Line arrester operation monitoring method and device based on dual-mode synchronous analysis

Abstract

The present invention provides a method and device for monitoring the operation of a line lightning arrester based on dual-modal synchronous analysis. The method is applied to an operation monitoring device including a lightning strike identification module, a signal acquisition module, and an operation diagnosis module. Specifically, the method comprises: identifying a lightning strike event, synchronously triggering the acquisition of dual-modal current waveform signals of an impulse current signal and an industrial frequency continuous current signal; dynamically correlating the dual-modal current waveform signals based on the time dimension and the amplitude dimension; establishing a correlation diagnosis model, and outputting an abnormality diagnosis result based on the correlated dual-modal current waveform signals. The present invention comprehensively and accurately grasps the recovery state of the line lightning arrester after operation by synchronously acquiring the dual-modal current waveform and dynamically correlating the dual-modal current waveform signals, more accurately identifying the abnormal state of the line lightning arrester, improving the accuracy of identifying potential fault hazards, reducing the probability of misjudgment and missed judgment, and ensuring the effectiveness and reliability of line lightning arrester monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment monitoring, and in particular to a method and device for monitoring the operation of a line lightning arrester based on dual-mode synchronous analysis. Background Art

[0002] For the safe and stable operation of power systems, line arresters, as key overvoltage protection devices, require accurate monitoring of their operating status. Existing line arrester monitoring devices widely use single-range sensors to collect relevant information for subsequent monitoring and operational analysis of line arrester operation. However, during a lightning strike, the instantaneous amplitude of the surge current can reach the kA level, with an extremely short duration, typically in the microsecond range. Meanwhile, the amplitude of the power-frequency continuous current is only mA, with a relatively long duration. Choosing a large-range sensor that accommodates the surge current as the corresponding single-range sensor will fully capture the surge current waveform. However, the amplitude of the continuous current signal is far below the sensor's lower range limit, resulting in extremely low signal resolution. The collected continuous current data is severely distorted and fails to accurately reflect its true characteristics. Conversely, choosing a small-range sensor that accommodates the continuous current signal will cause the sensor to saturate or even be damaged due to insufficient range when the surge current arrives, resulting in incomplete acquisition of the surge current waveform and loss of critical overvoltage information. This contradiction makes it impossible for a single-range sensor to simultaneously capture two current signals with huge amplitude differences, seriously affecting the subsequent accurate monitoring and analysis of the entire process of the lightning arrester operation.

[0003] To address the aforementioned issues with monitoring line arresters using a single-range sensor, related technologies have proposed dual-range monitoring devices. These devices can switch ranges by time, switching to a large range for acquisition when a surge current occurs and to a small range for acquisition of the power-frequency continuous current after the surge. However, range switching requires a certain response time, and during this switching process, key signals from the transition from surge current to continuous current may be missed, resulting in a gap in electrical characteristic information throughout the arrester's operation. Furthermore, because the acquisition of the two ranges is not performed synchronously, the collected surge current and continuous current waveforms lack a temporal correspondence, making it impossible to capture the correlation between the two. The recovery process of a line arrester after operation is a continuous electrical dynamic process. The correlation between surge current and continuous current contains important information about the arrester's internal state changes, such as changes in the nonlinear characteristics of the valve plate and the insulation recovery of the gap. This method of collecting information by time-sharing switching ranges to realize line arrester operation monitoring cannot achieve synchronous collection and correlation analysis, making it difficult to fully and accurately grasp the recovery status of the arrester after operation, and unable to timely discover potential fault hazards, thereby reducing the effectiveness and reliability of line arrester monitoring. Summary of the Invention

[0004] The purpose of the present invention is to overcome the shortcomings of the line lightning arrester monitoring device adopting a dual-range design in the prior art, which cannot timely discover the potential fault hidden dangers of the line lightning arrester when collecting information by time-sharing switching ranges to realize lightning arrester operation monitoring, and the low effectiveness and reliability of line lightning arrester monitoring. A line lightning arrester operation monitoring method and device based on dual-modal synchronous analysis are provided. By synchronously collecting dual-modal current waveforms, the current waveform signal is completely collected and the defects of range switching are eliminated, and the dual-modal current waveform signal is dynamically associated to comprehensively and accurately grasp the recovery state of the line lightning arrester after action, more accurately identify the abnormal state of the line lightning arrester, improve the accuracy of identifying potential fault hidden dangers, reduce the probability of misjudgment and missed judgment, and ensure the effectiveness and reliability of line lightning arrester monitoring.

