Line arrester operation monitoring method and device based on bimodal synchronous analysis

Abstract

The invention provides a line arrester operation monitoring method and device based on bimodal synchronous analysis, and the method is applied to an operation monitoring device comprising a lightning stroke recognition module, a signal collection module and an operation diagnosis module, and specifically comprises the steps: recognizing a lightning stroke event; bimodal current waveform signal acquisition for synchronously triggering an impact current signal and a power frequency follow current signal; dynamically associating the bimodal current waveform signal based on the time dimension and the amplitude dimension; and establishing a correlation diagnosis model, and outputting an abnormality diagnosis result according to the correlated bimodal current waveform signal. According to the method, the dual-mode current waveform is synchronously collected and the dual-mode current waveform signal is dynamically associated, so that the recovery state of the line arrester after action is comprehensively and accurately mastered, the abnormal state of the line arrester is more accurately identified, the identification accuracy of potential fault hidden dangers is improved, the occurrence probability of misjudgment and missed judgment is reduced, and the reliability of the line arrester is improved. And the effectiveness and reliability of line lightning arrester monitoring are ensured.

Description

Technical Field

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

[0002] In the safe and stable operation of the power system, the line arrester is a key overvoltage protection device, and its accurate monitoring of the operating status is crucial. The existing line arrester monitoring device widely uses a single range sensor to collect relevant information to achieve subsequent monitoring and operation analysis of the line arrester action. However, when a lightning strike occurs, the instantaneous amplitude of the impulse lightning current can reach the kA level, and the duration is extremely short, usually in the microsecond level, while the power frequency continuous current amplitude is only mA level, and the duration is relatively long. If a large range adapted to the impulse lightning current is selected as the corresponding single range sensor, although the impulse lightning current waveform can be fully collected, for the power frequency continuous current signal, since its amplitude is far below the lower limit of the sensor range, it will cause the signal resolution to be extremely low, and the collected power frequency continuous current data will be seriously distorted and cannot accurately reflect its true characteristics. On the contrary, if a small range adapted to the power frequency continuous current is selected, when the impulse lightning current arrives, the sensor will be saturated or even damaged due to insufficient range, resulting in incomplete acquisition of the impulse lightning current waveform and loss of key 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] In order to solve the problems existing in the above-mentioned line arrester monitoring using a single range sensor, the relevant technology proposes a monitoring device with a dual range design. This type of device can switch to a large range of acquisition when the impulse lightning current appears, and switch to a small range of acquisition of power frequency continuous current after the impact by switching the range in a time-sharing manner. However, the range switching requires a certain response time. During the switching process, the key signal of the transition stage from the impulse lightning current to the power frequency continuous current may be missed, resulting in a fault in the electrical characteristic information of the entire process of the arrester operation. And because the acquisition of the two ranges is not carried out synchronously, the collected impulse lightning current and the power frequency continuous current waveform lack a time correspondence, and the correlation between the two cannot be captured. The recovery process after the line arrester is activated is a continuous electrical dynamic change process. The correlation between the impulse lightning current and the power frequency continuous current contains important information about the changes in the internal state of the arrester, such as the nonlinear characteristic changes of the valve plate and the insulation recovery of the gap. This method of collecting information by switching the range at different times to realize the operation monitoring of the line lightning arrester cannot realize synchronous collection and correlation analysis, and it is difficult to fully and accurately grasp the recovery status of the lightning arrester after action, and it is impossible to discover potential fault hazards in time, which reduces the effectiveness and reliability of line lightning 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 using a dual-range design in the prior art, which cannot timely discover the potential fault hazards of the line lightning arrester when collecting information by time-sharing switching ranges to realize 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 the action, more accurately identify the abnormal state of the line lightning arrester, improve the accuracy of identifying potential fault hazards, 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: The line arrester operation monitoring method based on dual-mode synchronous analysis includes: Identify lightning strike events and synchronously trigger the acquisition of dual-mode current waveform signals of impulse current signal and power frequency follow-up signal; Based on the time dimension and the amplitude dimension, the dual-mode current waveform signal is dynamically correlated; A correlation diagnosis model is established, and the abnormal diagnosis results of the line arrester are output according to the correlated dual-mode current waveform signal.

