Method and system for diagnosing electrical equipment failure

Abstract

The present application relates to the technical field of fault detection, in particular to an electrical equipment fault diagnosis method and system, comprising the following steps: based on partial discharge pulse interval, constructing trigger incremental sequence quantity and spectrum cluster ratio group, deducing impedance frequency boundary migration, analyzing conductor temperature rise rate difference, forming thermal response lag information, analyzing voltage and current phase difference fluctuation, and giving behavior abnormality judgment information, in the present application, the spectrum energy evolution characteristics are extracted through the dynamic behavior of partial discharge pulse interval, the frequency migration path is constructed to reflect the impedance boundary fluctuation process, the lag effect characteristics are extracted by combining the thermal inertia response difference in the temperature rise stage, the voltage and current phase fluctuation law is analyzed and the change trend of the combined characteristics is deduced, the layer-by-layer progressive judgment logic between the multi-stage dynamic coupling characteristics is established, the evolution process from local disturbance to global abnormal state is tracked, and the fine granularity recognition level of abnormal mechanism in the operation behavior of electrical equipment is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fault detection, in particular to an electrical equipment fault diagnosis method and system. BACKGROUND

[0002] The technical field of fault detection involves monitoring, identifying and determining the running state of an electrical system, including collecting electrical and physical quantities such as voltage, current, frequency, impedance, insulation resistance, temperature rise and partial discharge, identifying device running behavior and abnormal characteristics, inferring potential fault modes and locating fault positions. Through real-time measurement of the working parameters of the measured electrical equipment, comparison of normal running characteristics and abnormal change rules, a systematic detection framework is formed. This field covers multiple types of target equipment such as power transmission and distribution equipment, industrial electrical devices and electrical terminals. Detection methods include contact measurement, non-contact measurement, state monitoring and feature analysis. The traditional electrical equipment fault diagnosis method refers to identifying abnormal conditions such as insulation aging, conductor breakage, poor contact, short circuit, overload and partial discharge during the running of electrical equipment by obtaining characteristic information such as voltage waveform, current waveform, phase difference, frequency spectrum, dielectric loss factor and temperature distribution, comparing normal operating parameters to identify abnormal characteristics, and diagnosing the device state according to parameter thresholds set by artificial experience, fixed rule determination conditions or single feature quantity logic judgment steps. Traditional methods usually use direct measurement of electrical quantities, monitoring of device surface temperature, acquisition of acoustic discharge signals or recording of device running curves, compare reference values or experience models, and analyze the amplitude, frequency components or waveform distortion characteristics of parameter changes to determine whether the device has a specific form of fault.

[0003] The prior art relies on single-point measurement and static feature judgment of electrical equipment operating parameters. The judgment logic is based on set thresholds and fixed rules, and each item of electrical and physical quantity changes is analyzed. It cannot fully capture the dynamic correlation between parameters in abnormal states, and has limitations in dealing with multi-source feature interaction, behavior characteristic evolution and nonlinear coupling in the fault evolution process. It misjudges local changes, lacks response to continuous evolution paths, and lacks a full-process tracking mechanism when dealing with spectrum disturbance, thermal inertia lag, phase fluctuation and other linked faults. It is difficult to accurately locate the formation conditions and development process of potential abnormal behavior, affecting the stability and comprehensiveness of fault diagnosis. SUMMARY

[0004] To solve the technical problems existing in the prior art, the embodiments of the present application provide an electrical equipment fault diagnosis method, comprising the following steps:

[0005] In order to achieve the above-mentioned purpose, the technical scheme adopted by the present application is as follows: an electrical equipment fault diagnosis method, comprising the following steps:

[0006] S1: obtaining an electrical monitoring signal of a device, analyzing a time sequence of partial discharge pulses during operation, calculating interval differences of adjacent time points, constructing a distance sequence and judging a change direction, dividing paragraphs according to direction continuity, calculating a change in a paragraph, evaluating stability of the difference change, and generating a trigger incremental sequence quantity;

[0007] S2: based on the trigger incremental sequence quantity, extracting partial discharge pulse time domain waveform data corresponding to the time period, performing frequency domain transformation to obtain a frequency spectrum signal and analyzing amplitude change, constructing an equal-width frequency band sequence, calculating a cumulative quantity of each frequency band, judging a change direction of adjacent paragraphs and dividing an energy region according to direction continuity, calculating an energy region span, and judging the energy region with the largest span as a main region;

[0008] S3: based on the main region, extracting main region frequency band data, analyzing impedance amplitude changes on both sides of the frequency band and constructing a frequency difference sequence, judging adjacent change directions in the sequence, dividing boundary paragraphs according to direction consistency, calculating a moving distance of the boundary frequency, and generating a frequency migration change amplitude;

[0009] S4: based on the frequency migration change amplitude, extracting temperature change data of the conductor during temperature rise and fall, constructing a temperature change rate sequence, evaluating response asymmetry of the temperature rise and fall process, and generating thermal response lag information;

[0010] S5: based on the thermal response lag information, extracting voltage and current phase data, calculating a phase difference and constructing a phase difference sequence, judging a change direction in the phase difference sequence, dividing fluctuation paragraphs according to direction continuity, calculating a phase difference change amplitude and a continuous range of the fluctuation paragraph, comparing change patterns, identifying and judging abnormal coupling behavior, and generating behavior abnormality judgment information.

[0011] As a further scheme of the application, the trigger incremental sequence quantity specifically includes a difference change direction, change stability and change amplitude in a paragraph, the frequency migration change amplitude includes start and end frequencies of a boundary frequency and a frequency moving distance, a frequency difference sequence, the thermal response lag information specifically includes a temperature change rate, a time span of temperature change and a response difference paragraph, and the behavior abnormality judgment information specifically includes a phase difference change amplitude, a continuous range of a fluctuation paragraph and an abnormal coupling behavior pattern.

