Overhead transmission line abnormal state identification method based on non-intrusive electrical parameter reconstruction

Abstract

The invention relates to the technical field of transmission line abnormal state detection, and discloses an overhead transmission line abnormal state identification method based on non-intrusive electrical parameter reconstruction. According to the method, key electrical parameters are determined, electromagnetic sensor arrangement and an array structure are optimized, finite element simulation is combined, efficient operation of the sensors at the optimal position is ensured, data acquisition precision is improved, support is provided for timely line state recognition, and in the data processing stage, advanced filtering and feature extraction technologies are utilized, so that the accuracy of data processing is improved. The method effectively distinguishes normal and abnormal states, constructs a correlation model of electrical parameters and environmental factors, can timely monitor and quantify the severity of abnormal conditions through a developed abnormal state recognition algorithm, further provides a scientific basis for operation and maintenance personnel, achieves the comprehensive monitoring of the overhead transmission line, and improves the safety of the overhead transmission line. Potential faults are recognized as soon as possible under extreme weather conditions, and the risk of electric power accidents is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of abnormal state detection of power transmission lines, and in particular to an abnormal state identification method of overhead power transmission lines based on non-invasive electrical parameter reconstruction. Background Art

[0002] In the daily operation of overhead transmission lines, their states are diverse, including but not limited to sag changes and vibrations caused by breezes. When encountering extreme weather such as strong winds, freezing or thunderstorms, the lines are more susceptible to abnormal conditions such as icing and wind deviation. From the perspective of electrical performance, possible faults include phase-to-phase short circuits, single-phase or multi-phase grounding, and discharge events. The deterioration of these abnormal states poses a risk of causing power accidents and threatening system safety, especially when such events trigger false operation of relay protection devices, which may cause line chain tripping and, in severe cases, cause large-scale power outages. Early and accurate identification of the above abnormalities can create conditions for timely intervention and prevent problems from escalating. Traditionally, the maintenance of power facilities relies on manual inspections and regular preventive maintenance and testing. This method is not only inefficient and delayed, but also unable to achieve real-time and comprehensive operating status monitoring, thus posing a challenge to the operational reliability of overhead transmission lines. Summary of the invention

[0003] 1. Technical issues to be solved

[0004] In view of the shortcomings of the prior art, the present invention provides an abnormal state identification method for overhead transmission lines based on non-invasive electrical parameter reconstruction, which has the characteristics of clarifying key electrical parameters through demand analysis, ensuring the pertinence and accuracy of monitoring, optimizing the layout and array structure of electromagnetic sensors, and combining finite element simulation to ensure the efficient operation of sensors in the optimal position, thereby improving the accuracy of data acquisition, and developing a high-speed data acquisition system to ensure real-time transmission and processing of data, providing support for timely identification of line status, and in the data processing stage, using advanced filtering and feature extraction technology to effectively distinguish normal and abnormal states, and constructing a correlation model between electrical parameters and environmental factors, and through the developed abnormal state identification algorithm, it is possible to timely monitor and quantify the severity of abnormal situations, thereby providing a scientific basis for operation and maintenance personnel, and finally the application of this comprehensive monitoring system can not only realize real-time and comprehensive monitoring of overhead transmission lines, but also identify potential faults early under extreme weather conditions, reduce the risk of power accidents, and improve the safety and reliability of the system, thus solving the above problems.

[0005] (II) Technical solution

[0006] To achieve the above object, the present invention provides the following technical solution: a method for identifying abnormal state of overhead transmission lines based on non-intrusive electrical parameter reconstruction, comprising the following steps:

[0007] S1. Conduct demand analysis, clarify the electrical parameters to be monitored, calculate the maximum current, voltage rating, and voltage and current fluctuation range, and select non-intrusive measurement solutions and electromagnetic sensors;

[0008] S2, optimize the layout and array structure of electromagnetic sensors, calculate the electric and magnetic field intensity distribution of sensors, and determine the optimal position through finite element simulation;

[0009] S3. Develop a high-speed data acquisition system to calculate data sampling rate, data transmission delay, and signal-to-noise ratio to ensure real-time acquisition and transmission of sensor data;

[0010] S4. In the data processing stage, filtering, denoising and feature extraction algorithms are used to identify normal and abnormal states, and to establish a correlation model between electrical parameters and environmental factors;

[0011] S5. Develop abnormal state recognition algorithms to monitor and quantify the severity of abnormal states;

[0012] S6. Integrate all modules into a complete monitoring system and conduct pilot tests and verification on actual overhead transmission lines.

