A method and device for on-line monitoring of a gas insulated switchgear

Abstract

The application discloses a kind of gas insulation cabinet online monitoring method and device, and the application relates to the technical field of insulation cabinet, it includes: based on gas insulation cabinet offline test construction discharge signal category library;In key area optimization arrangement multi-channel perception unit acquisition local discharge signal;Multi-channel signal is carried out time-sharing superposition, and frequency domain amplitude spectrum is obtained by fast fourier transform, and then extract insulation cabinet voltage frequency domain envelope and insulation cabinet voltage energy distribution;Through dynamic adjustment frequency weight calculation and category library energy distribution difference value, and utilize dynamic time regularization method to calculate frequency domain envelope similarity value, combine both to obtain discharge signal category matching value, identify discharge type.Based on discharge type and medium characteristic, energy gradient estimation method is used to realize discharge source positioning;Collect the time series characteristics of positioning point to carry out trend analysis, obtain local discharge signal evolution value, and accordingly divide discharge risk grade, complete online monitoring and early warning.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of insulation cabinets, in particular to an online monitoring method and device for a gas insulation cabinet. BACKGROUND

[0002] The gas insulated metal enclosed switchgear has become the core equipment in the modern high-voltage and super-high-voltage power transmission and transformation system due to its compact structure, high reliability, strong environmental adaptability and other significant advantages, and is widely used in power supply hubs of substations, urban power grids and large industrial and mining enterprises. The GIS device is internally filled with SF6 gas with excellent insulation and arc extinguishing performance, and its long-term operation state is directly related to the safety and stability of the power grid. Partial discharge is one of the most critical signs of early insulation deterioration in GIS, and effective online monitoring and diagnosis of partial discharge is a key technical means for assessing the insulation state of the equipment, predicting potential faults, achieving condition-based maintenance and ensuring the safe operation of the power grid.

[0003] The traditional monitoring method has insufficient recognition accuracy for the type of discharge, relies only on simple threshold comparison of a single signal feature (such as discharge amplitude, frequency), lacks analysis of discharge signals of different physical mechanisms (such as corona discharge, surface discharge, internal discharge), and results in high misjudgment rate in a complex noise environment. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides an online monitoring method and device for a gas insulation cabinet to solve the problems in the background art.

[0005] To achieve the above purpose, the present application is implemented by the following technical scheme: an online monitoring method for a gas insulation cabinet, comprising the following steps:

[0006] Step S1: a plurality of partial discharge sensing channels are embedded in the key electric field non-uniform region in the gas insulation cabinet in a reverse staggered manner, discharge signals of the gas insulation cabinet are collected through the partial discharge sensing channels, and a plurality of local voltage signal data of the gas insulation cabinet are obtained;

[0007] Step S2: the plurality of local voltage signal data are time-division superimposed to obtain an insulation cabinet superimposed voltage signal, the insulation cabinet superimposed voltage signal is subjected to fast Fourier transform to obtain an insulation cabinet voltage frequency domain amplitude spectrum, the insulation cabinet voltage frequency domain amplitude spectrum is subjected to smoothing processing to obtain an insulation cabinet voltage frequency domain envelope, and the insulation cabinet voltage frequency domain amplitude spectrum is subjected to energy distribution analysis to obtain an insulation cabinet voltage energy distribution;

[0008] Step S3: by offline testing of gas insulated switchgear and constructing discharge signal category library, the discharge signal category library includes: discharge category signal energy distribution, discharge category signal frequency domain envelope; by dynamically adjusting the frequency weight in the insulation cabinet voltage energy distribution, calculating the reconstruction difference between the insulation cabinet voltage energy distribution and the discharge category signal energy distribution, obtaining the insulation cabinet energy distribution difference value; by dynamic time warping method, calculating the cumulative distance of the insulation cabinet voltage frequency domain envelope and the discharge category signal frequency domain envelope, obtaining the insulation cabinet frequency domain similarity value;

[0009] Step S4: by combining the insulation cabinet energy distribution difference value and the insulation cabinet frequency domain similarity value, the discharge signal category matching value is calculated, and the discharge signal category is obtained according to the discharge signal category matching value;

[0010] Step S5: the internal medium absorption coefficient of gas insulated switchgear is collected, based on the discharge signal category, the internal medium absorption coefficient of gas insulated switchgear, the local voltage signal data is analyzed by energy gradient estimation method, and the discharge source position is obtained;

[0011] Step S6: collect the insulation cabinet voltage frequency domain envelope and the insulation cabinet voltage energy distribution of the discharge source position within a preset time, and perform trend change analysis to obtain the local discharge signal evolution value; by the local discharge signal evolution value, the discharge risk level is divided, and the online monitoring of the gas insulated switchgear is realized.

[0012] Preferably, the time-sharing superposition of the plurality of local voltage signal data to obtain the superposed voltage signal of the insulation cabinet comprises the following specific steps:

[0013] The energy contribution value of the local discharge data of each channel is calculated:

[0014]

[0015] Wherein, is the energy contribution value of the local voltage signal data of the ith channel, n is the index of the nth sampling point, and N is the total sampling number in the time window;

[0016] In the time window, the local voltage signal data is time-sharing superposed to obtain the superposed voltage signal of the insulation cabinet:

[0017]

[0018] Wherein, represents the superposed voltage signal of the insulation cabinet, represents the total number of channels, i represents the index of the local voltage signal data of the ith channel, is the energy contribution value of the local voltage signal data of the ith channel, represents the local voltage signal data of the i-th channel, and n represents the index of the n-th sampling point.

[0019] Preferably, the obtaining the insulation cabinet voltage frequency domain envelope by performing fast Fourier transform on the superimposed voltage signal of the insulation cabinet, and performing smoothing processing on the insulation cabinet voltage frequency domain amplitude spectrum comprises the following steps:

[0020] performing fast Fourier transform on the superimposed discharge signal to obtain a frequency domain complex sequence :

[0021]

[0022] wherein, is a complex amplitude of the k-th frequency point, represents the superimposed voltage signal of the insulation cabinet, j is a complex unit, k is the frequency point index of the frequency domain, and N is the total number of samples in the time window;

[0023] taking the modulus value of the frequency domain complex sequence to obtain the insulation cabinet voltage frequency domain amplitude spectrum |;

[0024] performing curve fitting on the insulation cabinet voltage frequency domain amplitude spectrum to obtain the insulation cabinet voltage frequency domain envelope:

[0025]

[0026] wherein, is the envelope amplitude of the insulation cabinet voltage frequency domain envelope at the frequency , is a complex amplitude of the k-th frequency point, is a Hilbert transform operator, is the frequency corresponding to the k-th frequency point.

[0027] Preferably, the obtaining the insulation cabinet voltage energy distribution by performing energy distribution analysis on the insulation cabinet voltage frequency domain amplitude spectrum comprises the following steps:

[0028] performing energy distribution analysis on the insulation cabinet voltage frequency domain amplitude spectrum to obtain the insulation cabinet voltage energy distribution:

[0029]

[0030] wherein, is the energy proportion of the insulation cabinet voltage energy distribution at the k-th frequency point, k is the frequency point index of the frequency domain, and N is the total number of samples in the time window, is a complex amplitude of the k-th frequency point.