[0005] The purpose of the present invention is achieved through the following technical solutions:

[0006] The line arrester operation monitoring method based on dual-mode synchronous analysis includes:

[0007] Identify lightning strike events and synchronously trigger the acquisition of dual-mode current waveform signals of impulse current signal and power frequency freewheeling signal;

[0008] Dynamically correlate dual-modal current waveform signals based on time and amplitude dimensions;

[0009] A correlation diagnosis model is established to output abnormal diagnosis results of line arresters based on the correlated dual-mode current waveform signals.

[0010] By synchronously triggering dual-mode current waveform acquisition, data acquisition with zero response time difference is achieved. This allows accurate capture of the key waveform segments where the impulse current transitions to the power frequency continuous current, avoiding signal omissions and distortion, and providing a reliable and accurate data foundation for subsequent line arrester operation monitoring and analysis. Based on the acquisition of complete current waveform data, the dual-mode current waveforms are dynamically correlated in both the time and amplitude dimensions to mine the potential arrester status information contained in the current waveforms. This allows for more accurate identification of abnormal line arrester states, improves the accuracy of identifying potential fault hazards, reduces the probability of misjudgments and missed judgments, and ensures the effectiveness and reliability of line arrester monitoring.

[0011] Furthermore, the dynamic correlation of the dual-modal current waveform signal based on the time dimension and the amplitude dimension includes:

[0012] A reference waveform is set based on the inrush current signal, and a time offset between the power frequency freewheeling signal and the reference waveform is calculated;

[0013] Based on the time offset, a dynamic time warping algorithm is used to find the optimal path for aligning the power frequency freewheeling signal.

[0014] Based on the found optimal path, the power frequency freewheeling signal is time-stretched or compressed to align the inrush current signal and the power frequency freewheeling signal.

[0015] Furthermore, the method of dynamically correlating the dual-modal current waveform signal based on the time dimension and the amplitude dimension further includes:

[0016] Extracting the amplitude features of the impulse current signal and the power frequency freewheeling signal respectively, and calculating the correlation between the amplitude features of the impulse current signal and the power frequency freewheeling signal;

[0017] Based on the correlation calculation results, the corresponding amplitude feature combination is constructed, and the relationship information of the amplitude feature combination is quantified through the regression model;

[0018] A composite data set is constructed based on the amplitude characteristics and corresponding relationship information.

[0019] Furthermore, the establishment of the correlation diagnosis model and outputting the abnormal diagnosis result of the line arrester according to the correlated dual-mode current waveform signal include:

[0020] Retrieve historical lightning event information and mark it based on the historical fault information of the line arrester;

[0021] Based on the impulse current signal and power frequency continuous current signal corresponding to historical lightning event information, combined with the corresponding annotations, a training dataset is constructed;

[0022] Construct an associated diagnosis model and train the associated diagnosis model based on the training data set.

[0023] Furthermore, the establishment of the correlation diagnosis model and outputting the abnormal diagnosis result of the line arrester according to the correlated dual-mode current waveform signal also includes:

[0024] When the associated diagnosis model outputs a conclusion that the line arrester has an abnormal operation, based on the corresponding output abnormality type and historical fault information, a corresponding typical abnormal waveform is established, and combined with the standard waveform of the line arrester, a standard waveform library is constructed;

[0025] Based on the standard waveform library and fuzzy logic algorithm, the health status index of the line lightning arrester is calculated according to the current correlated dual-modal current waveform signal, and the abnormal diagnosis result of the line lightning arrester is output.

[0026] Furthermore, the identification of a lightning strike event and the synchronous triggering of dual-mode current waveform signal acquisition of an impulse current signal and a power frequency freewheeling signal include:

[0027] When the instantaneous rate of change of the leakage line current of the line arrester exceeds a preset threshold, a lightning strike event is identified and dual-mode current waveform acquisition is synchronously triggered.

[0028] Furthermore, before dynamically correlating the dual-mode current waveform signals, the following steps are performed:

[0029] Perform dynamic gain adjustment and high-frequency filtering on the impact current signal;

[0030] Perform power frequency notching and baseline calibration on the power frequency freewheeling signal.

[0031] A line arrester operation monitoring device based on dual-modal synchronous analysis, used to execute any one of the above-mentioned line arrester operation monitoring methods based on dual-modal synchronous analysis, comprising:

[0032] The lightning strike identification module is installed at the discharge line end of the line arrester and is used to monitor the current amplitude of the discharge line in real time to identify lightning strike events and issue a collection trigger signal;

[0033] A signal acquisition module, responsive to the acquisition trigger signal of the lightning strike identification module, for acquiring the impulse current signal and the power frequency freewheeling signal;

[0034] The operation diagnosis module is connected to the signal acquisition module and is used to perform abnormal diagnosis on the line lightning arrester according to the impulse current signal and the power frequency continuous current signal.