[0006] By synchronously triggering the dual-mode current waveform acquisition, data acquisition with zero response time difference is achieved, which can accurately capture the key waveform segments of the transition from impulse current to power frequency continuous current, avoid signal omission and distortion problems, and provide a reliable and accurate data basis for subsequent line arrester operation monitoring and analysis. On the basis of obtaining complete current waveform data, the dual-mode current waveform is further dynamically associated in the time dimension and amplitude dimension to mine the potential state information of the arrester contained in the current waveform, so as to more accurately identify the abnormal state of the line arrester, improve the accuracy of identifying potential fault hazards, reduce the probability of misjudgment and missed judgment, and ensure the effectiveness and reliability of line arrester monitoring.

[0007] Furthermore, the dynamically correlating dual-mode current waveform signals based on the time dimension and the amplitude dimension includes: A reference waveform is set based on the impulse current signal, and a time offset between the power frequency continuous current signal and the reference waveform is calculated; Based on the time offset, the optimal path for aligning the power frequency freewheeling signal is found through the dynamic time warping algorithm; Based on the found optimal path, the power frequency follow-up signal is time-stretched or compressed to align the impulse current signal and the power frequency follow-up signal.

[0008] Further, the dynamic correlation of the bimodal current waveform signals based on the time dimension and the amplitude dimension further includes: Extract the amplitude characteristics of the impulse current signal and the power frequency follow - current signal respectively, and calculate the correlation between the amplitude characteristics of the impulse current signal and the power frequency follow - current signal; Construct a corresponding amplitude - characteristic combination based on the correlation calculation result, and quantify the relationship information of the amplitude - characteristic combination through a regression model; Construct a composite data set according to the amplitude characteristics and the corresponding relationship information.

[0009] Further, establishing the correlation diagnosis model, and outputting the abnormal diagnosis result of the line lightning arrester according to the correlated bimodal current waveform signal includes: Retrieve the historical lightning strike event information, and label the historical lightning strike event information according to the historical fault information of the line lightning arrester; Based on the impulse current signal and the power frequency follow - current signal corresponding to the historical lightning strike event information, and combined with the corresponding label, construct a training data set; Construct a correlation diagnosis model, and train the correlation diagnosis model based on the training data set.

[0010] Further, establishing the correlation diagnosis model, and outputting the abnormal diagnosis result of the line lightning arrester according to the correlated bimodal current waveform signal further includes: When the correlation diagnosis model outputs a conclusion that the line lightning arrester has an abnormal operation, based on the corresponding abnormal type output, establish a corresponding typical abnormal waveform according to the historical fault information, and combine it with the standard waveform of the line lightning arrester to construct a standard waveform library; Based on the standard waveform library, through the fuzzy logic algorithm, calculate the health state index of the line lightning arrester according to the currently correlated bimodal current waveform signal, and output the abnormal diagnosis result of the line lightning arrester.

[0011] Further, the identification of the lightning strike event and the synchronous triggering of the acquisition of the bimodal current waveform signals of the impulse current signal and the power frequency follow - current signal includes: When the instantaneous change rate of the current in the discharge line of the line lightning arrester exceeds a preset threshold, identify that a lightning strike event has occurred, and synchronously trigger the bimodal current waveform acquisition.

[0012] Further, before dynamically correlating the bimodal current waveform signals, the following is also performed: Perform dynamic gain adjustment and high - frequency filtering processing on the impulse current signal; Perform power - frequency notch filtering and baseline calibration processing on the power frequency follow - current signal.

[0013] A line arrester operation monitoring device based on dual-mode synchronous analysis, used to execute any one of the above-mentioned line arrester operation monitoring methods based on dual-mode synchronous analysis, comprising: The lightning strike identification module is set at the leakage line end of the line arrester, and is used to monitor the leakage line current amplitude in real time to identify lightning strike events and send out acquisition trigger signals; A signal acquisition module, in response to the acquisition trigger signal of the lightning strike identification module, is used to collect the impulse current signal and the power frequency continuous current 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.

[0014] Furthermore, the operation diagnosis module includes: A signal processing unit, connected to the signal acquisition module, for preprocessing the impulse current signal and the power frequency continuous current signal; A data fusion unit connected to the signal processing unit and used for dynamically associating the preprocessed impulse current signal with 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 continuous current signal after dynamic correlation.