[0031] Preferably, the step of dynamically adjusting the frequency weights in the voltage energy distribution of the insulating cabinet and calculating the reconstructed difference between the voltage energy distribution of the insulating cabinet and the energy distribution of the discharge category signal to obtain the energy distribution difference value of the insulating cabinet includes the following steps:

[0032] Calculate the average value of the energy distribution of discharge category signals in the discharge signal category library:

[0033]

[0034] in, The average energy proportion of the discharge category signal energy distribution at the k-th frequency point. This represents the total number of signal energy distributions for each discharge category. For the first Index of signal energy distribution for each discharge category Indicates the first The proportion of energy of each discharge category signal at the k-th frequency point;

[0035] The weight of the energy proportion at each frequency point of the voltage energy distribution of the insulating cabinet is calculated by using the average value of the energy distribution of the discharge category signal.

[0036]

[0037] in, The energy proportion weight of the voltage energy distribution of the insulation cabinet at the k-th frequency point. This represents the proportion of voltage energy distributed at the k-th frequency point in the insulation cabinet. The average energy proportion of the discharge category signal energy distribution at the k-th frequency point. For adjustment coefficients, >0, used to adjust the effect of differences in voltage energy distribution and discharge category signal energy distribution within the insulation cabinet. For constant terms, >1;

[0038] The reconstructed difference between the voltage energy distribution of the insulating cabinet and the energy distribution of the discharge category signal is calculated to obtain the energy distribution difference value of the insulating cabinet:

[0039]

[0040] in, For the voltage energy distribution of the insulating cabinet and the first The energy distribution difference of the insulation cabinet for each discharge category signal energy distribution, where k is the frequency index in the frequency domain and N is the total number of samples within the time window. The energy proportion weight of the voltage energy distribution of the insulation cabinet at the k-th frequency point. This represents the proportion of voltage energy distributed at the k-th frequency point in the insulation cabinet. represents the kth energy proportion of the energy distribution of the kth

[0041] Preferably, the calculation of the cumulative distance of the voltage frequency envelope of the insulating cabinet and the discharge category signal frequency envelope by the dynamic time warping method to obtain the insulating cabinet frequency envelope similarity value comprises the following specific steps:

[0042] The cumulative distance of the voltage frequency envelope of the insulating cabinet and the discharge category signal frequency envelope is calculated by the dynamic time warping method to obtain the insulating cabinet frequency envelope similarity value:

[0043]

[0044] wherein, is the insulating cabinet frequency envelope similarity value of the voltage frequency envelope of the insulating cabinet and the kth discharge category signal frequency envelope, is the envelope amplitude of the voltage frequency envelope of the insulating cabinet at frequency is the envelope amplitude of the kth discharge category signal frequency envelope at frequency is the dynamic time warping method function, is an adjustment coefficient, used to adjust the influence of the difference between the voltage frequency envelope of the insulating cabinet and the discharge category signal frequency envelope.

[0045] Preferably, the calculation of the discharge signal category matching value by combining the insulating cabinet energy distribution difference value and the insulating cabinet frequency envelope similarity value comprises the following specific steps:

[0046] The discharge signal category matching value is calculated by combining the insulating cabinet energy distribution difference value and the insulating cabinet frequency envelope similarity value:

[0047]

[0048] wherein, is the discharge signal category matching value of the partial discharge signal and the kth discharge signal category, is the insulating cabinet energy distribution difference value of the voltage energy distribution of the insulating cabinet and the kth discharge category signal energy distribution, is the insulating cabinet frequency envelope similarity value of the voltage frequency envelope of the insulating cabinet and the kth discharge category signal frequency envelope.

[0049] Preferably, the positioning analysis of the partial voltage signal data by the energy gradient estimation method to obtain the discharge source position comprises the following specific steps: ​

[0050] Objective function of energy gradient estimation method:

[0051]

[0052] where F is the objective function of energy gradient estimation method, is the response amplitude of the local voltage signal data of the i-th channel, is the theoretical amplitude calculated based on the preset discharge source position, is the actual signal arrival time difference between the i-th channel and the reference channel, is the theoretical time difference between the i-th channel and the reference channel calculated based on the preset discharge source position, is the energy contribution of the local voltage signal data of the i-th channel, is the theoretical energy contribution value of the i-th channel calculated based on the preset discharge source position, i is the index of the i-th channel, denotes the total number of channels;

[0053] Adjust the coordinates of the preset discharge source candidate point by gradient descent method until the objective function of energy gradient estimation method converges to a minimum value, at which time is the discharge source position.

[0054] Preferably, the insulation cabinet voltage frequency domain envelope and insulation cabinet voltage energy distribution of the discharge source position within the preset time are collected and trend change analysis is performed to obtain the local discharge signal evolution value, including the following specific steps:

[0055] Calculate the difference of the insulation cabinet voltage frequency domain envelope and insulation cabinet voltage energy distribution of each adjacent time window:

[0056]

[0057] wherein, denotes the local discharge signal difference value of the ms-th time window, denotes the envelope amplitude of the insulation cabinet voltage frequency domain envelope of the m-th time window at frequency f, denotes the envelope amplitude of the insulation cabinet voltage frequency domain envelope of the m-th time window at frequency f, denotes the energy proportion of the insulation cabinet voltage energy distribution of the m-th time window at the k-th frequency point, denotes the energy proportion of the insulation cabinet voltage energy distribution of the m-th time window at the k-th frequency point, denotes the energy proportion of the insulation cabinet voltage energy distribution of the m-th time window at the k-th frequency point, denotes the energy proportion of the insulation cabinet voltage energy distribution of the m-th time window at the k-th frequency point, denotes the energy proportion of the insulation cabinet voltage energy distribution of the m-th time window at the k-th frequency point, denotes the energy proportion of the insulation cabinet voltage energy distribution of the m-th time window at the k-th frequency point, denotes the energy proportion of the insulation cabinet voltage energy distribution of the m-th time window at the k-th frequency point, denotes the energy proportion of the insulation cabinet voltage energy distribution of the m-th time window at the k-th frequency point, denotes the energy proportion of the insulation cabinet voltage energy distribution of the m-th time window at the k-th frequency point, Index of a time window, This is the envelope amplitude difference adjustment coefficient, used to adjust the effect of envelope difference on the difference value of partial discharge signal. This is the energy ratio difference adjustment coefficient, used to adjust the effect of the energy ratio difference adjustment coefficient on the difference value of the partial discharge signal. To prevent division by zero, the value is taken as... ;

[0058] The evolution value of the partial discharge signal within a preset time period is calculated based on the difference value of the partial discharge signal.

[0059]

[0060] Where XB is the evolution value of the partial discharge signal, and MS is the total number of time windows. This represents the difference in partial discharge signal value within the ms-th time window. This indicates the length of a unit time window.

[0061] Preferably, the step of classifying the discharge risk level based on the partial discharge signal evolution value to achieve online monitoring of the gas-insulated cabinet includes the following specific steps:

[0062] The discharge risk level is determined by the evolution value of the partial discharge signal, enabling online monitoring of the gas-insulated cabinet and setting multiple risk levels: XB If the risk level is 1 (normal), routine inspection is required. <XB< If so, the risk level is Level 2 (Caution), and the monitoring frequency needs to be increased. <XB< If so, the risk level is Level 3 alert, requiring dedicated monitoring; XB> If so, the risk level is Level 4 (emergency), requiring immediate repair.

[0063] An online monitoring device for gas-insulated cabinets, comprising:

[0064] Signal acquisition module: Several partial discharge sensing channels are embedded in the key non-uniform electric field region inside the gas-insulated cabinet in a reverse interleaved manner. The discharge signal of the gas-insulated cabinet is acquired through the partial discharge sensing channels to obtain several local voltage signal data of the gas-insulated cabinet.