[0013] Preferably, the formula for calculating the maximum current of S1 is as follows:

[0014]

[0015] In the formula, Dlzd represents the maximum current, Dxyl represents the effective value of the current, and Fzyx represents the maximum instantaneous value of the AC signal.

[0016] Preferably, the formula for calculating the voltage rating of S1 is as follows:

[0017]

[0018] In the formula, Dyed represents the rated voltage, and Dyyx represents the effective value of the voltage. It is used in literature and equipment specifications, and the unit is V. For DC voltage, the existing nominal value is the rated value and no further calculation is required.

[0019] Preferably, the formula for calculating the voltage and current fluctuation range of S1 is as follows:

[0020] Byfw=V max -V min

[0021] Blfw=I max -I min

[0022] In the formula, Byfw represents the voltage fluctuation range, Blfw represents the current fluctuation range, V maxIndicates the maximum voltage measured during the monitoring period, V min Indicates the minimum voltage measured during the monitoring period, I max Indicates the maximum current measured during the monitoring period, I min Indicates the minimum current measured during the monitoring period.

[0023] Preferably, the formula for calculating the electric field distribution intensity of the sensor by S2 is as follows:

[0024]

[0025] In the formula, Etdc represents the electric field distribution intensity of the sensor, and k represents the Coulomb constant, which is 8.99*10 9 Nm 2 / C 2 , Qt represents the charge of the point charge, and r represents the distance from the charge to the measurement point.

[0026] Preferably, the formula for calculating the magnetic field distribution strength of the sensor in S2 is as follows:

[0027]

[0028] In the formula, Ccqd represents the magnetic field distribution intensity of the sensor, ut0 represents the vacuum magnetic permeability, Zddl represents the current in the current-carrying wire, p represents the distance from the wire to the measuring point, and π represents 3.1415.

[0029] Preferably, the formula for calculating the data sampling rate in S3 is as follows:

[0030]

[0031] In the formula, Sc represents the data sampling rate, Nctb represents the number of samples collected within the time Tp, and Tp represents the time interval.

[0032] Preferably, the formula for calculating the data transmission delay in S3 is as follows:

[0033]

[0034] In the formula, Sjyc represents the data transmission delay, Sjzl represents the amount of data to be transmitted, which is determined by the output format of the data acquisition system, Scsl is the data transmission rate, which represents the rate at which data is transmitted through the serial interface, and Clyc represents the data processing delay.

[0035] Preferably, the formula for calculating the signal-to-noise ratio in S3 is as follows:

[0036]

[0037] In the formula, Xzbz represents the signal-to-noise ratio, Vsgl represents the signal voltage obtained by direct measurement, and Vnos represents the interference voltage in the device or environment.

[0038] Preferably, the S4 establishes a correlation model between electrical parameters and environmental factors as follows:

[0039] Mxgs=k0+k1*Q1+k2*Q2+k3*Q3+...+k n *Q n +∈

[0040] In the formula, Mxgs is the target variable, which represents the electrical parameters to be predicted, Q1, Q2, Q3 and Q n represents environmental factors and other factors that affect electrical parameters, including current, voltage, sag, and windage. k0 represents the intercept term, which means the predicted value of the target variable when all independent variables are 0. k1, k2, k3, and k n They respectively represent the degree of influence of each variable on the target variable. The size and sign of the coefficient indicate the direction and intensity of the influence of the independent variable on the dependent variable. ∈ represents the error term, which indicates the part that the model cannot explain.