[0012] As a further scheme of the application, the trigger incremental sequence quantity specifically includes a difference change direction, change stability and change amplitude in a paragraph, the frequency migration change amplitude includes start and end frequencies of a boundary frequency and a frequency moving distance, a frequency difference sequence, the thermal response lag information specifically includes a temperature change rate, a time span of temperature change and a response difference paragraph, and the behavior abnormality judgment information specifically includes a phase difference change amplitude, a continuous range of a fluctuation paragraph and an abnormal coupling behavior pattern.

[0013] S101: Obtain an electrical monitoring signal of a device, analyze a time sequence of partial discharge pulses formed during operation, calculate interval differences of adjacent time points, construct a continuous interval sequence, judge a change direction of adjacent differences in the interval sequence, divide trend paragraphs based on a change distribution characteristic of the adjacent differences, and generate an interval direction distribution sequence;

[0014] S102: Based on the interval direction distribution sequence, calculate a direction change of the interval differences in each paragraph, count a number of jumps, analyze a change mode of the interval differences and a jump performance in each trend paragraph, and generate a difference change statistical parameter;

[0015] S103: According to the difference change statistical parameter, evaluate the stability of the change of the interval differences in the trend paragraph, judge a continuous release performance of a discharge activity, and establish a trigger incremental sequence quantity.

[0016] As a further scheme of the present application, the obtaining step of the main region is specifically:

[0017] S201: Based on the trigger incremental sequence quantity, extract time-domain waveform data of partial discharge pulses in a corresponding time period, perform frequency domain transformation to obtain a frequency spectrum signal and analyze an amplitude change, construct an equal-width frequency band sequence, calculate an amplitude cumulative quantity of each frequency band, form an energy paragraph sequence, and establish an energy paragraph distribution sequence;

[0018] S202: Based on the energy paragraph distribution sequence, judge an energy change direction between adjacent paragraphs, divide energy regions according to direction continuity, calculate a span of the energy regions in the frequency direction, and judge an energy region with the largest span as the main region.

[0019] As a further scheme of the present application, the obtaining step of the frequency migration change amplitude is specifically:

[0020] S301: Based on the main region, extract frequency band data corresponding to the main region, analyze changes of impedance amplitudes on both sides of the frequency band, construct a continuous frequency difference sequence, and generate a frequency difference change sequence;

[0021] S302: Call the frequency difference change sequence, judge a continuous direction of adjacent changes, divide boundary paragraphs according to direction consistency, locate a starting frequency and a terminal frequency appearing in the change sequence, combine to form a boundary range, and generate a frequency boundary range parameter;

[0022] S303: Based on the frequency boundary range parameter, arrange boundary frequencies in multiple periods in time sequence, calculate a moving distance of the boundary frequencies in the multiple periods, construct a cross-period change sequence, and generate a frequency migration change amplitude.

[0023] As a further scheme of the present application, the obtaining step of the thermal response hysteresis information is specifically:

[0024] S401: Extract temperature change data of the conductor in the running state in the temperature rising stage and the temperature falling stage based on the frequency migration change amplitude, analyze the time span of temperature change in the same section, construct a temperature rising rate sequence and a temperature falling rate sequence, and generate a temperature rate section sequence;

[0025] S402: Call the temperature rate section sequence, judge the rate change direction of the temperature rising stage and the temperature falling stage in the same temperature section, divide the response difference paragraph based on the temperature interval corresponding to the temperature rate section sequence, calculate the direction alternation times and the offset amplitude of the temperature change response in each difference paragraph, and generate a temperature response difference parameter;

[0026] S403: Based on the temperature response difference parameter, evaluate the response asymmetric performance of the temperature rising process and the temperature falling process in the same temperature section, and establish thermal response lag information.

[0027] As a further scheme of the present application, the behavior abnormality judgment information acquisition step is specifically:

[0028] S501: Based on the thermal response lag information, extract the voltage phase and current phase data corresponding to the running stage, calculate the phase difference between the two, construct a continuous phase difference sequence, and generate a phase difference change sequence;

[0029] S502: Call the phase difference change sequence, judge the direction of adjacent changes in the phase difference sequence, divide the fluctuation paragraph according to the direction continuity, calculate the phase difference change amplitude inside each fluctuation paragraph and the paragraph duration range, and generate a phase fluctuation parameter;

[0030] S503: According to the phase fluctuation parameter, compare the combination change mode of the phase difference change amplitude and the duration range, combine the fluctuation paragraph characteristic information, identify and judge the abnormal coupling behavior, and obtain behavior abnormality judgment information.

[0031] As a further scheme of the present application, the process of identifying and judging the abnormal coupling behavior is specifically:

[0032] Based on the phase fluctuation parameter, obtain a fluctuation paragraph set and a non-fluctuation paragraph set, and take the distribution upper limit of the non-fluctuation paragraph set on the phase difference change amplitude and the distribution upper limit on the duration range as the phase difference change amplitude determination limit and the duration range determination limit, respectively;

[0033] For each fluctuation paragraph, calculate the difference between the phase difference change amplitude and the phase difference change amplitude determination limit, and calculate the difference between the duration range and the duration range determination limit;

[0034] In the case that the two difference quantities are greater than zero at the same time, the corresponding fluctuation paragraph is marked as an abnormal coupling event, and the behavior abnormality judgment information is constituted by the time position of the marked fluctuation paragraph, the phase difference change amplitude of the marked fluctuation paragraph and the duration range of the marked fluctuation paragraph.

[0035] The electrical equipment fault diagnosis system comprises:

[0036] The interval behavior identification module is used for acquiring the electrical monitoring signal of the equipment, analyzing the time sequence of the partial discharge pulse during operation, calculating the interval difference of adjacent time points, constructing an interval sequence and judging the change direction, dividing paragraphs according to the direction continuity, calculating the change in the paragraph, evaluating the stability of the difference change, and generating a trigger incremental sequence quantity.