[0065] The insulation cabinet voltage energy distribution generation module: the local voltage signal data are superposed in time to obtain an insulation cabinet superposed voltage signal, the insulation cabinet superposed voltage signal is subjected to fast Fourier transform to obtain an insulation cabinet voltage frequency domain amplitude spectrum, the insulation cabinet voltage frequency domain amplitude spectrum is subjected to smoothing processing to obtain an insulation cabinet voltage frequency domain envelope, and the insulation cabinet voltage frequency domain amplitude spectrum is subjected to energy distribution analysis to obtain an insulation cabinet voltage energy distribution;

[0066] The insulation cabinet frequency domain similarity value generation module: the gas insulation cabinet is subjected to offline testing, and a discharge signal category library is constructed, the discharge signal category library including: discharge category signal energy distribution and discharge category signal frequency domain envelope; the frequency weight in the insulation cabinet voltage energy distribution is dynamically adjusted, the reconstruction difference between the insulation cabinet voltage energy distribution and the discharge category signal energy distribution is calculated, and an insulation cabinet energy distribution difference value is obtained; the cumulative distance between the insulation cabinet voltage frequency domain envelope and the discharge category signal frequency domain envelope is calculated by dynamic time warping method, and an insulation cabinet frequency domain similarity value is obtained;

[0067] The discharge signal category generation module: a discharge signal category matching value is calculated by combining the insulation cabinet energy distribution difference value and the insulation cabinet frequency domain similarity value, and a discharge signal category is obtained according to the discharge signal category matching value;

[0068] The discharge source position judgment module: the internal medium absorption coefficient of the gas insulation cabinet is collected, the discharge signal category, the internal medium absorption coefficient of the gas insulation cabinet, and the local voltage signal data are subjected to positioning analysis by energy gradient estimation method to obtain a discharge source position;

[0069] The monitoring module: the insulation cabinet voltage frequency domain envelope and the insulation cabinet voltage energy distribution of the discharge source position in a preset time are collected, and trend change analysis is performed to obtain a local discharge signal evolution value; the discharge risk level is divided by the local discharge signal evolution value, and online monitoring of the gas insulation cabinet is realized.

[0070] Beneficial effects

[0071] The application provides a gas insulation cabinet online monitoring method, relates to power distribution switch control equipment manufacturing technology, and has the following beneficial effects:

[0072] (1) The significance of dynamically adjusting the frequency weight and calculating the energy distribution difference value of the insulating cabinet. By dynamically adjusting the frequency weight, this step can adaptively highlight the frequency points with significant feature differences between the voltage signal and the discharge category library, making the energy distribution difference value of the insulating cabinet more accurately reflect the deviation degree of the real-time signal from the standard category. This process enhances the sensitivity to the characteristics of the discharge signal, effectively identifies the uniqueness of different types of discharge (such as corona, surface discharge, etc.) in energy distribution, provides key quantitative basis for subsequent discharge category matching, and improves the accuracy and reliability of fault identification.

[0073] (2) Calculate the cumulative distance between the voltage frequency envelope of the insulating cabinet and the frequency envelope of the discharge category signal using dynamic time warping method, and get the frequency domain similarity value of the insulating cabinet. Its core significance lies in solving the nonlinear alignment problem of frequency domain feature sequence, so as to accurately capture the shape similarity of different discharge signals. Due to the influence of factors such as transmission path and medium attenuation in gas insulated switchgear, the frequency envelope of the discharge signal may have a shift in time axis or frequency axis (such as phase difference, frequency spread, etc.), and traditional Euclidean distance cannot effectively handle such asynchronous features. By using dynamic programming algorithm to find the optimal time warping path, the two frequency envelope sequences can be locally stretched or compressed in time or frequency dimension, making the cumulative distance calculation more consistent with the actual signal difference. The obtained frequency domain similarity value of the insulating cabinet can quantitatively represent the shape matching degree between the real-time signal and the standard discharge category, and then assist in judging the type of discharge (such as corona discharge, surface discharge, etc.), providing key basis for accurate classification of discharge signals, and also identifying the evolution law of early fault characteristics through the trend of similarity value in long-term monitoring of equipment.

[0074] (3) The significance of collecting time series data and analyzing to obtain the evolution value of partial discharge signal. By collecting frequency envelope and energy distribution data within a predetermined time and analyzing the trend, the evolution law of partial discharge signal can be obtained, such as the change rate and stability of characteristic parameters. The evolution value of partial discharge signal can dynamically reflect the development trend of discharge, such as the gradual change from normal to warning state, providing quantitative support in time dimension for risk level division. This mechanism can provide early warning for potential faults, help maintenance personnel to develop differentiated maintenance strategies (such as routine inspection and emergency repair) according to risk level, realize the forward-looking management of gas insulated switchgear state, and reduce the risk of equipment failure. BRIEF DESCRIPTION OF DRAWINGS

[0075] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creating labor.

[0076] Fig. 1 A flow chart of the steps of the gas insulated cabinet online monitoring method is provided for the present application.

[0077] Fig. 2 A hierarchical diagram of the steps of the gas insulated cabinet online monitoring method is provided for the present application. DETAILED DESCRIPTION

[0078] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0079] Please refer to Figs. 1-2 The present application provides a technical solution: a gas insulated cabinet online monitoring method.

[0080] Step S1: embed a plurality of partial discharge sensing channels in the key electric field non-uniform regions in the gas insulated cabinet in a reverse staggered manner, collect the discharge signals of the gas insulated cabinet through the partial discharge sensing channels, and obtain a plurality of local voltage signal data of the gas insulated cabinet.

[0081] In the gas insulated cabinet (GIS) shell, the key regions where the electric field distribution is uneven and partial discharge is prone to occur are identified, mainly including: bus connection joint, high-voltage cable terminal interface, circuit breaker fracture region, and other weak insulation or complex structure parts.

[0082] Deploy multi-channel sensing units: independently install a partial discharge sensing channel in each selected region, and each channel includes: an ultra-high frequency (UHF) voltage detection module: used to capture electromagnetic wave signals generated by partial discharge (frequency band is usually 300MHz-3GHz). A high-sensitivity ultrasonic receiving module: used to detect the mechanical vibration sound wave signals accompanied by discharge (frequency band is usually 20kHz-200kHz).

[0083] Optimize channel space arrangement: arrange the sensing channels in a reverse staggered manner: so that the signals of the same discharge source can be received by multiple channels at different positions (for example, signal source A is detected by channels 1, 3, and 5, and signal source B is detected by channels 2, 4, and 6). Considering the interference of the internal structure of GIS on the signal (such as electromagnetic wave reflection, metal shielding attenuation), adjust the sensor angle and spacing to avoid signal superposition distortion.

[0084] Establish a global coverage network: through the heterogeneous layout of multiple channels (i.e., different positions use different sensor combinations), ensure that there is no blind area coverage in the internal space of GIS.

[0085] Verification of the sensing network: The consistency of the response of each channel is tested by simulating the discharge source to ensure that the signal can be effectively captured by at least three channels. The discharge signal of the gas insulated switchgear is collected in real time to obtain the local voltage signal data.

[0086] Step S2: The local voltage signal data is superimposed in time to obtain the superimposed voltage signal of the switchgear. The voltage frequency domain amplitude spectrum of the switchgear is obtained by performing fast Fourier transform on the superimposed voltage signal of the switchgear. The voltage frequency domain envelope of the switchgear is obtained by performing smoothing processing on the voltage frequency domain amplitude spectrum of the switchgear. The voltage energy distribution of the switchgear is obtained by performing energy distribution analysis on the voltage frequency domain amplitude spectrum of the switchgear.