[0035] Furthermore, the operation diagnosis module includes:

[0036] The signal processing unit is connected to the signal acquisition module and is used to pre-process the impulse current signal and the power frequency freewheeling signal;

[0037] A data fusion unit connected to the signal processing unit, configured to dynamically correlate the pre-processed impulse current signal and the power frequency freewheeling signal;

[0038] The abnormality diagnosis unit is connected to the data fusion unit and is used to perform abnormality diagnosis on the line lightning arrester according to the impulse current signal and the power frequency freewheeling signal after dynamic correlation.

[0039] Furthermore, the operation diagnosis module also includes:

[0040] The model building unit is used to establish a correlation diagnosis model based on historical lightning strike events, and optimize and adjust the correlation diagnosis model in combination with the abnormal diagnosis results of the abnormal diagnosis unit.

[0041] The beneficial effects of the present invention are:

[0042] By synchronously triggering dual-mode current waveform acquisition, data acquisition with zero response time difference is achieved. This allows accurate capture of the key waveform segments where the impulse current transitions to the power frequency continuous current, avoiding signal omissions and distortion, and providing a reliable and accurate data foundation for subsequent line arrester operation monitoring and analysis. Based on the acquisition of complete current waveform data, the dual-mode current waveforms are dynamically correlated in both the time and amplitude dimensions to mine the potential arrester status information contained in the current waveforms. This allows for more accurate identification of abnormal line arrester states, improves the accuracy of identifying potential fault hazards, reduces the probability of misjudgments and missed judgments, and ensures the effectiveness and reliability of line arrester monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0044] The present invention will be further described below with reference to the accompanying drawings and examples. Example

[0045] Inrush current and power-frequency freewheeling current actually influence and interact with each other during the arrester's operation. For example, the magnitude and waveform characteristics of the inrush current can affect the heating and aging of the valve plate inside the arrester, thereby changing the nonlinear resistance characteristics of the valve plate. This change in the nonlinear resistance characteristics of the valve plate directly affects the magnitude and decay rate of the freewheeling current. The duration and energy accumulation of the freewheeling current also affect the arrester's recovery process, determining whether it can quickly return to a normal insulation state. Therefore, traditional line arrester operation monitoring methods that independently analyze inrush current and freewheeling current can only obtain partial information from a single current signal and cannot comprehensively identify the changes in the electrical characteristics of the arrester after operation. This makes it difficult to accurately assess the arrester's recovery state after operation and cannot promptly detect potential faults, increasing the safety risks of power system operation. For example, monitoring the operation of a line arrester by analyzing only the peak value of the inrush current may not detect abnormal increases in freewheeling current caused by valve plate degradation. If only focusing on the decay of the freewheeling current, it is difficult to trace the potential damage caused by the inrush current to the valve plate.

[0046] Based on this, this embodiment proposes a line arrester operation monitoring method based on dual-mode synchronous analysis on the basis of a line arrester operation monitoring device with a dual-range design. Figure 1 Shown, including:

[0047] Identify lightning strike events and synchronously trigger the acquisition of dual-mode current waveform signals of impulse current signal and power frequency freewheeling signal;

[0048] Dynamically correlate dual-modal current waveform signals based on time and amplitude dimensions;

[0049] A correlation diagnosis model is established to output abnormal diagnosis results of line arresters based on the correlated dual-mode current waveform signals.

[0050] When a lightning strike occurs, data collection of the impulse current signal and the power frequency follow-on current signal is started synchronously to ensure that the collected current signal is not missed or distorted during the entire process from the generation of the impulse current to the attenuation of the power frequency follow-on current. The electrical information of the entire operation process of the line lightning arrester after the lightning strike is completely retained, providing a comprehensive and accurate data basis for subsequent abnormal diagnosis of the line lightning arrester.

[0051] Among them, in order to reduce the impact of normal current fluctuations or other interference factors on the abnormal diagnosis results of subsequent line lightning arresters, lightning strike events are identified. After the occurrence of the lightning strike event is determined, the data collection of the strike current signal and the power frequency follow-on current signal is synchronously triggered.