[0037] The frequency band energy construction module is used for extracting the partial discharge pulse time domain waveform data of the corresponding time period based on the trigger incremental sequence quantity, performing frequency domain transformation to obtain a frequency spectrum signal and analyzing the amplitude change, constructing an equal-width frequency band sequence, calculating the cumulative quantity of each frequency band, judging the energy change direction of adjacent paragraphs and dividing the energy region according to the direction continuity, calculating the energy region span, and judging the energy region with the largest span as a main region.

[0038] The frequency boundary derivation module is used for extracting the main region frequency band data based on the main region, analyzing the impedance amplitude change on both sides of the frequency band and constructing a frequency difference sequence, judging the adjacent change direction in the sequence, dividing boundary paragraphs according to the direction consistency, calculating the moving distance of the boundary frequency, and generating a frequency migration change amplitude.

[0039] The temperature hysteresis extraction module is used for extracting the temperature change data of the conductor temperature rise and fall stage based on the frequency migration change amplitude, constructing a temperature change rate sequence, evaluating the response asymmetry of the temperature rise and fall process, and generating thermal response hysteresis information.

[0040] The coupling offset determination module is used for extracting the voltage and current phase data based on the thermal response hysteresis information, calculating the phase difference and constructing a phase difference sequence, judging the change direction in the phase difference sequence, dividing fluctuation paragraphs according to the direction continuity, calculating the phase difference change amplitude and the duration range of the fluctuation paragraph, comparing the change mode, identifying and judging the abnormal coupling behavior, and generating behavior abnormality judgment information.

[0041] Compared with the prior art, the advantages and positive effects of the present application are that:

[0042] In the present application, the frequency spectrum energy evolution characteristics are extracted through the dynamic behavior of partial discharge pulse spacing, the frequency migration path is constructed to reflect the impedance boundary fluctuation process, the lag effect characteristics are extracted by combining the thermal inertia response difference in the temperature rise stage, the voltage and current phase fluctuation law is analyzed and the change trend of the combined characteristics is derived, the layer-by-layer progressive determination logic between the multi-stage dynamic coupling characteristics is established, the evolution process tracking from local disturbance to global abnormal state is realized, and the fine granularity recognition level of abnormal mechanism in the running behavior of electrical equipment is improved. BRIEF DESCRIPTION OF DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced. 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 creative labor.

[0044] Figure 1 The step flowchart of the present application is shown in the figure.

[0045] Figure 2 The S1 refinement schematic diagram of the present application is shown in the figure.

[0046] Figure 3 The S2 refinement schematic diagram of the present application is shown in the figure.

[0047] Figure 4 The S3 refinement schematic diagram of the present application is shown in the figure.

[0048] Figure 5 The S4 refinement schematic diagram of the present application is shown in the figure.

[0049] Figure 6 The S5 refinement schematic diagram of the present application is shown in the figure.

[0050] Figure 7 The system module diagram of the present application is shown in the figure. DETAILED DESCRIPTION

[0051] The technical solutions in the present application will be described below in combination with the drawings.

[0052] In the embodiments of the present application, the words such as "example", "for example" are used to represent as an example, illustration or description. Any embodiment or design scheme described as "example" in the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present application, the meaning expressed by "and / or" can be both, or can be one of the two.

[0053] In the embodiments of the present application, "image" and "picture" can be used interchangeably, and it should be pointed out that the meanings expressed are consistent when the distinction is not emphasized.

[0054] In the embodiments of the present application, sometimes the subscript such as W1 can be written in the form of non-subscript such as W1, and the meanings expressed are consistent when the distinction is not emphasized.

[0055] In order to make the technical problems, technical solutions and advantages to be solved by the present application more clear, the following will be described in detail in conjunction with the drawings and specific embodiments.

[0056] Please refer to Figure 1 The embodiments of the present application provide an electrical equipment fault diagnosis method, comprising the following steps:

[0057] S1: Obtain an electrical monitoring signal of the equipment, analyze a time sequence of partial discharge pulses during operation, calculate interval differences of adjacent time points, construct a distance sequence and judge a change direction, divide paragraphs according to direction continuity, calculate change conditions in the paragraphs, evaluate stability of difference changes, and generate a trigger incremental sequence quantity;

[0058] S2: Based on the trigger incremental sequence quantity, extract partial discharge pulse time domain waveform data of the corresponding time period, perform frequency domain transformation to obtain a frequency spectrum signal and analyze amplitude changes, construct an equal-width frequency band sequence, calculate a cumulative quantity of each frequency band, judge energy change directions of adjacent paragraphs and divide energy regions according to direction continuity, calculate energy region spans, and judge the energy region with the largest span as a main region;

[0059] S3: Based on the main region, extract main region frequency band data, analyze impedance amplitude changes on both sides of the frequency band and construct a frequency difference sequence, judge adjacent change directions in the sequence, divide boundary paragraphs according to direction consistency, calculate moving distances of boundary frequencies, and generate a frequency migration change amplitude;

[0060] S4: Based on the frequency migration change amplitude, extract temperature change data of the conductor temperature rise and fall stage, construct a temperature change rate sequence, evaluate response asymmetry of the temperature rise and fall process, and generate thermal response lag information;

[0061] S5: Based on the thermal response lag information, extract voltage and current phase data, calculate a phase difference and construct a phase difference sequence, judge change directions in the phase difference sequence, divide fluctuation paragraphs according to direction continuity, calculate phase difference change amplitudes and continuous ranges of the fluctuation paragraphs, compare change modes, identify and judge abnormal coupling behaviors, and generate behavior abnormality judgment information.

[0062] The trigger increment sequence quantity is specifically the difference change direction, change stability and change amplitude in the paragraph, the frequency migration change amplitude includes the start frequency and the frequency moving distance of the boundary frequency, the frequency difference sequence, the thermal response lag information is specifically the temperature change rate, the time span of the temperature change and the response difference paragraph, and the behavior abnormality judgment information is specifically the phase difference change amplitude, the duration range of the fluctuation paragraph and the abnormal coupling behavior mode.