[0087] In a time window, the local voltage signal data has a sampling frequency of (e.g. 3 GHz). The collected partial discharge signal is an analog voltage signal, which is converted into a discrete sequence after being sampled by an analog-to-digital converter (ADC). The local voltage signal data is V[n], n=1,2,...,N, and N is the total number of samples in the time window.

[0088] The energy contribution value of the partial discharge data of each channel is calculated as follows:

[0089]

[0090] wherein, is the energy contribution value of the local voltage signal data of the i-th channel, n is the index of the n-th sampling point, and N is the total number of samples in the time window.

[0091] In the time window, the local voltage signal data is superimposed in time to obtain the superimposed voltage signal of the switchgear:

[0092]

[0093] wherein, represents the superimposed voltage signal of the switchgear, represents the total number of channels, i represents the index of the local voltage signal data of the i-th channel, is the energy contribution value of the local voltage signal data of the i-th channel, represents the local voltage signal data of the i-th channel, and n represents the index of the n-th sampling point.

[0094] The superimposed discharge signal is subjected to fast Fourier transform to obtain a frequency domain complex sequence :

[0095]

[0096] wherein, is the complex amplitude of the k-th frequency point, represents the superimposed voltage signal of the insulated cabinet, j is a complex unit, k is the frequency point index of the frequency domain, and N is the total number of samples in the time window.

[0097] Taking the modulus value of the frequency domain complex sequence, the frequency domain amplitude spectrum of the insulated cabinet voltage is obtained .

[0098] Curve fitting is performed on the frequency domain amplitude spectrum of the insulated cabinet voltage to obtain the frequency domain envelope of the insulated cabinet voltage:

[0099]

[0100] wherein, is the envelope amplitude of the frequency domain envelope of the insulated cabinet voltage at the frequency , is the complex amplitude of the kth frequency point, is the Hilbert transform operator, is the frequency corresponding to the kth frequency point.

[0101] Through energy distribution analysis on the frequency domain amplitude spectrum of the insulated cabinet voltage, the energy distribution of the insulated cabinet voltage is obtained:

[0102]

[0103] wherein, is the energy proportion of the energy distribution of the insulated cabinet voltage at the kth frequency point, k is the frequency point index of the frequency domain, and N is the total number of samples in the time window, is the complex amplitude of the kth frequency point.

[0104] Step S3: Through offline testing of the gas insulated cabinet and constructing a discharge signal category library, the discharge signal category library includes: discharge category signal energy distribution, discharge category signal frequency domain envelope; by dynamically adjusting the frequency weight in the energy distribution of the insulated cabinet voltage, the reconstruction difference between the energy distribution of the insulated cabinet voltage and the discharge category signal energy distribution is calculated, to obtain the energy distribution difference value of the insulated cabinet; by dynamic time warping method, the cumulative distance of the frequency domain envelope of the insulated cabinet voltage and the frequency domain envelope of the discharge category signal is calculated, to obtain the frequency domain similarity value of the insulated cabinet.

[0105] The gas insulated switchgear is tested offline by an offline test method, and a discharge signal category library is constructed. An ultrasonic sensor, a UHF sensor and other devices are used to collect discharge signals of the gas insulated switchgear in an offline state. Different working conditions and typical discharge types need to be covered during collection to ensure the integrity and representativeness of the signals, and the collection environment parameters and the equipment operation state are recorded. Secondly, the collected original signals are preprocessed, and environmental interference and invalid data are removed through band-pass filtering and denoising to improve the signal quality and lay a foundation for subsequent analysis. Then, feature extraction is performed. On the one hand, the normalized energy distribution of the discharge signal is calculated, that is, the preprocessed signal is converted to the frequency domain through Fourier transform, the proportion of the energy of each frequency point to the total energy is calculated, and the distribution characteristics of the energy in the frequency domain are reflected. On the other hand, the frequency domain envelope is extracted, the amplitude spectrum in the frequency domain is smoothed, the contour characteristics of the spectrum are obtained, and the frequency component distribution trend of the signal is represented. Then, the extracted features are classified according to the types of the discharge signals (such as corona discharge, surface discharge, internal discharge and the like), the energy distribution and the frequency domain envelope data of the same type of discharge signals are integrated, and a discharge signal category library is constructed. The feature parameter range and the typical feature curve of each category in the library need to be clearly marked to facilitate subsequent matching and comparison. Finally, the constructed category library is verified and optimized. The test signals of known discharge types are input into the library for matching test, the energy distribution difference value and the frequency domain similarity value of the insulated cabinet (such as the dynamic time warping algorithm) are calculated, the recognition accuracy of the category library is evaluated, the categories with large recognition errors are rechecked for feature extraction process or supplemented with sample data until the category library meets the actual application requirements. The constructed category library can be used for online monitoring and fault type identification of the discharge signals of the gas insulated switchgear. The discharge type is quickly determined by real-time signal collection and comparison with the features in the library.

[0106] The reconstruction difference between the energy distribution of the insulated cabinet voltage and the energy distribution of the discharge category signal is calculated by dynamically adjusting the frequency weight, and the energy distribution difference value of the insulated cabinet is obtained.

[0107] The average value of the energy distribution of the discharge category signal in the discharge category signal library is calculated:

[0108]

[0109] wherein, is the average value of the energy proportion of the energy distribution of the discharge category signal at the kth frequency point, is the total number of the energy distribution of the discharge category signal, is the index of the mth discharge category signal energy distribution, is the index of the mth discharge category signal energy distribution, represents the energy proportion of the mth discharge category signal energy distribution at the kth frequency point.

[0110] ​The weight of the energy proportion of each frequency point of the energy distribution of the insulation cabinet voltage is calculated by the average value of the energy distribution of the discharge category signal:

[0111]

[0112] wherein, is the energy proportion weight of the energy distribution of the insulation cabinet voltage at the kth frequency point, is the energy proportion of the energy distribution of the insulation cabinet voltage at the kth frequency point, is the average value of the energy proportion of the energy distribution of the discharge category signal at the kth frequency point, is an adjustment coefficient, >0, used to adjust the influence of the difference between the energy distribution of the insulation cabinet voltage and the energy distribution of the discharge category signal, is a constant term, >1.

[0113] It should be noted that by dynamically adjusting the frequency weight in the energy distribution of the insulation cabinet voltage, the sensitivity to key frequency point features is enhanced, so that the difference features of the real-time discharge signal and the discharge signal category library are more accurately captured. Different types of discharges (such as corona discharge, surface discharge, internal discharge, etc.) have unique characteristics in the frequency energy distribution, and the energy distribution may be shifted due to the influence of the internal structure of GIS (such as metal shielding, electromagnetic wave reflection) and the medium characteristics (such as SF6 gas attenuation) during signal propagation. Dynamic adjustment of the frequency weight can give higher weight to the frequency points with significant differences between the real-time signal and the discharge signal category library energy distribution, highlighting the role of this part of the feature in the matching process.

[0114] The reconstruction difference between the energy distribution of the insulation cabinet voltage and the energy distribution of the discharge category signal is calculated to obtain the insulation cabinet energy distribution difference value:

[0115]

[0116] wherein, is the insulation cabinet energy distribution difference value between the energy distribution of the insulation cabinet voltage and the energy distribution of the kth discharge category signal, k is the frequency point index of the frequency domain, and N is the total number of samples in the time window, is the energy proportion weight of the energy distribution of the insulation cabinet voltage at the kth frequency point, is the energy proportion of the energy distribution of the insulation cabinet voltage at the kth frequency point, is the energy proportion of the energy distribution of the kth discharge category signal at the kth frequency point.