[0015] Furthermore, the operation diagnosis module also includes: The model building unit is used to establish a correlation diagnosis model according to historical lightning strike events, and optimize and adjust the correlation diagnosis model in combination with the abnormal diagnosis results of the abnormal diagnosis unit.

[0016] The beneficial effects of the present invention are: By synchronously triggering the dual-mode current waveform acquisition, data acquisition with zero response time difference is achieved, which can accurately capture the key waveform segments of the transition from impulse current to power frequency continuous current, avoid signal omission and distortion problems, and provide a reliable and accurate data basis for subsequent line arrester operation monitoring and analysis. On the basis of obtaining complete current waveform data, the dual-mode current waveform is further dynamically associated in the time dimension and amplitude dimension to mine the potential state information of the arrester contained in the current waveform, so as to more accurately identify the abnormal state of the line arrester, improve the accuracy of identifying potential fault hazards, reduce the probability of misjudgment and missed judgment, and ensure the effectiveness and reliability of line arrester monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of a process of the present invention. DETAILED DESCRIPTION

[0018] The present invention is further described below in conjunction with the accompanying drawings and embodiments. Example

[0019] The impulse current and power frequency follow current actually affect and interact with each other during the operation of the lightning arrester. For example, the magnitude and waveform characteristics of the impulse current will affect the heating and aging degree of the valve disc inside the lightning arrester, and then change the non-linear resistance characteristics of the valve disc. This change in the non-linear resistance characteristics of the valve disc will directly affect parameters such as the magnitude and decay rate of the follow current. Also, the duration and energy accumulation of the follow current will act on the recovery process of the lightning arrester and determine whether it can quickly return to the normal insulation state. Therefore, the traditional method of monitoring the operation of line lightning arresters by independently analyzing the impulse current and follow current can only obtain partial information of a single current signal, and cannot identify the change law of the electrical characteristics after the lightning arrester operates as a whole. It is difficult to accurately evaluate the recovery state of the lightning arrester after operation, and it is impossible to detect potential faults in time, increasing the safety risk of the power system operation. For example, if only the method of analyzing the peak value of the impulse current is used to monitor the operation state of the line lightning arrester, it may not be able to detect the problem of abnormal increase in the follow current caused by valve disc deterioration. If only the decay situation of the follow current is concerned, it is difficult to trace the potential damage caused by the impulse current to the valve disc.

[0020] Based on this, in this embodiment, on the basis of a line lightning arrester operation monitoring device with a dual-range design, a line lightning arrester operation monitoring method based on dual-modal synchronous analysis is proposed, as Figure 1 shown, including: Identifying a lightning strike event and synchronously triggering the acquisition of dual-modal current waveform signals of the impulse current signal and the power frequency follow 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 abnormal diagnosis result of the line lightning arrester according to the correlated dual-modal current waveform signals.

[0021] When a lightning strike occurs, the data acquisition of the impulse current signal and the power frequency follow current signal is synchronously started to ensure that during the whole process from the generation of the impulse current to the decay of the power frequency follow current, the acquired current signals are without omission and distortion, and the electrical information of the whole process of the line lightning arrester's operation after the lightning strike is completely retained, providing a comprehensive and accurate data basis for the subsequent abnormal diagnosis of the line lightning arrester.

[0022] Among them, in order to reduce the influence of normal current fluctuations or other interference factors on the subsequent abnormal diagnosis results of the line lightning arrester, the lightning strike event is identified, and after determining that the lightning strike event occurs, the data acquisition of the impulse current signal and the power frequency follow current signal is synchronously triggered accordingly.

[0023] Specifically, the step of identifying the lightning strike event and synchronously triggering the acquisition of dual-modal current waveform signals of the impulse current signal and the power frequency follow current signal includes: When the instantaneous change rate of the discharge line current of the line lightning arrester exceeds the preset threshold, a lightning strike event is identified, and the acquisition of the dual-mode current waveform signal is synchronously triggered.

[0024] The discharge line current of the line lightning arrester will change violently during a lightning strike, and its instantaneous change rate far exceeds the current fluctuation during normal operation. By calculating the instantaneous change rate of the current in real time and comparing it with the preset threshold, lightning strike events can be identified quickly and accurately. Compared with traditional methods based on voltage mutation or simple current amplitude judgment, it utilizes the transient change characteristics of lightning strike current and reduces misjudgment caused by normal current fluctuations or other interference factors.