[0041] Compared with the prior art, the present invention provides a method for identifying abnormal states of overhead transmission lines based on non-intrusive electrical parameter reconstruction, which has the following beneficial effects:

[0042] The present invention clarifies key electrical parameters through demand analysis to ensure the pertinence and accuracy of monitoring. The optimized electromagnetic sensor layout and array structure, combined with finite element simulation, ensure the efficient operation of the sensor in the optimal position, thereby improving the accuracy of data acquisition. The development of a high-speed data acquisition system ensures real-time transmission and processing of data, providing support for timely identification of line status. In the data processing stage, advanced filtering and feature extraction techniques are used to effectively distinguish between normal and abnormal states, and a correlation model between electrical parameters and environmental factors is constructed. Through the developed abnormal state identification algorithm, the severity of abnormal conditions can be monitored and quantified in a timely manner, thereby providing a scientific basis for operation and maintenance personnel. Ultimately, the application of this comprehensive monitoring system can not only realize real-time and comprehensive monitoring of overhead transmission lines, but also identify potential faults early under extreme weather conditions, reduce the risk of power accidents, and improve the safety and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic diagram of the steps of the method of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] Traditional power facility maintenance relies on manual inspections and regular preventive maintenance and testing. This method is not only inefficient and slow to respond, but also cannot achieve real-time and comprehensive operating status monitoring. Therefore, a method for identifying abnormal states of overhead transmission lines based on non-invasive electrical parameter reconstruction is proposed. Figure 1 , the method comprises the following steps:

[0046] S1. Conduct demand analysis, clarify the electrical parameters to be monitored, calculate the maximum current, voltage rating, and voltage and current fluctuation range, and select non-intrusive measurement solutions and electromagnetic sensors;

[0047] Conducting demand analysis is the first step to ensure that the overhead transmission line monitoring system is effective and reliable. At this stage, the electrical parameters to be monitored must be clearly defined, including AC current, AC voltage, power factor, frequency, current, voltage, sag, and windage, to ensure that all important indicators that may affect the system's operating status are covered;

[0048] After the monitoring parameters are determined, the next step is to calculate the electrical performance. First, the maximum current is calculated to determine the carrying capacity of the transmission line under different load conditions. This calculation will help identify the behavior of the equipment under normal operation and extreme conditions, and ensure that the monitoring system can accurately capture the characteristics of current fluctuations within the expected range. In addition, the rated voltage needs to be calculated. This indicator usually depends on the design standard of the transmission line and is used to analyze the voltage performance under various operating conditions.

[0049] After that, the fluctuation range of voltage and current needs to be evaluated, including transient fluctuations in a short period of time and trend changes in long-term operation. These fluctuations may be caused by load changes, equipment failures, or external environment (such as weather changes). By analyzing the fluctuation range, the threshold of the monitoring system can be better set, which helps to identify abnormal conditions in a timely manner.

[0050] In terms of selecting measurement schemes and sensors, non-intrusive measurement methods are the recommended choice because they can avoid direct interference and potential damage to the transmission line. Non-intrusive electrical measurement technologies such as current transformers and voltage transformers can be effectively monitored without cutting off the power supply. In addition, electromagnetic sensors suitable for specific applications should be selected, considering their measurement range, accuracy, response time and anti-interference ability to ensure that they can work reliably under complex environmental conditions (such as strong electromagnetic interference or severe vibration);

[0051] The calculation formula for the maximum current is as follows:

[0052]

[0053] By understanding the maximum current, the carrying capacity of the transmission line under different load conditions can be evaluated to avoid overload risks. In the formula, Dlzd represents the maximum current, Dxyl represents the effective value of the current, and Fzyx represents the maximum instantaneous value of the AC signal. The calculation of the maximum current value helps identify abnormal load conditions and promptly discover the risk of potential faults (such as short circuits or equipment damage). At the same time, it provides data basis for the design of equipment and systems to ensure their normal operation within the maximum load range, thereby extending the service life of the equipment.