[0063] Please refer to Figure 2 The acquisition step of the trigger increment sequence quantity is specifically:

[0064] S101: Obtain an electrical monitoring signal of a device, analyze a local discharge pulse arrival time sequence formed during operation, calculate interval differences of adjacent time points, construct a continuous interval sequence, judge the change direction of adjacent differences in the interval sequence, divide trend paragraphs based on the change distribution characteristics of adjacent differences, and generate an interval direction distribution sequence;

[0065] In the insulation state monitoring implementation process of a 110kV GIS device, first, an electrical monitoring signal inside the device is obtained through an ultra-high frequency (UHF) sensor array, the sampling frequency is set to 2GS / s, and the capture time window length is 20ms (i.e. one power frequency period). The system executes a program to identify local discharge pulses with an amplitude exceeding the background noise level (for example, -60dBm) in the signal, extracts the peak arrival time of each pulse, and records it as a set . For example, in a certain sampling, 5 pulses are captured, and the arrival times are . The system calculates the interval differences of adjacent time points , and obtains the interval sequence . Then, a continuous interval sequence is constructed and the change amount of adjacent differences is calculated . In this example, . The system judges the change direction of adjacent differences in the interval sequence according to the positive and negative values of . When , it is marked as “increasing direction”, when , it is marked as “decreasing direction”, and when , it is marked as “maintaining direction”. According to the overall change trend of adjacent differences, paragraphs are divided. For example, if mainly shows positive values (allowing a small number of negative or zero values caused by noise) in a certain period, it is marked as an “increasing trend paragraph”. The system traverses all pulse data in the monitoring period, divides the time axis into multiple paragraphs with consistent direction, and finally generates an interval direction distribution sequence containing the starting time position, the ending time position and the direction attribute of each paragraph, as the basic data source for subsequent feature extraction.

[0066] S102: Based on the interval direction distribution sequence, the direction change of the interval difference in each paragraph is calculated, the number of jumps is counted, the change pattern of the interval difference in each trend paragraph is analyzed, and the difference change statistical parameter is generated;

[0067] Based on the interval direction distribution sequence, the system retrieves each divided paragraph to calculate the microscopic features. Taking an "increasing paragraph" containing 10 pulse intervals as an example, the system calculates the specific numerical change of the interval difference in the paragraph, i.e. the second-order difference sequence. If the difference value sequence in the paragraph is (unit: ), where -5 represents a non-monotonic numerical fluctuation, the system counts the number of times the sign changes, which is defined as the "number of jumps". In this sequence, there is one positive-to-negative jump from 10 to -5, and one negative-to-positive jump from -5 to 10, so the number of jumps is 2. The system analyzes the change pattern of the interval difference in each paragraph, and quantifies the performance by calculating the mean and variance of the difference values in the paragraph. For example, the mean difference of a paragraph is calculated to be , and the variance is . At the same time, the system combines the jump performance and defines the ratio of the number of jumps to the length of the paragraph as the "perturbation rate". If the paragraph length is 7 difference points and the number of jumps is 2, the perturbation rate is . The system combines the mean, variance, number of jumps, and perturbation rate to generate the difference change statistical parameter of the paragraph. This process is repeated for each paragraph in the sequence, thereby converting the originally discrete time sequence into a set of statistical vectors describing the compactness and regularity of the discharge rhythm, which is used to distinguish random interference signals (usually with many jumps and large variance) from typical insulation defect discharges (usually with regular increasing or decreasing trends).

[0068] S103: According to the difference change statistical parameter, the stability of the interval difference change in the trend paragraph is evaluated, the continuous release performance of the discharge activity is judged, and the trigger increasing sequence quantity is established.

[0069] According to the difference change statistical parameter, the system performs stability evaluation and continuity judgment. Set the stability judgment threshold and the jump frequency threshold (the threshold is obtained according to the historical typical metal protrusion defect database statistics). For each paragraph, if its difference variance and perturbation rate If the difference changes in the paragraph are stable, it is determined that the paragraph has "stability". On this basis, the system further determines the sustained release performance of the discharge activity: the proportion of the length of time covered by the paragraph determined as "stability" in the total monitoring period is calculated. If the total length of the paragraphs with stable increasing or decreasing in a 20ms period is more than 12ms (accounting for 60%), it is determined that there is a sustained discharge release activity in the period. The system selects all pulse sequences that meet the above stability and sustainability conditions, extracts the original time stamp and corresponding amplitude information, and establishes the trigger increasing sequence amount. For example, in one detection, the system excludes random pulse interference caused by switch operation, locks three groups of stable increasing pulse clusters that appear near the peak value of the power frequency voltage, packages the three groups of data as the trigger increasing sequence amount, and transmits it to the subsequent frequency domain analysis module to ensure that the subsequent analysis is only for high-confidence defect signals.

[0070] Please refer to Figure 3 The acquisition step of the main area is specifically:

[0071] S201: Based on the trigger increasing sequence amount, extract the local discharge pulse time domain waveform data of the corresponding time period, perform frequency domain transformation to obtain the frequency spectrum signal and analyze the amplitude change, construct an equal-width frequency band sequence, calculate the amplitude cumulative amount of each frequency band, form an energy paragraph sequence, and establish an energy paragraph distribution sequence;

[0072] Based on the trigger increasing sequence amount, the system traces back to the acquisition memory and extracts the original time domain waveform data in the corresponding time period (for example, the window) where the pulse cluster occurs. Perform fast Fourier transform (FFT) on the waveform data to convert the signal from time domain to frequency domain, and the frequency analysis range is set to 300MHz to 1.5GHz. The system constructs an equal-width frequency band sequence, divides the entire analysis band into a plurality of sub-frequency bands with a width of , a total of 120 frequency bands. For each sub-frequency band, calculate the sum of the amplitudes of all frequency points within it as the amplitude cumulative amount (i.e. the frequency band energy) of the frequency band. For example, for the 400MHz-410MHz frequency band, the energy value is ; for the 410MHz-420MHz frequency band, the energy value is . Calculate the energy of all 120 frequency bands in turn to form an energy paragraph sequence. Then, the system establishes an energy paragraph distribution sequence, which is composed of 120 energy values arranged in ascending order of frequency.

