High-voltage switch cabinet fault monitoring and early warning system and method

Abstract

The invention discloses a high-voltage switch cabinet fault monitoring and early warning system and method, and relates to the technical field of high-voltage switch cabinet fault monitoring. Comprising a detection acquisition module, a temperature monitoring module, a live monitoring module, a fault analysis module and an early warning execution module. In order to solve the problems of multiple challenges, insufficient data acquisition precision and comprehensiveness, lack of prediction capability for fault development trend and inaccurate fault abnormity judgment in the prior art, the invention utilizes the technical means of normalization processing, Fourier transform, correlation analysis and the like to accurately evaluate the monitoring data, so that the accuracy of the data acquisition is improved. Comprehensive judgment is performed in combination with multiple indexes, so that the problem of insufficient fault feature extraction and anomaly judgment capabilities in a traditional method is effectively solved; space positioning, time synchronization and correlation calculation technologies are introduced through the fault analysis module, abnormal points are locked quickly, and the change trend of potential risks is predicted in combination with trend analysis, so that the hidden danger identification and positioning efficiency of the system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of high-voltage switch cabinet fault monitoring, and in particular to a high-voltage switch cabinet fault monitoring and early warning system and method. Background Art

[0002] The existing high-voltage switchgear is an important device for distributing electric energy and protecting power equipment in the power system. It is widely used in power plants, substations and industrial fields. With the continuous expansion of the scale of the power system and the increasing load, the operating reliability of the high-voltage switchgear is directly related to the stability of the entire power system. The traditional high-voltage switchgear fault monitoring method mainly relies on regular manual inspections, but this method has the disadvantages of long detection cycle, limited coverage, and delayed hidden danger discovery, which is difficult to meet the high requirements of modern power systems for safe operation of equipment. In recent years, online monitoring technology has gradually been applied to high-voltage switchgear. Sensors are used to collect parameters such as temperature rise, current, voltage, partial discharge, mechanical vibration, etc. inside the cabinet, and combined with data acquisition and analysis systems to achieve real-time monitoring and early warning of faults.

[0003] However, current technologies still face a number of challenges, such as: insufficient accuracy and comprehensiveness of data collection, and the inability to fully and real-time obtain multi-dimensional data inside the high-voltage switchgear, resulting in a narrow monitoring coverage and difficulty in detecting potential faults or hidden dangers at an early stage; lack of ability to predict fault development trends: many traditional monitoring systems only alarm after a fault occurs, but cannot effectively analyze abnormal change trends and cannot identify early changes in potential risks; inaccurate fault anomaly judgment: existing systems often use simple threshold judgment methods when processing the collected multi-dimensional data, and are unable to conduct in-depth analysis and multi-angle evaluation of the data. Summary of the invention

[0004] The purpose of the present invention is to solve the above-mentioned problems and to provide a high-voltage switch cabinet fault monitoring and early warning system and method.

[0005] The object of the present invention can be achieved by the following technical solutions: A high-voltage switch cabinet fault monitoring and early warning system includes a temperature monitoring module, a live monitoring module and a fault analysis module;

[0006] The temperature monitoring module is used to monitor the temperature of the devices and facilities in the high-voltage switch cabinet; and analyze the correlation between the discharge signals corresponding to the temperature of each device and facility to obtain the linear correlation r between the device and facility temperature and the discharge signal i , if |r i ∣>r0, the temperature is significantly correlated with the discharge signal; then collect the ambient temperature Th and historical data as well as the temperature range of normal operation of the equipment to set the threshold Ty, ΔT i =T i -Th, if ΔT i>Ty, it is judged that the device has temperature abnormality;

[0007] The live monitoring module is used to detect whether the surface of the high-voltage switch cabinet and the device facilities are live, convert the time domain signal to the frequency domain, and calculate the complex value S(fk) of the frequency domain signal at the frequency; calculate the amplitude corresponding to the frequency to obtain the corresponding amplitude Ak 1 , and the frequency corresponding to the maximum amplitude within the corresponding amplitude is the main frequency, and then according to the amplitude and frequency of the frequency signal, it is judged that there is partial discharge or leakage;

[0008] The fault analysis module is used to retrieve the data analyzed by the temperature monitoring module and the live monitoring module, and perform abnormal position locking analysis to obtain the final abnormal point set W;

[0009] The fault analysis module is also used to perform abnormal trend analysis on the abnormal points in the final abnormal point set to generate early warning signals and early warning instructions.