[0025] Moreover, for the preset threshold corresponding to the instantaneous change rate, it can be set and adjusted according to the actual situation to adapt to the lightning strike intensity changes in different regions and seasons, and improve the accuracy and reliability of lightning strike event identification.

[0026] 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 strike residual noise of the power frequency follow current signal.

[0027] The collected dual-mode current waveform signals are preprocessed respectively, which specifically includes: Performing dynamic gain adjustment and high-frequency filtering on the impulse current signal; Performing power frequency notch filtering and baseline calibration on the power frequency follow current signal.

[0028] The amplitude of the impulse current signal changes greatly during a lightning strike, and its peak value is quite different from the current amplitude under normal operating conditions. In order to ensure that the waveforms of the impulse current signal in all stages can be collected and recorded, according to the real-time amplitude of the impulse current signal, the amplification factor of the signal is automatically adjusted by the dynamic gain method. For example, when the amplitude of the impulse current is small, the gain multiple is increased to ensure that the details of the weak signal are clearly presented. When the impulse current reaches the peak value, the gain multiple is reduced to avoid signal saturation distortion due to excessive amplification.

[0029] In order to reduce the high-frequency noise interference of other equipment in the power system, such as high-frequency harmonics generated by switch operations, stray high-frequency signals in the surrounding electromagnetic environment, etc., the impulse current signal is subjected to high-frequency filtering through a band-pass filter or a low-pass filter, etc.

[0030] In actual operation, there may be interference from other frequency components in the power-frequency follow current signal, such as high-order harmonics, low-order harmonics, etc. These harmonic components will affect the accurate measurement and analysis of key parameters such as the amplitude and phase of the power-frequency follow current signal. Therefore, in this embodiment, a notch filter is set to suppress the harmonic components in the power-frequency follow current signal to highlight its power-frequency characteristics. After power-frequency notch processing, the amplitude of the power-frequency follow current signal can be made more accurate and the phase information more stable.

[0031] Affected by factors such as the drift of the sensor itself, changes in environmental temperature, and electromagnetic interference, the power-frequency follow current signal may exhibit a baseline shift phenomenon during the acquisition process, that is, the position of the zero-level reference line of the signal changes. Therefore, according to the overall trend of the collected power-frequency follow current signal, the baseline offset amount is calculated and compensated to adjust the baseline of the power-frequency follow current signal to the accurate zero-level position.

[0032] Through the above preprocessing, the signal quality of the collected impulse current signal and power-frequency follow current signal can be effectively improved, providing a more accurate and reliable data basis for subsequent dynamic correlation analysis, and reducing the influence of signal interference and errors on dynamic correlation analysis.

[0033] Considering that the operation of the line arrester during lightning strikes is a dynamic process with a strict time sequence, and many fault characteristics of the line arrester are closely related to the correlated changes of the impulse current and power-frequency follow current in the time dimension. For example, the deterioration of the varistor will cause a decrease in the impulse current energy absorption efficiency, resulting in an earlier start time and a faster rising rate of the power-frequency follow current. The gap adhesion fault will change the conversion process of the impulse current to the power-frequency follow current, causing the follow current duration to be abnormally extended, etc. Therefore, in this embodiment, the dual-mode current waveform signals are dynamically correlated in the time dimension to present the sequence and change relationship of the impulse current and power-frequency follow current in the time series, and accurately obtain the timing characteristics of the arrester operation process.

[0034] Specifically, based on the time dimension, dynamically correlating the dual-mode current waveform signals includes: Setting a reference waveform based on the impulse current signal and calculating the time offset between the power-frequency follow current signal and the reference waveform; Based on the time offset, finding the optimal path for aligning the power-frequency follow current signal through the dynamic time warping algorithm; Based on the found optimal path, stretching or compressing the power-frequency follow current signal in time to align the impulse current signal and the power-frequency follow current signal.

[0035] In this embodiment, through calculating the time offset, finding the optimal path, and signal adjustment, precise time alignment at the sub-sampling point level is achieved to accurately correspond each time point of the impulse current signal and the power-frequency follow current signal, and completely restore the electrical dynamic changes of the arrester from the lightning strike moment to the subsequent recovery process.