[0052] Specifically, the identification of a lightning strike event and the synchronous triggering of dual-mode current waveform signal acquisition of an impulse current signal and a power frequency freewheeling signal include:

[0053] When the instantaneous rate of change of the leakage line current of the line arrester exceeds a preset threshold, a lightning strike event is identified and dual-mode current waveform signal acquisition is synchronously triggered.

[0054] The current in the discharge line of a line arrester undergoes dramatic changes at the moment of a lightning strike, with the instantaneous rate of change far exceeding the current fluctuations during normal operation. By calculating the instantaneous rate of change in current in real time and comparing it with a preset threshold, lightning strike events can be quickly and accurately identified. Compared to traditional methods based on sudden voltage changes or simple current amplitude judgments, this method utilizes the transient variation of lightning current to reduce misjudgments caused by normal current fluctuations or other interference factors.

[0055] Moreover, the preset threshold corresponding to the instantaneous rate of change can be set and adjusted according to actual conditions to adapt to changes in lightning intensity in different regions and seasons, thereby improving the accuracy and reliability of lightning event identification.

[0056] In order to optimize the subsequent dual-mode current waveform signal, the collected dual-mode current waveform signals are preprocessed respectively to highlight the waveform characteristics of the impulse current signal and eliminate the influence of the lightning residual noise of the power frequency freewheeling signal.

[0057] The collected dual-mode current waveform signals are pre-processed separately, specifically including:

[0058] Perform dynamic gain adjustment and high-frequency filtering on the impact current signal;

[0059] Perform power frequency notching and baseline calibration on the power frequency freewheeling signal.

[0060] The amplitude of the impulse current signal fluctuates significantly at the moment of a lightning strike, and its peak value differs significantly from the current amplitude under normal operating conditions. To ensure that the impulse current signal waveform at all stages can be collected and recorded, the signal amplification factor is automatically adjusted through dynamic gain based on the real-time amplitude of the impulse current signal. For example, when the impulse current amplitude is small, the gain factor is increased to ensure that weak signal details are clearly presented. When the impulse current reaches its peak, the gain factor is reduced to avoid signal saturation and distortion due to over-amplification.

[0061] In order to reduce the high-frequency noise interference of other equipment in the power system, such as high-frequency harmonics generated by switching operations and stray high-frequency signals in the surrounding electromagnetic environment, the inrush current signal is subjected to high-frequency filtering through a bandpass filter or a low-pass filter.

[0062] In actual operation, the power-frequency freewheeling signal may contain interference from other frequency components, such as higher-order harmonics and lower-order harmonics. These harmonic components can affect the subsequent accurate measurement and analysis of key parameters such as the power-frequency freewheeling signal's amplitude and phase. Therefore, this embodiment employs a notch filter to suppress the harmonic components in the power-frequency freewheeling signal, thereby highlighting its power-frequency characteristics. This power-frequency notch filter processing makes the amplitude of the power-frequency freewheeling signal more accurate and its phase information more stable.

[0063] Due to factors such as sensor drift, ambient temperature fluctuations, and electromagnetic interference, the power-frequency freewheeling signal may experience baseline offset during acquisition. This means that the signal's zero-level reference line shifts. Therefore, based on the overall trend of the acquired power-frequency freewheeling signal, the baseline offset is calculated and compensated to accurately adjust the signal's baseline to zero level.

[0064] Through the above preprocessing, the signal quality of the collected impulse current signal and power frequency continuous current signal can be effectively improved, providing a more accurate and reliable data basis for subsequent dynamic correlation analysis, and reducing the impact of signal interference and errors on dynamic correlation analysis.

[0065] Considering that the action of the line lightning arrester during a lightning strike is a dynamic process with a strict time sequence, and many fault characteristics of the line lightning arrester are closely related to the correlation and changes between the impulse current and the power frequency continuous current in the time dimension. For example, valve plate degradation will lead to a decrease in the energy absorption efficiency of the impulse current, making the starting time of the power frequency continuous current earlier and the rising rate faster. The gap adhesion fault will change the conversion process from the impulse current to the power frequency continuous current, causing the continuous current duration to be abnormally prolonged. Therefore, this embodiment dynamically associates the dual-mode current waveform signal in the time dimension to present the order and change relationship between the impulse current and the power frequency continuous current in the time series, and accurately obtain the timing characteristics of the arrester action process.

[0066] Specifically, based on the time dimension, the dual-modal current waveform signals are dynamically correlated, including:

[0067] A reference waveform is set based on the inrush current signal, and a time offset between the power frequency freewheeling signal and the reference waveform is calculated;

[0068] Based on the time offset, a dynamic time warping algorithm is used to find the optimal path for aligning the power frequency freewheeling signal.