[0054] The voltage rating is calculated as follows:

[0055]

[0056] Clearly define the rated voltage to provide a reference benchmark for monitoring and help evaluate whether it meets the standards in actual operation. In the formula, Dyed represents the rated voltage, and Dyyx represents the effective value of the voltage. It is used in literature and equipment specifications and the unit is V. For DC voltage, the existing nominal value is the rated value and no further calculation is required. Voltage monitoring can help identify the stability of the system under normal and extreme conditions and ensure the reliability of power supply.

[0057] The formula for calculating the voltage and current fluctuation range is as follows:

[0058] Byfw=V max -V min

[0059] Blfw=I max -I min

[0060] Identifying the fluctuation range of voltage and current can help monitor potential abnormal conditions, such as equipment failure or external interference. In the formula, Byfw represents the voltage fluctuation range, Blfw represents the current fluctuation range, and V max Indicates the maximum voltage measured during the monitoring period, V minIndicates the minimum voltage measured during the monitoring period, I max Indicates the maximum current measured during the monitoring period, I min Indicates the minimum current measured during the monitoring period. Understanding the fluctuation range can help system design take extreme conditions into account, thereby improving the resilience and reliability of the entire power system. At the same time, the correlation between changes in the fluctuation range and specific fault modes helps to accurately identify the fault type;

[0061] S2, optimize the layout and array structure of electromagnetic sensors, calculate the electric and magnetic field intensity distribution of sensors, and determine the optimal position through finite element simulation;

[0062] In the process of optimizing the arrangement and array structure of electromagnetic sensors, the performance is evaluated by calculating the distribution of electric and magnetic field strengths around the sensors. This process is usually carried out through finite element simulation methods, using numerical simulation technology to accurately predict the changing characteristics of the electromagnetic field. By establishing a physical model of the sensor and its working environment, the electric and magnetic field distributions under different positions and arrangements are simulated, and the optimal sensor position can be intuitively identified. During the optimization process, various factors will be considered, such as the mutual coupling between sensors, the influence of the external environment, and the characteristics of the monitored object. Based on the simulation results, the signal-to-noise ratio, sensitivity, and resolution under each array configuration can be analyzed, thereby effectively reducing interference and improving the measurement accuracy and reliability of the sensor. Finally, through reasonable sensor arrangement and array optimization, comprehensive monitoring of the electromagnetic field can be achieved, ensuring the best working performance in practical applications and meeting the needs of overhead transmission line monitoring systems.

[0063] Among them, the formula for the electric field distribution intensity of the sensor is as follows:

[0064]

[0065] By analyzing the electric field intensity distribution, the sensor layout can be reasonably planned to obtain the best measurement effect. In the formula, Etdc represents the electric field distribution intensity of the sensor, and k represents the Coulomb constant, which is 8.99*10 9 Nm 2 / C 2 , Qt represents the charge of the point charge, r represents the distance from the charge to the measurement point. Under changing operating conditions, the calculation of the electric field strength can provide a basis for the real-time monitoring system to quickly adjust parameters to improve the monitoring effect;

[0066] The calculation formula of the magnetic field distribution strength of the sensor is as follows:

[0067]

[0068] By analyzing the magnetic field intensity distribution, we can identify external interference sources and take timely measures to improve the system's anti-interference ability. In the formula, Ccqd represents the magnetic field distribution intensity of the sensor, ut0 represents the vacuum magnetic permeability, Zddl represents the current in the current-carrying wire, p represents the distance from the wire to the measuring point, and π represents 3.1415. By understanding the magnetic field distribution, we can optimize the selection and layout of the sensor to ensure that it is in the best working environment. Accurate magnetic field intensity data is crucial to monitoring state changes and helps to achieve accurate and immediate feedback.