[0036] Among them, when calculating the time offset, the impulse current signal can be used as the reference waveform first. Then, by finding the time difference between the similar characteristic points in the power frequency follow - current signal and the reference waveform, the time offset between the two can be determined. Select the characteristic points of the reference waveform as the reference points, find the points with corresponding energy changes or similar waveform patterns in the power frequency follow - current signal, calculate the time intervals between each reference point and the corresponding similar points of the impulse current, and obtain the corresponding average value, which is the time offset.

[0037] When comparing the power frequency follow - current signal and the reference waveform, normalization processing needs to be carried out to facilitate the comparison of signal amplitudes on the same scale.

[0038] Since the change rhythms of the impulse current and the power frequency follow - current are different, direct point - by - point comparison will produce errors. Therefore, in this embodiment, the dynamic time warping algorithm is adopted to construct a distance matrix, record the distances between different time points of the two signals, and limit the search range through the time offset. Using the dynamic programming method, search for the optimal path from the starting point to the ending point in the matrix. The found optimal path corresponds to the best time alignment method of the two signals.

[0039] The optimal path obtained through the dynamic time warping algorithm reflects the corresponding relationship between the impulse current signal and the power frequency follow - current signal in the time series. Based on this optimal path, the time scale of the power frequency follow - current signal can be locally expanded or contracted, changing the time intervals of each time point in the power frequency follow - current signal, realizing the time alignment of the two signals, achieving the association in the time dimension, and endowing the features required for subsequent abnormal diagnosis with time - series features.

[0040] After realizing the association in the time dimension, it is possible to further start from the amplitude dimension, associate the dual - mode current waveform signals, and realize the data fusion of the impulse current signal and the power frequency follow - current signal to more comprehensively display the characteristics of the dual - mode current waveform signals.

[0041] Specifically, in the amplitude dimension, dynamically associating the dual - mode current waveform signals includes: Extract the amplitude features of the impulse current signal and the power frequency follow - current signal respectively, and calculate the correlation between the amplitude features of the impulse current signal and the power frequency follow - current signal; Based on the correlation calculation results, construct the corresponding amplitude feature combinations, and quantify the relationship information of the amplitude feature combinations through a regression model; Construct a composite data set according to the amplitude features and the corresponding relationship information.

[0042] First, for the impulse current signal and power frequency follow - current signal after time alignment, starting from feature types such as the basic amplitude, dynamic amplitude change characteristics, and frequency - domain amplitude characteristics, a variety of amplitude characteristics are extracted respectively, specifically including peak value, effective value, average amplitude, amplitude change rate, harmonic amplitude, etc. And the extracted amplitude characteristics will have corresponding time - series information on the basis of being associated in the time dimension.

[0043] Then, the Pearson correlation coefficient is used to calculate the correlation between the amplitude characteristics of the impulse current signal and the power frequency follow - current signal. Furthermore, according to the calculation results of the correlation, the amplitude characteristics with strong correlation are combined. Specifically, by setting a correlation threshold, the amplitude - characteristic groups that meet the threshold conditions can be screened out. Each characteristic combination represents an association mode of the impulse current signal and the power frequency follow - current signal in the amplitude dimension.

[0044] Then, for each amplitude - characteristic combination, a corresponding model is constructed through linear regression or non - linear regression methods and solved by the least - squares method to quantitatively describe the relationship between the amplitude characteristics of the impulse current signal and the power frequency follow - current signal.

[0045] The various amplitude - characteristic parameters of the extracted impulse current and power frequency follow - current are integrated in chronological order to construct a composite data set. Each data sample in the composite data set contains all the amplitude characteristics of the impulse current signal and the power frequency follow - current signal within a certain time period and the related quantified relationship information.

[0046] In order to achieve efficient identification of abnormal operation of line arresters, an association diagnosis model is further established based on historical lightning strike events to realize the diagnosis of abnormal operation of line arresters according to the constructed composite data set.

[0047] Specifically, establishing the association diagnosis model includes: Retrieving historical lightning strike event information and annotating the historical lightning strike event information according to the historical fault information of the line arrester; Based on the impulse current signal and power frequency follow - current signal corresponding to the historical lightning strike event information, combined with the corresponding annotation, constructing a training data set; Constructing the association diagnosis model and training the association diagnosis model based on the training data set.

