protocol or the DL / T 634.5104 power protocol, in the format of: device ID + timestamp + data type + check code. The check code is generated using the CRC-16 algorithm. S4: Data transmission; Using a wireless communication module that supports NB-IoT or 4G networks, when the signal strength is <-100dBm, it automatically switches to LoRa spread spectrum communication to send the encapsulated data to the remote server and monitoring center. S5: Receiving and displaying; The remote server uses edge computing to build a remote server at the lightning arrester counter terminal to receive data, parse it and store it in the database. Edge computing employs a sliding window + local threshold judgment strategy, marking abnormal data before uploading it to a remote server. Specific methods include: The sliding window divides time into consecutive 5-minute segments, each segment serving as an independent data processing unit. The local threshold is dynamically generated based on the average number of actions during the same time period over the past 7 days, calculated as follows: Local threshold = 7-day average × 1.2 Within each 5-minute window, the actual number of surge arresters is counted in real time. When the actual number of surge arresters exceeds the local threshold, it indicates that the surge arresters are operating abnormally frequently and there is a equipment fault, including: internal component short circuits causing malfunctions or abnormally strong lightning activity in the area. The number of surge arresters, time, and local threshold are marked as abnormal data and uploaded to the remote server through the communication module. When the actual number of surge arresters is less than or equal to the local threshold, it is considered a normal fluctuation, and the data is only stored locally and not uploaded to the remote server. The monitoring center reads data from the database and presents it to the maintenance personnel in an intuitive form, such as charts and reports.

2. The method for recording the number of operations of a remote transmission line surge arrester according to claim 1, characterized in that, The auxiliary information includes: timestamp, latitude and longitude, line name, phase, and tower number.

3. The method for recording the number of operations of a remote transmission line surge arrester according to claim 1, characterized in that, The σ principle includes: determining this by establishing a statistical model based on historical data; Based on the action data of the past year, the mean μ and standard deviation σ of each parameter were calculated using statistical methods. For the number of actions, when the mean number of actions within a consecutive week exceeds μ+2σ, it is considered to be abnormally frequent, indicating abnormally enhanced regional lightning activity or that the surge arrester itself is too sensitive to normal voltage fluctuations. If it is lower than μ-2σ, it indicates that the surge arrester is faulty and cannot respond to lightning strikes normally, including: internal component open circuit causing current to be unable to pass through the counting device. Regarding the amplitude of the operating current, if the amplitude of a certain operating current is greater than μ+3σ, it indicates that the surge arrester has encountered a powerful lightning strike far exceeding the design standard or that its current limiting capacity has decreased, and its performance status needs to be checked immediately; if the amplitude of a certain operating current is less than μ-3σ, it indicates that the surge arrester has poor conductivity and cannot discharge lightning current.

4. The method for recording the number of operations of a remote transmission line surge arrester according to claim 1, characterized in that, The data types in step S3 include: Numerical data includes: number of actions, current amplitude, and voltage value; Time-based data includes the precise time when each action occurred; Status data includes: whether the surge arrester is currently in normal operation, abnormal alarm, or fault state; text data includes: equipment model and manufacturer information. Statistical analysis data includes: the monthly average number of actions and the standard deviation of current amplitude.

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