[0069] Based on the found optimal path, the power frequency freewheeling signal is time-stretched or compressed to align the inrush current signal and the power frequency freewheeling signal.

[0070] This embodiment specifically achieves precise time alignment at the sub-sampling point level by calculating the time offset, finding the optimal path and adjusting the signal, so as to accurately correspond each time point of the impulse current signal and the power frequency continuous current signal, and completely restore the electrical dynamic changes of the lightning arrester from the moment of lightning strike to the subsequent recovery process.

[0071] When calculating the time offset, the inrush current signal can be used as the reference waveform. The time offset between the two can be determined by finding the time difference between similar characteristic points in the power-frequency freewheeling signal and the reference waveform. The characteristic points of the reference waveform are selected as reference points. Points with corresponding energy changes or similar waveform shapes are found in the power-frequency freewheeling signal. The time intervals between each reference point and the corresponding similar point in the inrush current are calculated, and the corresponding average value is calculated to obtain the time offset.

[0072] When comparing the power frequency continuous current signal and the reference waveform, normalization processing is required to make the signal amplitudes on the same scale for easy comparison.

[0073] Since the impact current and the power frequency continuous current have different changing rhythms, direct point-by-point comparison will produce errors. Therefore, this embodiment adopts a dynamic time warping algorithm to construct a distance matrix, record the distance between the two signals at different time points, and limit the search range by the time offset. Using dynamic programming, the optimal path from the starting point to the end point is searched in the matrix. The optimal path found corresponds to the optimal time alignment method of the two signals.

[0074] The optimal path obtained by the dynamic time warping algorithm reflects the corresponding relationship between the impulse current signal and the power frequency continuous current signal in the time series. Based on this optimal path, the time scale of the power frequency continuous current signal can be locally expanded or contracted, and the time interval of each time point in the power frequency continuous current signal can be changed to achieve time alignment of the two signals and realize correlation in the time dimension, thereby giving time series characteristics to the features required for subsequent abnormal diagnosis.

[0075] After achieving correlation in the time dimension, we can further correlate the dual-modal current waveform signals from the amplitude dimension to achieve data fusion of the impulse current signal and the power frequency freewheeling signal, so as to more comprehensively display the characteristics of the dual-modal current waveform signals.

[0076] Specifically, in the amplitude dimension, the dual-modal current waveform signals are dynamically correlated, including:

[0077] Extracting the amplitude features of the impulse current signal and the power frequency freewheeling signal respectively, and calculating the correlation between the amplitude features of the impulse current signal and the power frequency freewheeling signal;

[0078] Based on the correlation calculation results, the corresponding amplitude feature combination is constructed, and the relationship information of the amplitude feature combination is quantified through the regression model;

[0079] A composite data set is constructed based on the amplitude characteristics and corresponding relationship information.

[0080] First, for the time-aligned impulse current signal and power frequency continuous current signal, starting from the feature types such as basic amplitude, dynamic amplitude change characteristics and frequency domain amplitude characteristics, multiple amplitude features are extracted respectively, including peak value, effective value, average amplitude, amplitude change rate, harmonic amplitude, etc., and the extracted amplitude features will have corresponding timing information based on the correlation of time dimension.

[0081] The Pearson correlation coefficient is then used to calculate the correlation between the amplitude features of the inrush current signal and the power-frequency freewheeling signal. Based on the correlation results, the amplitude features with strong correlations are combined. Specifically, a correlation threshold can be set to filter out amplitude feature groups that meet the threshold conditions. Each feature combination represents a correlation pattern between the inrush current signal and the power-frequency freewheeling signal in the amplitude dimension.

[0082] Then, for each amplitude feature combination, a corresponding model is constructed through linear regression or nonlinear regression method, and solved by least squares method to quantitatively describe the relationship between the amplitude features of the impulse current signal and the power frequency freewheeling signal.

[0083] The extracted amplitude characteristic parameters of the inrush current and power-frequency freewheeling current are integrated in chronological order to construct a composite dataset. Each data sample in the composite dataset contains all the amplitude characteristics of the inrush current signal and the power-frequency freewheeling current signal within a certain time period, as well as the related quantized relationship information.

[0084] In order to achieve efficient identification of abnormal operation of line lightning arresters, a correlation diagnosis model is further established based on historical lightning events to realize abnormal operation diagnosis of line lightning arresters based on the constructed composite data set.