[0048] Retrieve historical lightning strike event information from multiple data sources such as the historical database of the power system, monitoring system logs, and fault record platforms. The historical lightning strike event information includes at least basic information such as the time, location, line number, and arrester model corresponding to each lightning strike event, as well as the corresponding impulse current signal and power frequency follow - current signal data. And in order to ensure the integrity and accuracy of the extracted data, preliminary screening and verification of the data are required during the retrieval process to eliminate obviously incorrect or missing data records.

[0049] Based on the historical fault information of the line lightning arrester, label the retrieved historical lightning strike event information to distinguish the operating states of the line lightning arrester after each lightning strike event, such as different fault types like normal operation, varistor deterioration, gap adhesion, internal moisture absorption, etc. For lightning strike events without faults, add normal labels, and for events with faults, accurate labels can be made according to the specific types of faults.

[0050] Then, combined with the above dynamic correlation methods in the time dimension and amplitude dimension, process the impulse current signal and power frequency follow - current signal corresponding to the historical lightning strike event information, and construct a training data set in combination with the corresponding labels.

[0051] Machine learning algorithms or deep learning algorithms can be selected to construct a correlation diagnosis model. Considering the time - series characteristics of the current signal, in this embodiment, a recurrent neural network algorithm is specifically selected to construct the correlation diagnosis model.

[0052] Then, train and learn the constructed correlation diagnosis model through the constructed training data set to learn the characteristic patterns and correlation rules of the bimodal current waveform signals under different fault types, so as to achieve precise diagnosis of the abnormal state of the line lightning arrester.

[0053] Then, input the composite data set into the trained correlation diagnosis model to output a conclusion on whether there is an abnormality and the specific type of abnormality.

[0054] To further optimize the operation and maintenance processing efficiency of the line lightning arrester, when the correlation diagnosis model outputs a conclusion that the line lightning arrester has an operation abnormality, based on the corresponding output abnormal type, establish a corresponding typical abnormal waveform according to the historical fault information, and combine it with the standard waveform of the line lightning arrester to construct a standard waveform library; Based on the standard waveform library, through the fuzzy logic algorithm, calculate the health state index of the line lightning arrester according to the currently correlated bimodal current waveform signal, and output the abnormal diagnosis result of the line lightning arrester.

[0055] When the correlation diagnosis model outputs that the line lightning arrester has an operation abnormality and the specific abnormal type, such as varistor deterioration, gap adhesion, etc., immediately start the abnormal waveform collection program. Extract the waveform data of all impulse current signals and power frequency follow - current signals related to this abnormal type from the historical fault information. Screen these waveform data, remove the invalid waveforms caused by data acquisition errors and signal interference, and retain the typical waveforms that can truly reflect the characteristics of this abnormal type. Then, combine the established typical abnormal waveform with the standard waveform of the current when the line lightning arrester is operating normally to construct a standard waveform library. And combined with historical fault information and expert experience, formulate fuzzy logic rules, and the formulated fuzzy logic rules cover the relationship between different characteristic combinations and the health state of the lightning arrester.

[0056] Extract various features from the current associated bimodal current waveform signal, such as amplitude, frequency, rise time, fall time, harmonic content, etc. Then, according to the feature ranges of various waveforms in the standard waveform library, define corresponding fuzzy sets for each feature, such as "low", "medium", "high", "short", "long", etc. Further, use the membership function to calculate the membership degrees of the current waveform features belonging to each fuzzy set.

[0057] Based on the calculation results of the membership degrees of each waveform feature belonging to each fuzzy set and the formulated fuzzy logic rules, through fuzzy logic operations, such as AND operation, OR operation, etc., determine the membership degrees of each health state fuzzy set. And through defuzzification methods such as weighted average method and maximum membership degree method, obtain the health state index of the line arrester according to the membership degrees of each health state fuzzy set.

[0058] Integrate the judged abnormal types and health state indices to identify the abnormal degree of the line arrester, and then judge the maintenance requirements of the line arrester, decide whether to perform simple maintenance, replace components, or replace the line arrester as a whole, optimize the operation and maintenance resource allocation, and ensure the safe and stable operation of the power system.