[0117] ​​It should be noted that the energy distribution difference value of the insulation cabinet can quantitatively reflect the degree of deviation between the real-time acquired voltage energy distribution of the insulation cabinet and the energy distribution of the pre-built discharge category signal. By dynamically adjusting the frequency weight, this process can adaptively highlight the frequency points with significant differences, effectively capturing the unique characteristics of different discharge types (such as tip discharge, air gap breakdown, etc.) in frequency domain energy distribution. The smaller the difference value, the higher the matching degree between the real-time signal and the energy characteristics of a certain type of discharge, thus providing key quantitative basis for accurate identification of discharge types. At the same time, in equipment operation status monitoring, the severity of discharge development can be judged by the changing trend of the difference value.

[0118] The cumulative distance between the frequency domain envelope of the voltage and the frequency domain envelope of the discharge category signal of the insulating cabinet is calculated using the dynamic time warping method, and the frequency domain similarity value of the insulating cabinet is obtained.

[0119]

[0120] in, For the voltage frequency domain envelope of the insulating cabinet and the first Frequency domain similarity values ​​of the insulation cabinet for each discharge category signal frequency domain envelope The voltage frequency domain envelope of the insulating cabinet at frequency The envelope amplitude, For the first The frequency domain envelope of each discharge category signal is in frequency. The envelope amplitude, DTW() is the dynamic time warping function. This is an adjustment coefficient used to adjust the effect of the difference between the frequency domain envelope of the insulation cabinet voltage and the frequency domain envelope of the discharge category signal.

[0121] It should be noted that calculating the cumulative distance between the frequency domain envelope of the insulating cabinet voltage and the frequency domain envelope of the discharge category signal using the dynamic time warping method, and obtaining the frequency domain similarity value of the insulating cabinet, is significant in that it effectively solves the problem of time or frequency axis offset (such as phase difference, frequency broadening) caused by factors such as propagation path and medium attenuation in the frequency domain characteristic sequence. Traditional distance calculation methods are difficult to handle such asynchronous characteristics, while the dynamic time warping method can find the optimal time curvature path through dynamic programming algorithms, allowing the two sequences to be locally stretched or compressed in the time or frequency dimensions, making the cumulative distance calculation more in line with the actual differences in the signals. The obtained similarity value can be quantified to represent the degree of matching between the real-time signal and the frequency domain shape of the standard discharge category, assisting in the accurate judgment of the discharge type (such as corona discharge, surface discharge, etc.). At the same time, in long-term monitoring, the evolution law of early fault characteristics can be identified through the trend of similarity value changes, providing a key basis for the accurate assessment of the discharge state of gas-insulated cabinets.

[0122] Step S4: Calculate the discharge signal category matching value by combining the insulating cabinet energy distribution difference value and the insulating cabinet frequency domain similarity value, and obtain the discharge signal category according to the discharge signal category matching value.

[0123] Calculate the discharge signal category matching value by combining the insulating cabinet energy distribution difference value and the insulating cabinet frequency domain similarity value:

[0124]

[0125] Wherein, is the insulating cabinet energy distribution difference value of the local discharge signal and the first discharge signal category, is the insulating cabinet energy distribution difference value of the insulating cabinet voltage energy distribution and the first discharge category signal energy distribution, is the insulating cabinet frequency domain similarity value of the insulating cabinet voltage frequency domain envelope and the first discharge category signal frequency domain envelope.

[0126] It should be noted that the insulating cabinet energy distribution difference value and the discharge signal category matching value are inversely proportional, and the insulating cabinet frequency domain similarity value and the discharge signal category matching value are directly proportional. Specifically, the insulating cabinet energy distribution difference value reflects the deviation degree of the real-time insulating cabinet voltage energy distribution from the standard energy distribution in the discharge category library. The smaller the value, the closer the energy characteristics of the real-time signal to a certain type of discharge, and the easier to match to the corresponding discharge category. Therefore, the contribution to the discharge signal category matching value is negative, that is, the smaller the difference value, the larger the matching value. The insulating cabinet frequency domain similarity value measures the similarity degree of the real-time signal frequency domain envelope and the standard frequency domain envelope through dynamic time warping method. The larger the value, the higher the frequency domain form matching degree of the two, and the more likely to belong to a certain type of discharge. The contribution to the discharge signal category matching value is positive, that is, the larger the similarity value, the larger the matching value. Through the combination of the positive and inverse proportional relationship of the two, they jointly act on the accurate determination of the discharge signal category.

[0127] By calculating the discharge signal category matching value of the discharge signal and all discharge signal categories in the discharge category library, the discharge signal category corresponding to the maximum value in the discharge signal category matching value (such as corona discharge, surface discharge, internal discharge, etc.) is selected as the discharge signal category of the local discharge signal. For example: the discharge signal category matching value corresponding to the local discharge signal and the "corona discharge" in the discharge category library is the largest, and the discharge signal category of the local discharge signal is "corona discharge".

[0128] Step S5: Collect the internal medium absorption coefficient of the gas insulated switchgear, based on the discharge signal category, the internal medium absorption coefficient of the gas insulated switchgear, and the local voltage signal data are analyzed by energy gradient estimation method to obtain the discharge source position.

[0129] Extract the response amplitude of the local voltage signal data of each channel , i = 1, 2, 3,... , , which represents the response amplitude of the local voltage signal data of the i-th channel, , which represents the total number of channels.

[0130] Core input parameters: response amplitude: the response amplitude of the local voltage signal data received by the i-th channel (unit: mV), reflecting the distance between the signal source and the channel and the propagation attenuation. Response time delay: is the signal arrival time difference of the local voltage signal data of the i-th channel and the reference channel, caused by the reflection of the GIS internal metal shell and the difference in path length.

[0131] Reverse iterative energy gradient estimation method: initial assumption: preset discharge source candidate point in the three-dimensional space of the gas insulated switchgear .

[0132] Forward calculation: calculate the theoretical distance of the candidate point to the i-th channel , the theoretical time difference , and the theoretical energy contribution value of the i-th channel. .

[0133] Structure parameters: input other insulating switchgear shell three-dimensional coordinates, internal metal component positions, internal medium absorption coefficient (gas medium absorption coefficient), electromagnetic wave propagation speed , = , is the theoretical signal arrival time difference of the local voltage signal data of the i-th channel and the reference channel based on the preset discharge source position, wherein ' represents the distance from the preset discharge source position to the reference channel.

[0134] It should be noted that the reference channel: when a discharge event is detected, the channel that detects the discharge earliest among all channels is taken as the reference channel.

[0135] Calculate the theoretical amplitude based on the preset discharge source position:

[0136]

[0137] in, The theoretical amplitude of the i-th channel is calculated based on the preset power supply position. This is the initial amplitude of the power supply. The discharge source to the channel is calculated based on the preset discharge source location. The distance, where α is the geometric attenuation factor. Let be the Fresnel loss function.

[0138] Calculate the theoretical energy contribution value based on the preset discharge source location:

[0139]

[0140] in, Let be the theoretical energy contribution value of the i-th channel calculated based on the preset discharge power supply position, where i is the index of the i-th channel. Indicates the total number of channels. 'Indicates candidate point The theoretical distance to the i-th channel, where α is the geometric attenuation factor.

[0141] It should be noted that, depending on the discharge signal type, when the discharge signal type is tip discharge, the signal propagation is mainly electromagnetic wave, with a fast attenuation rate and a larger geometric attenuation factor; when the discharge signal type is surface creepage, it is mainly ultrasonic wave, with stable propagation and a larger dielectric absorption coefficient; when the discharge signal type is air gap breakdown, it is a mixture of electromagnetic wave and ultrasonic wave, with a complex path and a higher loss function of the reflection step.