[0073] Table 1 Local Discharge Signal Frequency Band Energy Distribution Table (Part)

[0074] Frequency band number Frequency range (MHz) Amplitude accumulation Note 11 400-410 150 Background noise region 12 410-420 180 Background noise region … … … … 35 640-650 2500 Energy significantly rises 36 650-660 4800 Peak region 37 660-670 4600 Peak region 38 670-680 2200 Energy drops

[0075] As shown in Table 1, the energy calculation results of part of the frequency bands are shown as the basis data for subsequent energy region division.

[0076] S202: Based on the energy paragraph distribution sequence, the energy change direction between adjacent paragraphs is judged, the energy region is divided according to the direction continuity, the span of the energy region in the frequency direction is calculated, and the energy region with the largest span is judged as the main region;

[0077] Based on the energy paragraph distribution sequence, the system analyzes the energy change gradient between adjacent frequency bands. The energy difference between adjacent frequency bands is calculated , if , it is determined that the energy rises; if , it is determined that the energy decreases. The system divides the energy region according to the direction continuity, that is, the "peak" structure. For example, the energy continuously rises from the 34th frequency band to the 36th frequency band, and then continuously decreases from the 37th frequency band to the 40th frequency band, and then the 34th to the 40th frequency band is defined as a complete "energy region". The system identifies all energy regions in the entire frequency spectrum and calculates the span of each region in the frequency direction (i.e. the number of frequency bands multiplied by the width of a single frequency band). It is assumed that two main energy regions are identified: region A (main region) covers 600MHz-750MHz, with a span of ; region B (secondary region) covers 900MHz-950MHz, with a span of .

[0078] Please refer to Figure 4 , the steps for obtaining the frequency migration change amplitude are as follows:

[0079] S301: Based on the main region, the frequency band data corresponding to the main region is extracted, and the change of the impedance amplitude on both sides of the frequency band is analyzed, a continuous frequency difference sequence is constructed, and a frequency difference change sequence is generated;

[0080] Based on the main region, the system locks the frequency range corresponding to the main region (for example, 600MHz-750MHz) as the key analysis object. In this frequency band, the system calls the online impedance monitoring module to obtain the equivalent input impedance amplitude spectrum of the device in this frequency band. The system analyzes the impedance amplitude change on both sides of the frequency band (i.e. the edge region of 580MHz-600MHz and 750MHz-770MHz). The system calculates the impedance modulus difference of adjacent frequency points with a step of 1MHz, and constructs a continuous frequency difference sequence. For example, in the interval of 750MHz to 755MHz, the impedance modulus is , and the corresponding difference sequence is The sequence reflects the rate of change of system impedance characteristic at the edge of main discharge frequency band. The system stores this sequence of differential values by frequency index, generating a sequence of frequency difference changes, which is used to capture the precursor of resonance point drift caused by local carbonization of insulation material or structural micro-variation.

[0081] S302: Call the frequency difference change sequence, judge the continuous direction of adjacent changes, divide the boundary paragraph according to the consistency of the direction, locate the starting frequency and the ending frequency appearing in the change sequence, combine to form the boundary range, and generate the frequency boundary range parameter;

[0082] Call the frequency difference change sequence, and the system judges the continuous direction of adjacent changes. Set the impedance change threshold When the continuous 3 frequency steps are all positive and the cumulative change amount exceeds , it is determined to enter the "impedance mutation zone". The system divides the boundary paragraph according to the consistency of the direction, locates the starting frequency and the ending frequency appearing in the change sequence. For example, in a certain detection, it is found that the impedance starts to rise significantly at 752MHz (starting frequency) and tends to be flat at 758MHz (ending frequency), then the boundary range is combined to form. This range represents the current system resonance boundary or filter cutoff characteristic boundary. The system extracts the center frequency of the boundary range as the current characteristic boundary frequency. After completing the calculation of a single detection, the frequency boundary range parameter containing , and is generated, which accurately describes the frequency response boundary position under the current physical state.

[0083] S303: Based on the frequency boundary range parameter, arrange the boundary frequencies in multiple periods in chronological order, calculate the moving distance of the boundary frequencies in multiple periods, construct a cross-period change sequence, and generate the frequency migration change amplitude;

[0084] Based on the frequency boundary range parameter, the system calls the historical database and arranges the boundary frequency data in multiple detection periods within the past 30 days in chronological order. Assuming that it is recorded once every 24 hours, a time sequence is formed. The system calculates the moving distance of the boundary frequencies in multiple periods. For example, the first day , the 15th day , and the 30th day . Calculate the total migration amount and the average migration rate of adjacent periods. Construct a cross-period change sequence, which records the drift trajectory of the boundary frequency over time. The system sets an aging drift threshold . If the calculated Exceeding the threshold, it indicates that the dielectric constant or geometry of the insulating medium has undergone irreversible cumulative changes (such as the deformation of the insulating rod due to moisture, resulting in changes in capacitance parameters). The system finally generates a frequency migration change amplitude, quantitatively describes this slow-changing physical process, and transmits this amplitude value to the subsequent thermal analysis module.

[0085] Please refer to Figure 5 , the acquisition step of the thermal response lag information is specifically:

[0086] S401: Based on the frequency migration change amplitude, extract the temperature change data of the conductor in the running state during the heating stage and the cooling stage, analyze the time span of temperature change in the same section, construct the heating rate sequence and the cooling rate sequence, and generate the temperature rate section sequence;

[0087] Based on the frequency migration change amplitude anomaly (prompting the existence of structural or medium micro-change), the system starts thermal correlation analysis. Extract the temperature change data and corresponding current load data of the GIS guide rod in the last complete load cycle (including the heating stage from low load to full load, and the cooling stage from full load to low load). The data sampling interval is 1 minute. The system analyzes the time span of temperature change in the same temperature section (for example, the temperature section of 40°C to 60°C of the shell). Construct the heating rate sequence and the cooling rate sequence . Specifically, divide the 40°C-60°C section into 1°C micro units, calculate the time required for each 1°C increase , and obtain the heating rate . Similarly, calculate the cooling rate. For example, in the 45°C-46°C section, the heating time is 5 minutes (rate ), and the cooling time through the same section is 8 minutes (rate ). The system aligns the rate data in the full section according to the temperature index, generating a temperature rate section sequence, preparing for comparing the thermal inertia difference.