[0085] Specifically, the associated diagnosis model is established, including:

[0086] Retrieve historical lightning event information and mark it based on the historical fault information of the line arrester;

[0087] Based on the impulse current signal and power frequency continuous current signal corresponding to historical lightning event information, combined with the corresponding annotations, a training dataset is constructed;

[0088] Construct an associated diagnosis model and train the associated diagnosis model based on the training data set.

[0089] Historical lightning event information is retrieved from multiple data sources, including the power system's historical database, monitoring system logs, and fault recording platforms. This historical lightning event information includes at least basic information such as the time, location, line number, and arrester model of each lightning strike, as well as the corresponding surge current signal and power frequency continuous current signal data. To ensure the integrity and accuracy of the extracted data, preliminary data screening and verification are performed during the retrieval process to eliminate data records with obvious errors or omissions.

[0090] Based on the historical fault information of the line arrester, the retrieved historical lightning strike event information is annotated to distinguish the operating status of the line arrester after each lightning strike, such as normal operation, valve plate degradation, gap adhesion, internal moisture, and other different fault types. For lightning strike events without faults, normal labels are added, and for events with faults, accurate labels can be added according to the specific fault type.

[0091] Combined with the above-mentioned dynamic correlation method of time dimension and amplitude dimension, the impulse current signal and power frequency continuous current signal corresponding to the historical lightning event information are processed, and the training data set is constructed by combining the corresponding annotations.

[0092] A machine learning algorithm or a deep learning algorithm may be used to construct an associated diagnosis model. Considering the time series characteristics of the current signal, this embodiment specifically uses a recurrent neural network algorithm to construct the associated diagnosis model.

[0093] The constructed correlation diagnosis model is then trained and learned using the constructed training data set to learn the characteristic patterns and correlation rules of the dual-modal current waveform signals under different fault types, thereby achieving accurate diagnosis of the abnormal state of the line lightning arrester.

[0094] The composite data set is then input into the trained association diagnosis model to output a conclusion on whether an anomaly exists and the specific type of anomaly.

[0095] To further optimize the operation and maintenance efficiency of line arresters, when the associated diagnosis model outputs a conclusion that the line arrester has an abnormal operation, a corresponding typical abnormal waveform is established based on the corresponding output abnormality type and historical fault information. This is combined with the standard waveform of the line arrester to build a standard waveform library.

[0096] Based on the standard waveform library and fuzzy logic algorithm, the health status index of the line lightning arrester is calculated according to the current correlated dual-modal current waveform signal, and the abnormal diagnosis result of the line lightning arrester is output.

[0097] When the associated diagnostic model outputs that the line arrester has an operational abnormality and a specific abnormality type, such as valve plate degradation or gap adhesion, the abnormal waveform collection program is immediately started. From the historical fault information, the waveform data of all impulse current signals and power frequency continuous current signals related to the abnormality type are extracted. These waveform data are screened to remove invalid waveforms caused by data acquisition errors and signal interference, and retain typical waveforms that can truly reflect the characteristics of the abnormality type. The established typical abnormal waveforms are then compared with the standard waveforms of the current of the line arrester during normal operation to construct a standard waveform library. Fuzzy logic rules are formulated based on historical fault information and expert experience. The formulated fuzzy logic rules cover the relationship between different feature combinations and the health status of the arrester.

[0098] Multiple features, such as amplitude, frequency, rise time, fall time, and harmonic content, are extracted from the correlated bimodal current waveform signal. Fuzzy sets, such as "low," "medium," "high," "short," and "long," are then defined for each feature based on the characteristic ranges of various waveforms in the standard waveform library. A membership function is then used to calculate the degree of membership of the current waveform feature within each fuzzy set.

[0099] Based on the calculated membership of each waveform feature to each fuzzy set and the established fuzzy logic rules, the membership of each health status fuzzy set is determined through fuzzy logic operations such as AND and OR operations. Defuzzification methods such as weighted average and maximum membership methods are then used to obtain the health status index of the line arrester based on the membership of each health status fuzzy set.

[0100] By integrating the identified abnormality type and health status index, the degree of abnormality of the line lightning arrester can be identified, and then the maintenance needs of the line lightning arrester can be determined. It can be decided whether to perform simple maintenance, replace components, or replace the entire line lightning arrester, thereby optimizing the allocation of operation and maintenance resources and ensuring the safe and stable operation of the power system.