[0142] The objective function of the energy gradient estimation method is:

[0143]

[0144] Where F is the objective function of the energy gradient estimation method. Let be the response amplitude of the local voltage signal data of the i-th channel. 'This is the theoretical amplitude calculated based on the preset discharge source position.' Let be the actual signal arrival time difference between the i-th channel and the reference channel. 'This represents the theoretical time difference between the i-th channel and the reference channel, calculated based on the preset power supply position.' The energy contribution of the local voltage signal data of the i-th channel. Let be the theoretical energy contribution value of the i-th channel calculated based on the preset discharge power supply position, where i is the index of the i-th channel. This indicates the total number of channels.

[0145] Adjust the preset candidate discharge source points using the gradient descent method. the coordinates of the discharge source until the objective function of the energy gradient estimation method converges to a minimum value, at which time is the position of the discharge source.

[0146] Step S6: Collect the insulation cabinet voltage frequency domain envelope and insulation cabinet voltage energy distribution of the discharge source position within a preset time, and perform trend change analysis to obtain a partial discharge signal evolution value; divide the discharge risk level through the partial discharge signal evolution value to realize online monitoring of the gas insulation cabinet.

[0147] Collect the insulation cabinet superimposed voltage signal and insulation cabinet voltage energy distribution of the discharge source position within a preset time, divide the preset time into MS time windows, and perform trend change analysis.

[0148] Calculate the difference of the insulation cabinet voltage frequency domain envelope and the insulation cabinet voltage energy distribution of each adjacent time window:

[0149]

[0150] wherein, represents the partial discharge signal difference value of the ms-th time window, represents the envelope amplitude of the insulation cabinet voltage frequency domain envelope at frequency of the ms-th time window, represents the envelope amplitude of the insulation cabinet voltage frequency domain envelope at frequency of the ms-th time window, represents the energy proportion of the insulation cabinet voltage energy distribution at the k-th frequency point of the ms-th time window, represents the energy proportion of the insulation cabinet voltage energy distribution at the k-th frequency point of the ms-th time window, represents the index of the ms-th time window, is an envelope amplitude difference adjustment coefficient, used to adjust the influence of the envelope difference on the partial discharge signal difference value, is an energy proportion difference adjustment coefficient, used to adjust the influence of the energy proportion difference adjustment coefficient on the partial discharge signal difference value, is a zero prevention constant, and the value is . It should be noted that the envelope amplitude difference adjustment coefficient and the energy proportion difference adjustment coefficient can be determined according to the discharge signal category, for example: when the discharge signal category is “corona discharge”, the frequency domain envelope amplitude change is more sensitive, then can be taken as 0.7,

[0151] .​​​​ 0.3 is preferable; when the discharge signal category is "creepage discharge", the energy distribution deviation is more critical, 0.4 is preferable, 0.6 is preferable; when the discharge signal category is "internal discharge", both characteristics need to be balanced, 0.5 is preferable, 0.5 is preferable.

[0152] The partial discharge signal evolution value of the partial discharge signal within a preset time is calculated through the partial discharge signal difference value:

[0153]

[0154] wherein XB is the partial discharge signal evolution value, MS is the total number of time windows, represents the partial discharge signal difference value of the ms-th time window, represents the length of a unit time window.

[0155] The discharge risk level is divided by the partial discharge signal evolution value, realizing online monitoring of the gas insulated switchgear. XB If XB< 0.3, the risk level is level 1 normal, and routine inspection is required; <XB< 0.5, If XB< 0.5, the risk level is level 2 attention, and the monitoring frequency needs to be increased; <XB< 0.5, If XB< 0.5, the risk level is level 3 alert, and special monitoring is required; XB> If XB> 0.5, the risk level is level 4 emergency, and immediate repair is required.

[0156] This paper proposes a gas insulated switchgear online monitoring method and device, which constructs a discharge signal category library offline, collects partial voltage signals in real time and performs time division superposition, fast Fourier transform and other processing to obtain frequency domain envelope and energy distribution characteristics, then calculates difference value and similarity value through dynamic adjustment of frequency weight and dynamic time warping algorithm, realizes discharge signal category matching and discharge source positioning, and finally divides the discharge risk level through trend analysis to complete online monitoring.

[0157] The significance of dynamically adjusting the frequency weight and calculating the energy distribution difference value of the insulated cabinet, this step can adaptively highlight the frequency points with significant feature differences between the voltage signal and the discharge category library, so that the energy distribution difference value of the insulated cabinet can more accurately reflect the deviation degree of the real-time signal and the standard category. This process enhances the sensitivity to the characteristics of the discharge signal, effectively identifies the uniqueness of different types of discharge (such as corona, creepage discharge, etc.) in energy distribution, provides key quantitative basis for subsequent discharge category matching, and improves the accuracy and reliability of fault identification.

[0158] The cumulative distance of the frequency domain envelope of the insulating cabinet voltage and the frequency domain envelope of the discharge category signal is calculated by the dynamic time warping method, and the frequency domain similarity value of the insulating cabinet is obtained. The core significance lies in solving the nonlinear alignment problem of the frequency domain feature sequence, so as to accurately capture the shape similarity of different discharge signals. Due to the influence of factors such as propagation path and medium attenuation in the gas insulated switchgear, the frequency domain envelope may exist on the time axis or frequency axis (such as phase difference, frequency spread, etc.). The traditional Euclidean distance cannot effectively process such non-synchronous features. By using the dynamic programming algorithm to find the optimal time bending path, the two frequency domain envelope sequences are allowed to be locally stretched or compressed in the time or frequency dimension, so that the cumulative distance calculation is more in line with the actual signal difference. The obtained frequency domain similarity value of the insulating cabinet can quantitatively represent the shape matching degree of the real-time signal and the standard discharge category, and then assist in judging the discharge type (such as corona discharge, surface discharge, etc.), providing a key basis for accurate classification of discharge signals. At the same time, it can also identify the evolution law of early fault characteristics through the trend of the similarity value in long-term monitoring of the equipment.

[0159] The significance of collecting time series data and analyzing the evolution value of the partial discharge signal is that by collecting the frequency domain envelope and energy distribution data within a predetermined time and analyzing the trend, the evolution law of the partial discharge signal can be obtained, such as the change rate and stability of the characteristic parameters. The evolution value of the partial discharge signal can dynamically reflect the development trend of the discharge, such as the gradual change from normal to warning state, providing quantitative support in the time dimension for risk level division. This mechanism can provide early warning for potential faults, help maintenance personnel to develop differentiated maintenance strategies (such as routine inspection and emergency repair) according to the risk level, realize the forward-looking management of the gas insulated switchgear state, and reduce the risk of equipment failure.