[0088] S402: Call the temperature rate section sequence, judge the rate change direction in the same temperature section during the heating and cooling stages, divide the response difference paragraphs based on the temperature interval of the temperature rate section sequence, calculate the number of direction alternations and the offset amplitude of temperature change response in each difference paragraph, and generate the temperature response difference parameter;

[0089] Call the temperature rate section sequence, the system judges the rate change direction and difference in the same temperature section during the heating and cooling stages for each temperature micro unit (such as 45°C). Calculate the rate difference , where represents the heating rate at the temperature monitoring point , and represents the cooling rate at the same temperature monitoring point The cooling rate at the temperature monitoring point, where subscript represents the corresponding temperature monitoring point. The continuity of the rate direction divides the response difference section. Normally, since the natural cooling is slower than the active heating, the positive value should be maintained and linearly changed. If fluctuations or sign reversals occur in a section (such as 50°C-55°C) , i.e. the cooling rate is faster than the heating rate, or the heating rate suddenly slows down, it is marked as an abnormal difference section. The system calculates the number of times the direction of temperature change response alternates and the maximum deviation amplitude in each difference section. For example, in the 50°C-55°C section, the value changes from positive to negative and then to positive, with 2 times of alternation, and the maximum deviation amplitude (deviation from the theoretical thermal model value) reaches . These parameters reflect whether there is a nonlinear heat source inside the device, such as a loose contact point whose contact resistance changes with temperature expansion. The system generates a temperature response difference parameter to quantify the nonlinear characteristics of this thermal response.

[0090] S403: Based on the temperature response difference parameter, evaluate the asymmetric response of the heating and cooling processes in the same temperature section, and establish the thermal response hysteresis information;

[0091] Based on the temperature response difference parameter, the system evaluates the asymmetric response of the heating and cooling processes in the same temperature section. Using the "thermal hysteresis loop area" algorithm, the area enclosed by the heating curve and the cooling curve in the "temperature-rate" coordinate system is calculated. At the same time, combined with the number of times the direction alternates, it is judged whether there is a thermal breathing effect caused by mechanical looseness. Set the hysteresis judgment reference value (unitized area). In the actual example, the of the current monitoring period is calculated, and it is accompanied by 2 times of rate direction alternation. indicating that the device has a significant thermal response hysteresis. This result reveals that the frequency migration (S3 result) is not simply overall aging, but is accompanied by changes in the contact surface or structural micro-displacement caused by thermal expansion and contraction. The system codes the area ratio and nonlinear characteristic calculated, and establishes the thermal response hysteresis information, which confirms that the fault has the complex attribute of "thermal-mechanical-electric" coupling.

[0092] Please refer to Figure 6 , the steps of obtaining the abnormal behavior judgment information are as follows:

[0093] S501: Based on the thermal response hysteresis information, extract the voltage phase and current phase data of the corresponding running stage, calculate the phase difference, construct a continuous phase difference sequence, and generate a phase difference change sequence;

[0094] Based on the thermal response hysteresis information (confirming the existence of thermomechanical instability), the system further investigates the microscopic impact of this instability on the electrical phase. High-precision voltage phases for the corresponding operating phases are extracted. Phase with current The data originates from a synchronized phasor measurement unit (PMU) with a sampling rate of 100Hz. The system calculates the phase difference between the two data sources. (i.e., power factor angle), and construct a continuous phase difference sequence. Under normal steady-state operation, It should be a constant or change very slowly. However, at the fault trigger point, Microsecond-level jitter may occur. The system generates a sequence of phase difference changes. For example, a segment in the sequence shows a phase difference from... Slight changes to Then restore to This step aims to capture the loss tangent caused by the insulating dielectric. Weak phase modulation signals caused by instantaneous changes or partial discharges.

[0095] S502: Call the phase difference change sequence, determine the direction of adjacent changes in the phase difference sequence, divide the fluctuation segment according to the continuity of direction, calculate the phase difference change amplitude within each fluctuation segment and count the segment duration range, and generate phase fluctuation parameters.

[0096] The system calls the phase difference change sequence and determines the direction of adjacent changes in the phase difference sequence. Calculation. The system divides the fluctuation segments into monotonically increasing or decreasing segments based on their signs. For each fluctuation segment, the system calculates the amplitude of the phase difference change within it. Where δ is the set of all phase difference values ​​within the fluctuation segment, and the duration of the segment is calculated. For example, identifying a wave segment with a phase difference within 0.5 seconds (). It happened offset ( The system statistically analyzes all fluctuations throughout the monitoring period, generating a set of phase fluctuation parameters including amplitude and duration. Through statistical analysis, the system establishes a two-dimensional distribution of fluctuation amplitude and duration to distinguish between systemic fluctuations on the power grid side (usually large amplitude and long period) and localized coupled fluctuations caused by internal equipment defects (usually extremely small amplitude, short duration, and time-aligned with thermal hysteresis events).

[0097] S503: Based on the phase fluctuation parameters, compare the combined change patterns of the phase difference change amplitude and duration range, combine the fluctuation segment feature information to identify and judge abnormal coupling behavior, and obtain abnormal behavior judgment information.