[0010] As a preferred embodiment of the present invention, it also includes a detection and collection module and an early warning execution module;

[0011] The detection and acquisition module obtains the working status data of the high-voltage switch cabinet through the onboard sensors, cleans and formats the collected raw data, and then transmits the processed data to the monitoring center for further analysis;

[0012] The warning execution module is used to receive and generate warning instructions and execute alarms; specifically: obtain warning instructions or warning signals from the fault analysis module or the monitoring center, perform warning intensity calculation to obtain a warning level value YJZ; and issue an alarm according to the warning level value.

[0013] As a preferred implementation manner of the present invention, the specific process of calculating the complex value S(fk) of the frequency domain signal at the frequency is:

[0014] The sampling signal is s(t), the continuous sampling time is T1, and the sampling frequency is fs; then the total number of sampling points N is: N = T × fs; then the signal amplitude is normalized; and then the time domain signal is converted to the frequency domain through fast Fourier transform, the formula is: The complex value S(fk) of the output frequency domain signal at frequency fk, where k=0, 1, ..., N-1, fk=k×fs / N is the corresponding frequency component, and s[n1] is the signal value of the n1th sampling point.

[0015] As a preferred embodiment of the present invention, the specific process of determining the presence of partial discharge or leakage based on the amplitude and frequency of the frequency signal is as follows:

[0016] For a periodic electrical signal, in addition to the main frequency, the signal also contains components at integer multiples of its frequency, which are called harmonics; to extract the harmonic components, through the formula: The amplitude Ax of the n2th harmonic is obtained, where S(n2fmain) is the result of the Fourier transform corresponding to the frequency n2fmain; then, the waveform is analyzed to calculate the effective value, through the formula: The average intensity Ay of the signal is obtained; then, the waveform distortion is detected: The distortion rate SZL is output, K is the highest order of the harmonics, and k2 = 2, 3,..., K; the main frequency signal is judged: judged according to the amplitude Amain and frequency fmain of the frequency signal: if Amain ≥ A0 and |fmain - f1| < f0, the device is energized; f1 is the preset power frequency, A0 is the lowest threshold of the signal amplitude, and f0 is the preset frequency deviation threshold; then, the harmonic abnormality is judged: if There is partial discharge or leakage.

[0017] As a preferred embodiment of the present invention, the specific process of performing abnormal position locking analysis to obtain the final abnormal point set W is as follows:

[0018] If U sensors are arranged in the high-voltage switchgear, the electric field intensity E j (t), temperature difference ΔT j (t), the amplitude A j (t) of the energized signal, and the harmonic ratio H j (t) of each monitoring point are collected; the comprehensive abnormality index S j (t) of each monitoring point is calculated through the established formula: The comprehensive abnormality index S j (t) is output, ω1, ω2, ω3 are the weights of each feature, and A0 j is the expected value of the amplitude of the preset main frequency signal, EO j is the preset electric field intensity response threshold, and H0 j is the response threshold of the harmonic ratio; the obtained comprehensive abnormality index S j (t) is compared with the corresponding threshold of the preset abnormality index, and the corresponding threshold of the preset abnormality index is denoted as SO j , if S j (t) > SO j , it is initially determined that there is an abnormality at position j;

[0019] When the energized signal propagates in space, it attenuates with distance, so the signal source distance is calculated using the signal intensity attenuation model, from the formula: The distance d from the signal source to the sensor j is obtained j , A jis the signal strength received by the jth sensor, A0 is the initial strength of the signal source, and q is the signal attenuation index; the change of the electric field strength E with the spatial coordinates (x, y, z) satisfies: is the electric field gradient, ρ is the space charge density, ε 0 is the vacuum dielectric constant; and according to the distance from the signal source to the sensor j and the electric field measurement value at the sensor arrangement position, the charge distribution is inferred and the position of the charged anomaly is located, which is recorded as the initial position;

[0020] By calculating the amplitude of the charged signal A j (t) and temperature difference ΔT j The correlation of (t) is r j ; The obtained r j Correlation with preset response value r0 j For comparison, if r j >r0 j , it is determined that the abnormality of live signal is related to the abnormal temperature; the time synchronization of abnormal live signal and abnormal temperature is calculated: |tA j -tΔT j |<Δtmax; if the synchronization condition is met, the abnormal point is further locked; combined with the comprehensive abnormal index S j (t), spatial positioning results d j , correlation j And time synchronization, filter the best matching abnormal position, specifically: through the established formula: Output final positioning coefficient P j (t); a1, a2 and a3 are the preset weights of each factor; lock the abnormal points to obtain the final abnormal point set W, W = {j | P j (t)>P0 j}, P0 j It is the preset final positioning coefficient critical value.