[0160] An online monitoring device for a gas insulated switchgear, comprising:

[0161] A signal acquisition module: a plurality of partial discharge sensing channels are embedded in the key electric field non-uniform region in the gas insulated switchgear in a reverse staggered manner, and the discharge signals of the gas insulated switchgear are acquired through the partial discharge sensing channels to obtain a plurality of local voltage signal data of the gas insulated switchgear;

[0162] An insulating cabinet voltage energy distribution generation module: time-sharing superposition is performed on the plurality of local voltage signal data to obtain an insulating cabinet superimposed voltage signal, fast Fourier transform is performed on the insulating cabinet superimposed voltage signal to obtain an insulating cabinet voltage frequency domain amplitude spectrum, smoothing processing is performed on the insulating cabinet voltage frequency domain amplitude spectrum to obtain an insulating cabinet voltage frequency domain envelope, and energy distribution analysis is performed on the insulating cabinet voltage frequency domain amplitude spectrum to obtain an insulating cabinet voltage energy distribution;

[0163] The insulation cabinet frequency domain similarity value generation module: by carrying out offline test on the gas insulation cabinet and constructing a discharge signal category library, the discharge signal category library includes: discharge category signal energy distribution, discharge category signal frequency domain envelope; by dynamically adjusting the frequency weight in the insulation cabinet voltage energy distribution, calculating the reconstruction difference between the insulation cabinet voltage energy distribution and the discharge category signal energy distribution, obtaining the insulation cabinet energy distribution difference value; by dynamic time warping method, calculating the cumulative distance of the insulation cabinet voltage frequency domain envelope and the discharge category signal frequency domain envelope, obtaining the insulation cabinet frequency domain similarity value;

[0164] The discharge signal category generation module: by combining the insulation cabinet energy distribution difference value and the insulation cabinet frequency domain similarity value, calculating the discharge signal category matching value, and obtaining the discharge signal category according to the discharge signal category matching value;

[0165] The discharge source position judgment module: the internal medium absorption coefficient of the gas insulation cabinet is collected, based on the discharge signal category, the internal medium absorption coefficient of the gas insulation cabinet, the local voltage signal data is positioned and analyzed by energy gradient estimation method, and the discharge source position is obtained;

[0166] The monitoring module: the insulation cabinet voltage frequency domain envelope and the insulation cabinet voltage energy distribution of the discharge source position within a preset time are collected, and trend change analysis is performed, and the local discharge signal evolution value is obtained; by the local discharge signal evolution value, the discharge risk level is divided, and the online monitoring of the gas insulation cabinet is realized.

[0167] It should be noted that in this paper, relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the term "includes", "contains" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment. Without more limitation. The statement "including a limited element" does not exclude the existence of another identical element in the process, method, article or equipment including the element.

[0168] Although the embodiments of the present application have been shown and described, it can be understood by those skilled in the art that various changes, modifications, replacements and variations can be made to the embodiments without departing from the principles and spirits of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for online monitoring of a gas insulated tank, characterized in that: The method comprises the following steps: Step S1: embedding a plurality of partial discharge sensing channels in a key electric field non-uniform region in a gas insulated switchgear in a reverse staggered manner, collecting a discharge signal of the gas insulated switchgear through the partial discharge sensing channels, and obtaining a plurality of local voltage signal data of the gas insulated switchgear; Step S2: time-sharing superimposing the plurality of local voltage signal data to obtain a superimposed voltage signal of the gas insulated switchgear, performing fast Fourier transform on the superimposed voltage signal of the gas insulated switchgear to obtain a voltage frequency domain amplitude spectrum of the gas insulated switchgear, performing smoothing processing on the voltage frequency domain amplitude spectrum of the gas insulated switchgear to obtain a voltage frequency domain envelope of the gas insulated switchgear, and performing energy distribution analysis on the voltage frequency domain amplitude spectrum of the gas insulated switchgear to obtain a voltage energy distribution of the gas insulated switchgear; Step S3: performing offline testing on the gas insulated switchgear and constructing a discharge signal category library, the discharge signal category library comprising: a discharge category signal energy distribution and a discharge category signal frequency domain envelope; dynamically adjusting a frequency weight in the voltage energy distribution of the gas insulated switchgear, calculating a reconstruction difference between the voltage energy distribution of the gas insulated switchgear and the discharge category signal energy distribution, and obtaining a voltage energy distribution difference value of the gas insulated switchgear; calculating an accumulated distance between the voltage frequency domain envelope of the gas insulated switchgear and the discharge category signal frequency domain envelope by using a dynamic time warping method, and obtaining a frequency domain similarity value of the gas insulated switchgear; Step S4: calculating a discharge signal category matching value by combining the voltage energy distribution difference value of the gas insulated switchgear and the frequency domain similarity value of the gas insulated switchgear, and obtaining a discharge signal category according to the discharge signal category matching value; Step S5: collecting an internal medium absorption coefficient of the gas insulated switchgear, performing positioning analysis on the local voltage signal data by using an energy gradient estimation method based on the discharge signal category and the internal medium absorption coefficient of the gas insulated switchgear, and obtaining a discharge source position; Step S6: collecting the voltage frequency domain envelope and the voltage energy distribution of the discharge source position within a preset time, performing trend change analysis, obtaining a partial discharge signal evolution value, and dividing the discharge risk level by using the partial discharge signal evolution value to realize online monitoring of the gas insulated switchgear.

2. The method according to claim 1, characterized in that: The time-sharing superimposing the plurality of local voltage signal data to obtain the superimposed voltage signal of the gas insulated switchgear comprises the following specific steps: calculating an energy contribution value of the partial discharge data of each channel; ； wherein, Ei(n) is the energy contribution value of the local voltage signal data of the ith channel, n is the index of the nth sampling point, and N is the total number of samples within the time window. time-sharing superimposing the local voltage signal data within a time window to obtain the superimposed voltage signal of the gas insulated switchgear; ； wherein, represents the insulation cabinet superimposed voltage signal, represents the total number of channels, i represents the index of the local voltage signal data of the i-th channel, is the energy contribution value of the local voltage signal data of the i-th channel, represents the local voltage signal data of the i-th channel, and n represents the index of the n-th sampling point.

3. The method of claim 2, wherein: The fast Fourier transform on the superimposed voltage signal of the gas insulated switchgear to obtain the voltage frequency domain amplitude spectrum of the gas insulated switchgear and the smoothing processing on the voltage frequency domain amplitude spectrum of the gas insulated switchgear to obtain the voltage frequency domain envelope of the gas insulated switchgear comprise the following steps: The superimposed discharge signal is subjected to fast Fourier transform to obtain a frequency domain complex sequence : ； wherein, ] is the complex amplitude of the kth frequency bin, represents the insulating cabinet superimposed voltage signal, j is the complex unit, k is the frequency point index of the frequency domain, and N is the total number of samples in the time window. Taking the modulus value of the frequency domain complex sequence, the insulation cabinet voltage frequency domain amplitude spectrum is obtained |; curve fitting the voltage frequency domain amplitude spectrum of the gas insulated switchgear to obtain the voltage frequency domain envelope of the gas insulated switchgear; ； wherein, is the envelope amplitude of the voltage frequency domain envelope of the insulated tank at the frequency , ] is the complex amplitude of the kth frequency bin, is the Hilbert transform operator, is the frequency corresponding to the kth frequency bin.

4. The method of claim 3, wherein: The energy distribution analysis on the voltage frequency domain amplitude spectrum of the gas insulated switchgear to obtain the voltage energy distribution of the gas insulated switchgear comprises the following steps: the energy distribution analysis on the voltage frequency domain amplitude spectrum of the gas insulated switchgear to obtain the voltage energy distribution of the gas insulated switchgear. ； wherein, is the energy proportion of the voltage energy of the insulated tank at the kth frequency point, k is the frequency point index of the frequency domain, and N is the total number of samples in the time window, is the complex amplitude of the kth frequency point.