[0059] On the other hand, this embodiment also provides a line arrester operation monitoring device based on bimodal synchronous analysis, including: A lightning strike identification module, arranged at the discharge line end of the line arrester, for real-time monitoring of the discharge line current amplitude to identify lightning strike events and issue a collection trigger signal; A signal acquisition module, in response to the collection trigger signal of the lightning strike identification module, for collecting impulse current signals and power frequency follow current signals; An operation diagnosis module, connected to the signal acquisition module, for performing abnormal diagnosis on the line arrester according to the impulse current signal and the power frequency follow current signal.

[0060] In this embodiment, the lightning strike identification module at least includes a current sensor based on a Rogowski coil and a microcontroller that issues a synchronous trigger signal according to the data collected by the current sensor, which can accurately measure the current amplitude in the discharge line of the line arrester, and has the advantages of fast response speed, high accuracy, good linearity, etc., and can monitor the change of current in real time.

[0061] The signal acquisition module contains a sensor unit with two acquisition channels, which respectively collect impulse current signals and power frequency follow current signals.

[0062] For the sensor unit that collects impulse current signals, a high-frequency impulse current sensor based on a Rogowski coil can be used to capture the full waveform of the impulse current at the moment of lightning strike.

[0063] For the sensor unit that collects power frequency follow - current signals, a high - sensitivity follow - current sensor based on a zero - flux Hall element can be used to continuously monitor the power frequency follow - current after the line arrester operates.

[0064] Moreover, the signal acquisition module needs to be additionally provided with an anti - interference shielding cavity, which adopts a double - layer electromagnetic shielding structure. An independent isolation area is divided inside to install the impulse sensor and the follow - current sensor respectively, reducing the mutual inductance interference.

[0065] The operation diagnosis module includes: A signal processing unit, connected to the signal acquisition module, for pre - processing the impulse current signal and the power frequency follow - current signal; A data fusion unit, connected to the signal processing unit, for dynamically correlating the pre - processed impulse current signal and the power frequency follow - current signal; An abnormal diagnosis unit, connected to the data fusion unit, for performing abnormal diagnosis on the line arrester according to the dynamically correlated impulse current signal and power frequency follow - current signal.

[0066] To ensure the data processing efficiency and accuracy of the operation diagnosis module, multiple functional units are further set up to process the data in different stages of the abnormal diagnosis of the line arrester respectively.

[0067] The operation diagnosis module also includes: A model construction unit, 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.

[0068] Moreover, to optimize the recognition accuracy of the correlation diagnosis model, a model construction unit is further set up. Based on the establishment of the correlation diagnosis model based on historical lightning strike events, the correlation diagnosis model is continuously optimized according to each abnormal diagnosis result.

[0069] The signal processing unit, data fusion unit, abnormal diagnosis unit, and model construction unit of the above - mentioned operation diagnosis module are all microprocessors with corresponding data processing and analysis algorithms built - in.

[0070] The above - described embodiments are only a preferred solution of the present invention, and do not impose any form of limitation on the present invention. There are other variations and modifications without exceeding the technical solutions recorded in the claims.

Claims

1. A method for monitoring the operation of line arresters based on bimodal synchronous analysis, characterized in that, Including: Identifying lightning strike events and synchronously triggering the acquisition of dual-mode current waveform signals of impulse current signals and power-frequency follow current signals; Dynamically correlating the dual-mode current waveform signals based on the time dimension and the amplitude dimension; Establishing a correlation diagnosis model and outputting an abnormal diagnosis result of the line arrester according to the correlated dual-mode current waveform signals.

2. The line arrester operation monitoring method based on bimodal synchronous analysis according to claim 1, characterized in that, The dynamically correlating the dual-mode current waveform signals based on the time dimension and the amplitude dimension includes: Setting a reference waveform based on the impulse current signal and calculating the time offset of the power-frequency follow current signal from the reference waveform; Based on the time offset, finding the optimal path for aligning the power-frequency follow current signal through the dynamic time warping algorithm; Based on the found optimal path, stretching or compressing the power-frequency follow current signal in time to align the impulse current signal and the power-frequency follow current signal.