[0069] S3. Develop a high-speed data acquisition system to calculate data sampling rate, data transmission delay, and signal-to-noise ratio to ensure real-time acquisition and transmission of sensor data;

[0070] By analyzing the dynamic characteristics of the monitored object and the acquisition target, a reasonable sampling rate is determined to ensure the effective capture of transient changes. When selecting the sampling rate, the Nyquist theorem needs to be followed to avoid aliasing and ensure that the system can accurately reflect the true form of the signal. In addition, the use of high-performance analog-to-digital converters and high-speed processing units can significantly improve the acquisition effect and achieve accurate capture and processing of high-frequency signals.

[0071] By optimizing the data transmission link, such as using high-speed serial communication protocol Ethernet and effective data compression algorithms, latency can be significantly reduced and data transmission efficiency can be improved. Using a real-time operating system (RTOS) can also enhance data processing efficiency, reduce latency, and ensure that data flows quickly from sensors to monitoring centers.

[0072] In order to improve signal quality and ensure data reliability, it is necessary to calculate the signal-to-noise ratio and consider technical means to reduce noise interference in the design, such as shielding, filtering, and considering the layout and environmental factors of the sensor. Adding appropriate gain settings and signal preprocessing links can improve signal quality and further optimize the overall performance of the system.

[0073] The formula for calculating the data sampling rate is as follows:

[0074]

[0075] Reasonable data sampling rate ensures that the monitoring system can capture transient events in real time and improve the response speed of fault detection. In the formula, Sc represents the data sampling rate, Nctb represents the number of samples collected within the time Tp, and Tp represents the time interval. By setting the sampling rate appropriately, the system load and data processing capacity can be balanced to reduce resource waste.

[0076] The formula for calculating data transmission delay is as follows:

[0077]

[0078] Accurate transmission delay calculation can optimize data flow and ensure real-time monitoring and rapid response capabilities. In the formula, Sjyc represents the data transmission delay, Sjzl represents the amount of data to be transmitted, which is determined by the output format of the data acquisition system, Scsl is the data transmission rate, which represents the rate at which data is transmitted through the serial interface, and Clyc represents the data processing delay. By calculating the data transmission delay, potential bottlenecks can be identified, and then the system architecture and data processing process can be optimized to improve the overall system efficiency.

[0079] The formula for calculating the signal-to-noise ratio is as follows:

[0080]

[0081] A high signal-to-noise ratio means a large gap between effective information and interference, which helps to evaluate and improve the quality of monitoring data. In the formula, Xzbz represents the signal-to-noise ratio, Vsgl represents the signal voltage directly measured, and Vnos represents the interference voltage in the device or environment. During the system design phase, the most suitable sensor and signal processing technology can be selected through signal-to-noise ratio analysis to improve the overall performance of the system.

[0082] S4. In the data processing stage, filtering, denoising and feature extraction algorithms are used to identify normal and abnormal states, and to establish a correlation model between electrical parameters and environmental factors;

[0083] The correlation model between electrical parameters and environmental factors is established as follows:

[0084] Mxgs=k0+k1*Q1+k2*Q2+k3*Q3+...+k n *Q n +∈

[0085] In the formula, Mxgs is the target variable, which represents the electrical parameters to be predicted, Q1, Q2, Q3 and Q n represents environmental factors and other factors that affect electrical parameters, including current, voltage, sag, and windage. k0 represents the intercept term, which means the predicted value of the target variable when all independent variables are 0. k1, k2, k3, and k n They respectively represent the degree of influence of each variable on the target variable. The size and sign of the coefficient indicate the direction and strength of the influence of the independent variable on the dependent variable. ∈ represents the error term, which indicates the part that the model cannot explain. Establishing a correlation model helps to fully understand the relationship between electrical parameters and the external environment and improve the fault prediction ability. By using the model for data analysis, a more intelligent monitoring and early warning mechanism can be achieved, reducing manual dependence.