[0098] According to the phase fluctuation parameters, the system performs the final discrimination of abnormal coupling behavior. First, the fluctuation paragraph set and the non-fluctuation paragraph set (baseline background) are obtained. The mean value plus 3 times the standard deviation of the phase difference change amplitude in the non-fluctuation state is calculated as the phase difference change amplitude judgment limit (e.g. ); the upper limit of the distribution of the duration range is calculated as the duration range judgment limit (e.g. ). For each fluctuation paragraph to be judged, the difference amount is calculated: , . Substituting the example data in S502: , ; , . In the case where both difference amounts are greater than zero, the system judges that the fluctuation paragraph is an "abnormal coupling event". This judgment logic shows that after the device experiences thermal hysteresis deformation, the electrical parameters of the insulating medium have macroscopic degradation sufficient to affect the phase angle. The system packages the time position, amplitude and duration range of the event to obtain behavior abnormality judgment information. This result shows that there is an insulation defect in the device that evolves with thermal cycling and has begun to affect power transmission characteristics, which belongs to a high-risk fault. The system finally outputs a comprehensive diagnostic report containing the fault type (thermal-electric coupling type), severity and occurrence time.

[0099] Please refer to Figure 7 , an electrical equipment fault diagnosis system, comprising:

[0100] a distance behavior recognition module for obtaining electrical monitoring signals of the device, analyzing the time sequence of partial discharge pulses during operation, calculating the interval difference between adjacent time points, constructing the interval sequence and judging the change direction, dividing paragraphs according to the direction continuity, calculating the change within the paragraph, evaluating the stability of the difference change, and generating the trigger incremental sequence amount;

[0101] a frequency band energy construction module for extracting the partial discharge pulse time domain waveform data of the corresponding time period based on the trigger incremental sequence amount, performing frequency domain transformation to obtain the frequency spectrum signal and analyzing the amplitude change, constructing the equal-width frequency band sequence, calculating the cumulative amount of each frequency band, judging the energy change direction of adjacent paragraphs and dividing the energy region according to the direction continuity, calculating the energy region span, and judging the energy region with the largest span as the main region;

[0102] a frequency boundary derivation module for extracting the main region frequency band data based on the main region, analyzing the impedance amplitude change on both sides of the frequency band and constructing the frequency difference sequence, judging the adjacent change direction in the sequence, dividing the boundary paragraph according to the direction consistency, calculating the moving distance of the boundary frequency, and generating the frequency migration change amplitude;

[0103] a temperature hysteresis extraction module configured to extract temperature change data of the conductor in the temperature rising and falling stage based on the frequency migration change amplitude, construct a temperature change rate sequence, evaluate the response asymmetry of the temperature rising and falling process, and generate thermal response hysteresis information;

[0104] a coupling offset determination module configured to extract voltage and current phase data based on the thermal response hysteresis information, calculate a phase difference and construct a phase difference sequence, determine a change direction in the phase difference sequence, divide fluctuation paragraphs according to the direction continuity, calculate a phase difference change amplitude and a continuous range of the fluctuation paragraphs, compare the change modes, identify and determine abnormal coupling behaviors, and generate behavior abnormality determination information.

[0105] The above merely describes a specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present application, which should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method of diagnosing a failure of an electrical device, characterized by, The method comprises the following steps: S1: obtaining an electrical monitoring signal of a device, analyzing a time sequence of partial discharge pulses during operation, calculating interval differences of adjacent time points, constructing a distance sequence and judging a change direction, dividing paragraphs according to direction continuity, calculating a change in a paragraph, evaluating the stability of the difference change, and generating a trigger incremental sequence quantity; S2: based on the trigger incremental sequence quantity, extracting partial discharge pulse time domain waveform data of the corresponding time period, performing frequency domain transformation to obtain a frequency spectrum signal and analyzing the amplitude change, constructing an equal-width frequency band sequence, calculating the cumulative quantity of each frequency band, judging the energy change direction of adjacent paragraphs and dividing the energy region according to the direction continuity, calculating the energy region span, and judging the main region with the largest span; S3: based on the main region, extracting the main region frequency band data, analyzing the impedance amplitude change on both sides of the frequency band and constructing a frequency difference sequence, judging the adjacent change direction in the sequence, dividing the boundary paragraph according to the direction consistency, calculating the moving distance of the boundary frequency, and generating a frequency migration change amplitude; S4: based on the frequency migration change amplitude, extracting temperature change data of the conductor temperature rise stage, constructing a temperature change rate sequence, evaluating the response asymmetry of the temperature rise process, and generating thermal response lag information; S5: based on the thermal response lag information, extracting voltage and current phase data, calculating the phase difference and constructing a phase difference sequence, judging the change direction in the phase difference sequence, dividing the fluctuation paragraph according to the direction continuity, calculating the phase difference change amplitude and the duration range of the fluctuation paragraph, comparing the change mode, identifying and judging the abnormal coupling behavior, and generating behavior abnormality judgment information; The acquisition step of the behavior abnormality judgment information is specifically: S501: based on the thermal response lag information, extracting voltage phase and current phase data corresponding to the operation stage, calculating the phase difference between the two, constructing a continuous phase difference sequence, and generating a phase difference change sequence; S502: calling the phase difference change sequence, judging the direction of adjacent changes in the phase difference sequence, dividing the fluctuation paragraph according to the direction continuity, calculating the phase difference change amplitude inside each fluctuation paragraph and the paragraph duration range, and generating a phase fluctuation parameter; S503: according to the phase fluctuation parameter, comparing the combination change mode of the phase difference change amplitude and the duration range, combining the fluctuation paragraph characteristic information, identifying and judging the abnormal coupling behavior, and obtaining the behavior abnormality judgment information.

2. The electric equipment failure diagnosis method according to claim 1, characterized by, The trigger incremental sequence quantity is specifically the difference change direction, change stability and change amplitude in the paragraph, the frequency migration change amplitude includes the start and end frequencies of the boundary frequency and the frequency moving distance, the frequency difference sequence, the thermal response lag information is specifically the temperature change rate, the time span of the temperature change and the response difference paragraph, and the behavior abnormality judgment information is specifically the phase difference change amplitude, the duration range of the fluctuation paragraph and the abnormal coupling behavior mode.