5. The method of online monitoring of a gas insulated tank according to claim 4, characterized in that: The method comprises the following steps: Calculate the average value of the discharge category signal energy distribution in the discharge signal category library: ； in, The average energy proportion of the discharge category signal energy distribution at the k-th frequency point. This represents the total number of signal energy distributions for each discharge category. For the first Index of signal energy distribution for each discharge category Indicates the first The proportion of energy of each discharge category signal at the k-th frequency point; Calculate the weight of the energy proportion of each frequency point of the insulation cabinet voltage energy distribution through the average value of the discharge category signal energy distribution: ； wherein, is the energy proportion weight of the insulation tank voltage energy distribution at the kth frequency point, is the energy proportion of the insulation tank voltage energy distribution at the kth frequency point, is the average value of the energy proportion of the discharge category signal energy distribution at the kth frequency point, is the adjustment coefficient, > 0, used to adjust the influence of the difference between the insulation tank voltage energy distribution and the discharge category signal energy distribution, is a constant term, > 1; Calculate the reconstruction difference between the insulation cabinet voltage energy distribution and the discharge category signal energy distribution to obtain the insulation cabinet energy distribution difference value: ； in, For the voltage energy distribution of the insulating cabinet and the first The energy distribution difference of the insulation cabinet for each discharge category signal energy distribution, where k is the frequency index in the frequency domain and N is the total number of samples within the time window. The energy proportion weight of the voltage energy distribution of the insulation cabinet at the k-th frequency point. This represents the proportion of voltage energy distributed at the k-th frequency point in the insulation cabinet. Indicates the first The proportion of energy of each discharge category signal at the k-th frequency point.

6. The method of online monitoring of a gas insulated tank according to claim 5, characterized in that: The method comprises the following steps: Calculate the cumulative distance between the insulation cabinet voltage frequency domain envelope and the discharge category signal frequency domain envelope through the dynamic time warping method to obtain the insulation cabinet frequency domain similarity value: ； in, For the voltage frequency domain envelope of the insulating cabinet and the first Frequency domain similarity values ​​of the insulation cabinet for each discharge category signal frequency domain envelope The voltage frequency domain envelope of the insulating cabinet at frequency The envelope amplitude, For the first The frequency domain envelope of each discharge category signal is in frequency. The envelope amplitude, DTW() is the dynamic time warping function. This is an adjustment coefficient used to adjust the effect of the difference between the frequency domain envelope of the insulation cabinet voltage and the frequency domain envelope of the discharge category signal.

7. The method of online monitoring of a gas insulated tank according to claim 6, characterized in that: Calculate the cumulative distance between the insulation cabinet voltage frequency domain envelope and the discharge category signal frequency domain envelope through the dynamic time warping method to obtain the insulation cabinet frequency domain similarity value: The method comprises the following steps: ； wherein, is a partial discharge signal and a first discharge signal class match value for a discharge signal class, is an insulation tank voltage energy distribution and a first discharge class signal energy distribution insulation tank energy distribution difference value, is an insulation tank voltage frequency domain envelope and a first discharge class signal frequency domain envelope insulation tank frequency domain similarity value.

8. The method of online monitoring of a gas insulated tank according to claim 7, characterized in that: Calculate the discharge signal category matching value by combining the insulation cabinet energy distribution difference value and the insulation cabinet frequency domain similarity value: Calculate the discharge signal category matching value by combining the insulation cabinet energy distribution difference value and the insulation cabinet frequency domain similarity value: ； wherein F is an objective function of the energy gradient estimation method, is a response amplitude of the local voltage signal data of the i-th channel, is a theoretical amplitude calculated based on the preset discharge source position, is an actual signal arrival time difference between the i-th channel and the reference channel, is a theoretical time difference between the i-th channel and the reference channel calculated based on the preset discharge source position, is an energy contribution of the local voltage signal data of the i-th channel, is a theoretical energy contribution value of the i-th channel calculated based on the preset discharge source position, i is an index of the i-th channel, denotes the total number of channels; Adjust the preset candidate discharge source points using the gradient descent method. The coordinates, until the objective function of the energy gradient estimation method. It converges to the minimum value, at which point This is where the power supply is located.

9. The method of online monitoring of a gas insulated tank according to claim 8, characterized in that: The method comprises the following steps: Objective function of the energy gradient estimation method: ； in, This represents the difference in partial discharge signal value within the ms-th time window. Indicates the first The frequency domain envelope of the insulating cabinet voltage within a time window is at frequency The envelope amplitude, Indicates the first The frequency domain envelope of the insulating cabinet voltage within a time window is at frequency The envelope amplitude, Indicates the first The proportion of the voltage energy distribution of the insulating cabinet at the k-th frequency point within a given time window. Indicates the first The proportion of the voltage energy distribution of the insulating cabinet at the k-th frequency point within a given time window. Indicates the first Index of a time window, This is the envelope amplitude difference adjustment coefficient, used to adjust the effect of envelope difference on the difference value of partial discharge signal. This is the energy ratio difference adjustment coefficient, used to adjust the effect of the energy ratio difference adjustment coefficient on the difference value of the partial discharge signal. To prevent division by zero, the value is taken as... ; The method comprises the following steps: ； Wherein XB is the partial discharge signal evolution value, MS is the total number of time windows, represents the partial discharge signal difference value of the ms time window, represents the length of a unit time window.

10. A gas insulated switchgear on-line monitoring device characterized by: Calculate the difference between the insulation cabinet voltage frequency domain envelope and the insulation cabinet voltage energy distribution of each adjacent time window: Calculate the difference between the insulation cabinet voltage frequency domain envelope and the insulation cabinet voltage energy distribution of each adjacent time window: Calculate the local discharge signal evolution value of the local discharge signal in the preset time through the local discharge signal difference value: Comprise: The signal acquisition module: a plurality of local discharge sensing channels are embedded in the key electric field non-uniform region in the gas insulated cabinet in a reverse staggered manner, the discharge signal of the gas insulated cabinet is collected through the local discharge sensing channel, and a plurality of local voltage signal data of the gas insulated cabinet are obtained; The insulation cabinet voltage energy distribution generation module: the plurality of local voltage signal data are superimposed in time to obtain an insulation cabinet superimposed voltage signal, the insulation cabinet voltage frequency domain amplitude spectrum is obtained through fast Fourier transform on the insulation cabinet superimposed voltage signal, the insulation cabinet voltage frequency domain envelope is obtained through smoothing processing on the insulation cabinet voltage frequency domain amplitude spectrum, and the insulation cabinet voltage energy distribution is obtained through energy distribution analysis on the insulation cabinet voltage frequency domain amplitude spectrum; The insulation cabinet frequency domain similarity value generation module: the gas insulated cabinet is tested offline, and a discharge signal category library is constructed, the discharge signal category library comprises: a discharge category signal energy distribution and a discharge category signal frequency domain envelope; The frequency weight in the energy distribution of the insulation cabinet voltage is dynamically adjusted, a reconstruction difference between the energy distribution of the insulation cabinet voltage and the discharge category signal energy distribution is calculated, and an insulation cabinet energy distribution difference value is obtained; the accumulated distance between the insulation cabinet voltage frequency domain envelope and the discharge category signal frequency domain envelope is calculated by the dynamic time warping method, and an insulation cabinet frequency domain similarity value is obtained; The discharge signal category generation module: the discharge signal category matching value is calculated by combining the insulation cabinet energy distribution difference value and the insulation cabinet frequency domain similarity value, and the discharge signal category is obtained according to the discharge signal category matching value; The discharge source position judgment module: the internal medium absorption coefficient of the gas insulation cabinet is collected, the discharge signal category, the internal medium absorption coefficient of the gas insulation cabinet, and the local voltage signal data are analyzed by the energy gradient estimation method based on the discharge signal category, and the discharge source position is obtained; The monitoring module: the insulation cabinet voltage frequency domain envelope and the insulation cabinet voltage energy distribution of the discharge source position within a preset time are collected, and the trend change analysis is performed, and the local discharge signal evolution value is obtained; the discharge risk level is divided by the local discharge signal evolution value, and the online monitoring of the gas insulation cabinet is realized.

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