[0101] Another aspect of this embodiment further provides a line arrester operation monitoring device based on dual-mode synchronous analysis, comprising:

[0102] The lightning strike identification module is installed at the discharge line end of the line arrester and is used to monitor the current amplitude of the discharge line in real time to identify lightning strike events and issue a collection trigger signal;

[0103] A signal acquisition module, responsive to the acquisition trigger signal of the lightning strike identification module, for acquiring the impulse current signal and the power frequency freewheeling signal;

[0104] The operation diagnosis module is connected to the signal acquisition module and is used to perform abnormal diagnosis on the line lightning arrester according to the impulse current signal and the power frequency continuous current signal.

[0105] The lightning strike identification module described in this embodiment includes at least a current sensor based on a Rogowski coil and a microcontroller that sends a synchronous trigger signal based on the data collected by the current sensor. It can accurately measure the current amplitude in the leakage line of the line lightning arrester, has the advantages of fast response speed, high accuracy, good linearity, etc., and can monitor the changes in current in real time.

[0106] The signal acquisition module contains two acquisition channel sensor units, which respectively collect the inrush current signal and the power frequency continuous current signal.

[0107] For the sensor unit that collects the impulse current signal, a high-frequency impulse current sensor based on a Rogowski coil can be used, which can capture the full waveform of the impulse current at the moment of lightning strike.

[0108] For the sensor unit that collects the power frequency freewheeling signal, a high-sensitivity freewheeling sensor based on a zero-flux Hall element can be used to continuously monitor the power frequency freewheeling after the line lightning arrester is activated.

[0109] In addition, the signal acquisition module needs to be equipped with an additional anti-interference shielding cavity, adopting a double-layer electromagnetic shielding structure, and dividing the interior into independent isolation areas to install the impact sensor and the freewheeling sensor separately to reduce mutual inductance interference.

[0110] The operation diagnosis module includes:

[0111] The signal processing unit is connected to the signal acquisition module and is used to pre-process the impulse current signal and the power frequency freewheeling signal;

[0112] A data fusion unit connected to the signal processing unit, configured to dynamically correlate the pre-processed impulse current signal and the power frequency freewheeling signal;

[0113] The abnormality diagnosis unit is connected to the data fusion unit and is used to perform abnormality diagnosis on the line lightning arrester according to the impulse current signal and the power frequency freewheeling signal after dynamic correlation.

[0114] In order to ensure the data processing efficiency and accuracy of the operation diagnosis module, multiple functional units are further set up to process the data of different stages of line lightning arrester abnormality diagnosis respectively.

[0115] The operation diagnosis module also includes:

[0116] The model building unit is used to establish a correlation diagnosis model based on historical lightning strike events, and optimize and adjust the correlation diagnosis model in combination with the abnormal diagnosis results of the abnormal diagnosis unit.

[0117] In order to optimize the recognition accuracy of the associated diagnosis model, a model construction unit is further set up. On the basis of establishing the associated diagnosis model based on historical lightning strike events, the associated diagnosis model is continuously optimized according to each abnormal diagnosis result.

[0118] The signal processing unit, data fusion unit, abnormality diagnosis unit and model building unit of the above-mentioned operation diagnosis module are all microprocessors with built-in corresponding data processing and analysis algorithms.

[0119] The embodiment described above is only a preferred solution of the present invention and does not limit the present invention in any form. Other variations and modifications are possible without exceeding the technical solution described in the claims.

Claims

1. A line arrester operation monitoring method based on dual-mode synchronous analysis is characterized in that: include: Identify lightning strike events and synchronously trigger the acquisition of dual-mode current waveform signals of impulse current signal and power frequency freewheeling signal; Dynamically correlate dual-modal current waveform signals based on time and amplitude dimensions; Establish a correlation diagnosis model and output abnormal diagnosis results of line arresters based on the correlated dual-mode current waveform signals; The method of dynamically correlating the dual-mode current waveform signal based on the time dimension and the amplitude dimension includes: A reference waveform is set based on the impulse current signal, and the time offset between the power frequency freewheeling signal and the reference waveform is calculated. Specifically, the impulse current signal is first used as the reference waveform, and then the time offset between the power frequency freewheeling signal and the reference waveform is determined by finding the time difference between similar feature points. The feature points of the reference waveform are selected as reference points, and points with corresponding energy changes or similar waveform shapes are found in the power frequency freewheeling signal. The time intervals between each reference point and the corresponding similar point of the impulse current are calculated, and the corresponding average value is obtained, which is the time offset. Based on the time offset, a dynamic time warping algorithm is used to find the optimal path for aligning the power frequency freewheeling signals. This algorithm constructs a distance matrix that records the distances between the two signals at different time points. The time offset is used to limit the search range. Dynamic programming is used to search the matrix for the optimal path from the starting point to the end point. The optimal path found corresponds to the optimal time alignment of the two signals. Based on the optimal path found, the power frequency freewheeling signal is time-stretched or compressed to align the inrush current signal and the power frequency freewheeling signal, thus achieving correlation in the time dimension. Dynamically correlate dual-modal current waveform signals based on time and amplitude dimensions, including: Extracting the amplitude features of the time-aligned impulse current signal and the power-frequency freewheeling signal respectively, and calculating the correlation between the amplitude features of the impulse current signal and the power-frequency freewheeling signal; Based on the correlation calculation results, the corresponding amplitude feature combination is constructed, and the relationship information of the amplitude feature combination is quantified through the regression model; A composite data set is constructed based on the amplitude characteristics and corresponding relationship information.