[0086] S5. Develop abnormal state recognition algorithms to monitor and quantify the severity of abnormal states;

[0087] Using machine learning and data mining techniques, we analyze historical data and build a benchmark model to identify the characteristics of normal system operation. These benchmark data provide a reference for subsequent anomaly detection;

[0088] Abnormal state recognition algorithms usually use a combination of supervised learning and unsupervised learning. Supervised learning can train classification models through labeled historical data, while unsupervised learning can be used for anomaly detection of unlabeled data, using the clustering algorithm DBSCAN to identify data points that are significantly different from normal patterns.

[0089] Once an abnormal state is identified, the system will quantify the abnormal state through the set threshold and severity scoring model. The model can dynamically calculate the severity of the abnormality based on multiple indicators, such as the degree of deviation, historical frequency and impact range. At the same time, real-time data monitoring and processing technologies, such as Apache Kafka based on the stream processing framework, can ensure the timeliness and accuracy of anomaly detection;

[0090] In order to improve the recognition accuracy, time series analysis and feature engineering technologies are introduced to mine potential correlation features and patterns in time series data. Combined with visualization tools, the monitoring results and the quantitative degree of abnormal status can be intuitively displayed, so that operation and maintenance personnel can quickly determine the nature and impact of the abnormality and take corresponding recovery measures in time;

[0091] S6. Integrate all modules into a complete monitoring system and conduct pilot tests and verification on actual overhead transmission lines;

[0092] When integrating all modules into a complete monitoring system, it is necessary to ensure the seamless collaboration of each component, including the data acquisition unit, abnormal state recognition algorithm, electromagnetic sensor, and data transmission and processing module. During the system integration process, standardized interfaces and protocols should be adopted to ensure smooth data transmission between different modules. The Modbus industrial communication protocol is used to facilitate real-time information exchange.

[0093] The integrated system will undergo detailed system testing to ensure that each module can work stably under various environmental conditions. Pilot tests will be carried out on actual overhead transmission lines to verify the overall performance and reliability of the system. During the field test phase, sensors will be used to monitor various parameters such as electric field, magnetic field and vibration in real time, and data will be collected through a high-speed data acquisition system. The collected data will be transmitted to the central processing unit in real time and analyzed through the cloud platform or edge computing device to immediately feedback the detection and quantification results of abnormal conditions;

[0094] On-site implementation should also consider the impact of environmental factors on data collection. Therefore, multi-point deployment and flexible configuration are required to ensure that sensors can cover different risk areas. Through in-depth analysis of pilot data, the response time, accuracy and stability of the monitoring system are evaluated, and the algorithm model and sensor layout are optimized. During the entire test implementation process, user feedback is continuously collected and an improvement mechanism is established to continuously improve system performance.

[0095] Through the application of the above methods, not only can real-time and comprehensive monitoring of overhead transmission lines be achieved, but potential faults can also be identified early under extreme weather conditions, reducing the risk of power accidents and improving the safety and reliability of the system.

[0096] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for identifying abnormal state of overhead transmission lines based on non-intrusive electrical parameter reconstruction, characterized in that: The following steps are involved: S1. Conduct demand analysis, clarify the electrical parameters to be monitored, calculate the maximum current, voltage rating, and voltage and current fluctuation range, and select non-intrusive measurement solutions and electromagnetic sensors; S2, optimize the layout and array structure of electromagnetic sensors, calculate the electric and magnetic field intensity distribution of sensors, and determine the optimal position through finite element simulation; S3. Develop a high-speed data acquisition system to calculate data sampling rate, data transmission delay, and signal-to-noise ratio to ensure real-time acquisition and transmission of sensor data; S4. In the data processing stage, filtering, denoising and feature extraction algorithms are used to identify normal and abnormal states, and to establish a correlation model between electrical parameters and environmental factors; S5. Develop abnormal state recognition algorithms to monitor and quantify the severity of abnormal states; S6. Integrate all modules into a complete monitoring system and conduct pilot tests and verification on actual overhead transmission lines.

2. According to claim 1, a method for identifying abnormal state of overhead transmission lines based on non-intrusive electrical parameter reconstruction is characterized in that: The formula for calculating the maximum current of S1 is as follows: In the formula, Dlzd represents the maximum current, Dxyl represents the effective value of the current, and Fzyx represents the maximum instantaneous value of the AC signal.