3. The line arrester operation monitoring method based on bimodal synchronous analysis according to claim 2, characterized in that The dynamically correlating the dual-mode current waveform signals based on the time dimension and the amplitude dimension further includes: Respectively extracting the amplitude features of the impulse current signal and the power-frequency follow current signal and calculating the correlation between the amplitude features of the impulse current signal and the power-frequency follow current signal; Constructing a corresponding amplitude feature combination based on the correlation calculation result and quantifying the relationship information of the amplitude feature combination through a regression model; Constructing a composite data set according to the amplitude features and the corresponding relationship information.

4. The method for monitoring the operation of a line lightning arrester based on bimodal synchronous analysis according to claim 1, wherein The establishing the correlation diagnosis model and outputting the abnormal diagnosis result of the line arrester according to the correlated dual-mode current waveform signals includes: Retrieving historical lightning strike event information and annotating the historical lightning strike event information according to the historical fault information of the line arrester; Based on the impulse current signal and the power-frequency follow current signal corresponding to the historical lightning strike event information and combining the corresponding annotation, constructing a training data set; Constructing a correlation diagnosis model and training the correlation diagnosis model based on the training data set.

5. The line arrester operation monitoring method based on bimodal synchronous analysis according to claim 1, characterized in that The establishing the correlation diagnosis model and outputting the abnormal diagnosis result of the line arrester according to the correlated dual-mode current waveform signals further includes: When the correlation diagnosis model outputs a conclusion that the line arrester has an abnormal operation, based on the corresponding output abnormal type, establishing a corresponding typical abnormal waveform according to the historical fault information and combining it with the standard waveform of the line arrester to construct a standard waveform library; Based on the standard waveform library, calculating the health state index of the line arrester according to the currently correlated dual-mode current waveform signals through a fuzzy logic algorithm; Combining the historical fault information and outputting the abnormal diagnosis result of the line arrester.

6. The method for monitoring the operation of a line lightning arrester based on bimodal synchronous analysis according to claim 1, wherein The identifying lightning strike events and synchronously triggering the acquisition of dual-mode current waveform signals of impulse current signals and power-frequency follow current signals includes: When the instantaneous change rate of the current in the discharge line of the line arrester exceeds a preset threshold, identifying the occurrence of a lightning strike event and synchronously triggering the acquisition of dual-mode current waveforms.

7. The line arrester operation monitoring method based on bimodal synchronous analysis according to claim 1, characterized in that Before dynamically correlating the dual-mode current waveform signals, the following is also performed: Performing dynamic gain adjustment and high-frequency filtering processing on the impulse current signal; Performing power-frequency notch filtering and baseline calibration processing on the power-frequency follow current signal.

8. A line lightning arrester operation monitoring device based on bimodal synchronous analysis, which is used to execute the line lightning arrester operation monitoring method based on bimodal synchronous analysis according to any one of claims 1 to 7, characterized in that, Including: A lightning strike identification module, arranged at the discharge line end of the line arrester, for real-time monitoring of the amplitude of the current in the discharge line to identify lightning strike events and issue a collection trigger signal; A signal acquisition module, in response to the collection trigger signal of the lightning strike identification module, for acquiring impulse current signals and power-frequency follow current signals; The operation diagnosis module, connected to the signal acquisition module, is used to perform abnormal diagnosis on the line lightning arrester according to the impulse current signal and power frequency follow current signal.

9. The line lightning arrester operation monitoring device based on dual-modal synchronous analysis according to claim 8, characterized in that The operation diagnosis module includes: A signal processing unit, connected to the signal acquisition module, for preprocessing the impulse current signal and power frequency follow current signal; A data fusion unit, connected to the signal processing unit, for dynamically correlating the preprocessed impulse current signal and power frequency follow current signal; An abnormal diagnosis unit, connected to the data fusion unit, for performing abnormal diagnosis on the line lightning arrester according to the dynamically correlated impulse current signal and power frequency follow current signal.

10. The line arrester operation monitoring device based on dual-modal synchronous analysis according to claim 8, characterized in that, The operation diagnosis module further includes: A model construction unit, for establishing a correlation diagnosis model based on historical lightning strike events and optimizing and adjusting the correlation diagnosis model in combination with the abnormal diagnosis results of the abnormal diagnosis unit.

Images