3. The electric equipment failure diagnosis method according to claim 1, characterized by, The acquisition step of the trigger incremental sequence quantity is specifically: S101: Obtain the electrical monitoring signal of the device, analyze the time sequence of the partial discharge pulses formed during operation, calculate the interval difference between adjacent time points, construct a continuous interval sequence, judge the change direction of the adjacent difference in the interval sequence, divide the trend paragraphs based on the change distribution characteristics of the adjacent difference, and generate the interval direction distribution sequence; S102: Based on the interval direction distribution sequence, calculate the direction change of the interval difference in each paragraph, count the number of jumps, analyze the interval difference change pattern and jump performance in each trend paragraph, and generate the difference change statistical parameter; S103: According to the difference change statistical parameter, evaluate the stability of the interval difference change in the trend paragraph, judge the continuous release performance of the discharge activity, and establish the trigger incremental sequence quantity.

4. The electric equipment failure diagnosis method according to claim 3, characterized by, The acquisition step of the main region is specifically: S201: Based on the trigger incremental sequence quantity, extract the time domain waveform data of the partial discharge pulse in the corresponding time period, perform frequency domain transformation to obtain the frequency spectrum signal and analyze the amplitude change, construct an equal-width frequency band sequence, calculate the amplitude accumulation of each frequency band, form an energy paragraph sequence, and establish an energy paragraph distribution sequence; S202: Based on the energy paragraph distribution sequence, judge the energy change direction between adjacent paragraphs, divide the energy region according to the direction continuity, calculate the span of the energy region in the frequency direction, and judge the energy region with the largest span as the main region.

5. The electric equipment failure diagnosis method according to claim 4, characterized by, The acquisition step of the frequency migration change amplitude is specifically: S301: Based on the main region, extract the frequency band data corresponding to the main region, analyze the change of impedance amplitude on both sides of the frequency band, construct a continuous frequency difference sequence, and generate a frequency difference change sequence; S302: Call the frequency difference change sequence, judge the continuous direction of adjacent changes, divide the boundary paragraph according to the direction consistency, locate the start frequency and end frequency appearing in the change sequence, combine to form a boundary range, and generate a frequency boundary range parameter; S303: Based on the frequency boundary range parameter, arrange the boundary frequencies in multiple cycles in time sequence, calculate the moving distance of the boundary frequencies in multiple cycles, construct a cross-cycle change sequence, and generate a frequency migration change amplitude.

6. The electric equipment failure diagnosis method according to claim 5, characterized by, The acquisition step of the thermal response lag information is specifically: S401: Based on the frequency migration change amplitude, extract the temperature change data of the conductor in the heating stage and the cooling stage under the running state, analyze the time span of the temperature change in the same section, construct a heating rate sequence and a cooling rate sequence, and generate a temperature rate section sequence; S402: Call the temperature rate section sequence, judge the rate change direction in the same temperature section during the heating and cooling stages, divide the response difference paragraph based on the temperature interval corresponding to the temperature rate section sequence, calculate the direction alternation times and offset amplitude of the temperature change response in each difference paragraph, and generate a temperature response difference parameter; S403: Based on the temperature response difference parameter, evaluate the response asymmetry performance of the heating and cooling processes in the same temperature section, and establish the thermal response lag information.

7. The electric equipment failure diagnosis method according to claim 1, characterized by, The process of identifying and judging abnormal coupling behavior is specifically: acquire a fluctuation paragraph set and a non-fluctuation paragraph set based on the phase fluctuation parameter, and take the distribution of the non-fluctuation paragraph set on the phase difference change amplitude and the distribution on the duration range as the phase difference change amplitude determination limit and the duration range determination limit respectively; calculate the difference between the phase difference change amplitude and the phase difference change amplitude determination limit and the difference between the duration range and the duration range determination limit for each fluctuation paragraph; in the case that both the differences are greater than zero, mark the corresponding fluctuation paragraph as an abnormal coupling event, and the behavior abnormality judgment information is composed of the time position of the marked fluctuation paragraph, the phase difference change amplitude of the marked fluctuation paragraph, and the duration range of the marked fluctuation paragraph.

8. An electric equipment failure diagnosis system characterized by comprising: The system is used to implement the electrical equipment fault diagnosis method of any one of claims 1-7, and the system comprises: a distance behavior recognition module, configured to acquire an electrical monitoring signal of the equipment, analyze a time sequence of partial discharge pulses during operation, calculate interval differences of adjacent time points, construct a distance sequence and judge a change direction, divide paragraphs according to direction continuity, calculate a change within a paragraph, evaluate the stability of difference changes, and generate a trigger incremental sequence amount; a frequency band energy construction module, configured to extract partial discharge pulse time domain waveform data of a corresponding time period based on the trigger incremental sequence amount, perform frequency domain transformation to obtain a frequency spectrum signal and analyze amplitude changes, construct an equal-width frequency band sequence, calculate a cumulative amount of each frequency band, judge energy change directions of adjacent paragraphs and divide energy regions according to direction continuity, calculate energy region spans, and judge a main region with the largest span; a frequency boundary derivation module, configured to extract main region frequency band data based on the main region, analyze impedance amplitude changes on both sides of the frequency band and construct a frequency difference sequence, judge adjacent change directions in the sequence, divide boundary paragraphs according to direction consistency, calculate a moving distance of a boundary frequency, and generate a frequency migration change amplitude; a temperature lag extraction module, configured to extract temperature change data of a conductor temperature rising and falling stage based on the frequency migration change amplitude, construct a temperature change rate sequence, evaluate response asymmetry of the temperature rising and falling process, and generate thermal response lag information; a coupling offset determination module, configured to extract voltage and current phase data based on the thermal response lag information, calculate a phase difference and construct a phase difference sequence, judge change directions in the phase difference sequence, divide fluctuation paragraphs according to direction continuity, calculate a phase difference change amplitude and a duration range of the fluctuation paragraphs, compare change modes, identify and judge abnormal coupling behaviors, and generate behavior abnormality judgment information.

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