3. The method for identifying abnormal state of overhead transmission lines based on non-intrusive electrical parameter reconstruction according to claim 2 is characterized in that: The formula for calculating the S1 voltage rating is as follows: In the formula, Dyed represents the rated voltage, and Dyyx represents the effective value of the voltage. It is used in literature and equipment specifications, and the unit is V. For DC voltage, the existing nominal value is the rated value and no further calculation is required.

4. The method for identifying abnormal state of overhead transmission lines based on non-intrusive electrical parameter reconstruction according to claim 3 is characterized in that: The formula for calculating the voltage and current fluctuation range of S1 is as follows: Byfw=V max -V min Blfw=I max -I min In the formula, Byfw represents the voltage fluctuation range, Blfw represents the current fluctuation range, V max Indicates the maximum voltage measured during the monitoring period, V min Indicates the minimum voltage measured during the monitoring period, I max Indicates the maximum current measured during the monitoring period, I min Indicates the minimum current measured during the monitoring period.

5. The method for identifying abnormal state of overhead transmission lines based on non-intrusive electrical parameter reconstruction according to claim 4 is characterized in that: The formula for calculating the electric field distribution strength of the S2 sensor is as follows: In the formula, Etdc represents the electric field distribution intensity of the sensor, and k represents the Coulomb constant, which is 8.99*10 9 Nm 2 / C 2 , Qt represents the charge of the point charge, and r represents the distance from the charge to the measurement point.

6. The method for identifying abnormal state of overhead transmission lines based on non-intrusive electrical parameter reconstruction according to claim 5, characterized in that: The formula for calculating the magnetic field distribution strength of the sensor S2 is as follows: In the formula, Ccqd represents the magnetic field distribution intensity of the sensor, ut0 represents the vacuum magnetic permeability, Zddl represents the current in the current-carrying wire, p represents the distance from the wire to the measuring point, and π represents 3.1415.

7. The method for identifying abnormal state of overhead transmission lines based on non-intrusive electrical parameter reconstruction according to claim 6 is characterized in that: The formula for calculating the data sampling rate of S3 is as follows: In the formula, Sc represents the data sampling rate, Nctb represents the number of samples collected within the time Tp, and Tp represents the time interval.

8. The method for identifying abnormal state of overhead transmission lines based on non-intrusive electrical parameter reconstruction according to claim 7, characterized in that: The formula for calculating the data transmission delay in S3 is as follows: In the formula, Sjyc represents the data transmission delay, Sjzl represents the amount of data to be transmitted, which is determined by the output format of the data acquisition system, Scsl is the data transmission rate, which represents the rate at which data is transmitted through the serial interface, and Clyc represents the data processing delay.

9. The method for identifying abnormal state of overhead transmission lines based on non-intrusive electrical parameter reconstruction according to claim 8, characterized in that: The formula for calculating the signal-to-noise ratio of S3 is as follows: In the formula, Xzbz represents the signal-to-noise ratio, Vsgl represents the signal voltage obtained by direct measurement, and Vnos represents the interference voltage in the device or environment.

10. The method for identifying abnormal state of overhead transmission lines based on non-intrusive electrical parameter reconstruction according to claim 9, characterized in that: The S4 establishes a correlation model between electrical parameters and environmental factors as follows: Mxgs=k0+k1*Q1+k2*Q2+k3*Q3+...+k n *Q n +∈ In the formula, Mxgs is the target variable, which represents the electrical parameters to be predicted, Q1, Q2, Q3 and Q n represents environmental factors and other factors that affect electrical parameters, including current, voltage, sag, and windage. k0 represents the intercept term, which means the predicted value of the target variable when all independent variables are 0. k1, k2, k3, and k n They respectively represent the degree of influence of each variable on the target variable. The size and sign of the coefficient indicate the direction and intensity of the influence of the independent variable on the dependent variable. ∈ represents the error term, which indicates the part that the model cannot explain.

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