2. The line arrester operation monitoring method based on dual-mode synchronous analysis according to claim 1 is characterized in that: The establishment of the correlation diagnosis model and outputting the abnormal diagnosis result of the line lightning arrester according to the correlated dual-mode current waveform signal include: Retrieve historical lightning event information and mark it based on the historical fault information of the line arrester; Based on the impulse current signal and power frequency continuous current signal corresponding to historical lightning event information, combined with the corresponding annotations, a training dataset is constructed; Construct an associated diagnosis model and train the associated diagnosis model based on the training data set.

3. The line arrester operation monitoring method based on dual-mode synchronous analysis according to claim 1 is characterized in that: The method of establishing a correlation diagnosis model and outputting abnormality diagnosis results of the line arrester according to the correlated dual-mode current waveform signal further includes: When the associated diagnosis model outputs a conclusion that the line arrester has an abnormal operation, based on the corresponding output abnormality type and historical fault information, a corresponding typical abnormal waveform is established, and combined with the standard waveform of the line arrester, a standard waveform library is constructed; Based on the standard waveform library and fuzzy logic algorithm, the health status index of the line lightning arrester is calculated according to the current correlated dual-mode current waveform signal; Combined with historical fault information, the abnormal diagnosis results of the line lightning arrester are output.

4. The line arrester operation monitoring method based on dual-mode synchronous analysis according to claim 1 is characterized in that: The method of identifying a lightning strike event and synchronously triggering the collection of dual-mode current waveform signals of an impulse current signal and a power frequency freewheeling signal includes: When the instantaneous rate of change of the leakage line current of the line arrester exceeds a preset threshold, a lightning strike event is identified and dual-mode current waveform acquisition is synchronously triggered.

5. The line arrester operation monitoring method based on dual-mode synchronous analysis according to claim 1 is characterized in that: Before dynamically correlating the dual-mode current waveform signals, the following steps are also performed: Perform dynamic gain adjustment and high-frequency filtering on the impact current signal; Perform power frequency notching and baseline calibration on the power frequency freewheeling signal.

6. A line arrester operation monitoring device based on dual-modal synchronous analysis, used to execute the line arrester operation monitoring method based on dual-modal synchronous analysis according to any one of claims 1 to 5, characterized in that: include: The lightning strike identification module is installed at the discharge line end of the line arrester and is used to monitor the current amplitude of the discharge line in real time to identify lightning strike events and issue a collection trigger signal; A signal acquisition module, responsive to the acquisition trigger signal of the lightning strike identification module, for acquiring the impulse current signal and the power frequency freewheeling signal; The operation diagnosis module is connected to the signal acquisition module and is used to perform abnormal diagnosis on the line lightning arrester according to the impulse current signal and the power frequency continuous current signal.

7. The line arrester operation monitoring device based on dual-mode synchronous analysis according to claim 6 is characterized in that: The operation diagnosis module includes: The signal processing unit is connected to the signal acquisition module and is used to pre-process the impulse current signal and the power frequency freewheeling signal; A data fusion unit connected to the signal processing unit, configured to dynamically correlate the pre-processed impulse current signal and the power frequency freewheeling signal; The abnormality diagnosis unit is connected to the data fusion unit and is used to perform abnormality diagnosis on the line lightning arrester according to the impulse current signal and the power frequency freewheeling signal after dynamic correlation.

8. The line arrester operation monitoring device based on dual-mode synchronous analysis according to claim 6, characterized in that: The operation diagnosis module also includes: The model building unit is used to establish a correlation diagnosis model based on historical lightning strike events, and optimize and adjust the correlation diagnosis model in combination with the abnormal diagnosis results of the abnormal diagnosis unit.

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