[0021] As a preferred implementation of the present invention, the specific process of performing abnormal trend analysis on the abnormal points in the final abnormal point set to generate an early warning signal is as follows:

[0022] Retrieve the number of outliers in the outlier set W and number the outliers. m is the total number of outliers. Then W = {p 1 , p 2 , ..., p m}; From each outlier point p x Collect historical and real-time data, including: electric field strength, charged signal amplitude, harmonic ratio and temperature difference; divide the data into several windows according to time and analyze the trend: Similarly, collect historical and real-time data and calculate the comprehensive abnormal index to obtain Will get Perform curve feature extraction: linear fitting: β 0 is the intercept, indicating the exponential value at the initial time t = 0, β 1 is the slope, The speed of change over time t; if β 1 >0, it is an upward trend and generates an early warning signal; otherwise, a nonlinear trend analysis is performed: γ 0 is the initial value, γ 1 is the coefficient of the first-order term, γ 2 is the quadratic term coefficient, describing the curvature of the nonlinear change; if γ 2 >0, the index changes in an accelerating upward trend, generating a warning signal.

[0023] As a preferred implementation of the present invention, the specific process of performing abnormal trend analysis on the abnormal points in the final abnormal point set to generate an early warning instruction is:

[0024] If an early warning signal is generated, and the fluctuation range and periodicity are calculated: the fluctuation range is Indicates the maximum range of variation of the comprehensive abnormality index; is the maximum value of the abnormal index; is the minimum value of the anomaly index, if Generate early warning instructions; analyze periodicity: through Fourier transform formula: The Fourier transform result F(υ) at the output frequency υ; e -jυt is the frequency basis function; then the main frequency υmax of the signal is queried through the spectrum diagram, and the period corresponding to the main frequency is: The period T1 corresponding to the main frequency is compared with the period corresponding to the preset normal working main frequency. If the similarity between the two does not reach 99%, an early warning instruction is generated.

[0025] As a preferred implementation of the present invention, the specific process of calculating the warning intensity to obtain the warning level value YJZ is:

[0026] By setting up the formula: Output warning level value YJZ, where c1, c2, c3 and c4 are preset weights, and xsd is the similarity between the two.

[0027] A high-voltage switch cabinet fault monitoring and early warning method, the method comprising:

[0028] Step 1: The detection and acquisition module obtains working status data through sensors; cleans and formats the raw data, and transmits the processed data to the monitoring center for further analysis;

[0029] Step 2: Real-time monitoring of device temperature and calculation of the correlation between temperature and discharge signal r j , and judge the temperature abnormality and combine it with the discharge signal analysis;

[0030] Step 3: Detect the electric field signal, extract the main frequency and harmonics; calculate the effective value and distortion rate, and determine whether the equipment is energized and the harmonics are abnormal;

[0031] Step 4: Calculate the comprehensive abnormality index S j (t) Determine the abnormal points; use signal attenuation and electric field gradient for spatial positioning; analyze correlation and time synchronization, and lock the final abnormal point set;

[0032] Step 5: Extract historical and real-time data trends; calculate fluctuation amplitude and periodic characteristics, and identify periodic anomalies;

[0033] Step 6: Calculate the warning level based on comprehensive indicators, implement low, medium and high level alarms, and provide corresponding warning light prompts.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] 1. The present invention acquires key data such as environmental parameters, temperature changes, electric field signals and discharge characteristics of high-voltage switchgear in real time through detection and acquisition modules, temperature monitoring modules and live monitoring modules. It uses technical means such as normalization processing, Fourier transform and correlation analysis to accurately evaluate the abnormal conditions of various parameters, and makes comprehensive judgments based on multiple indicators, effectively improving the coverage and accuracy of monitoring.

[0036] 2. Through the fault analysis module, the system uses spatial positioning, time synchronization and correlation calculation technologies to quickly lock abnormal points, and combines abnormal trend analysis to identify the changing trend of potential risks (such as rising or accelerating trends). This feature ensures the scientificity and timeliness of the generation of early warning signals, and helps to take measures in advance to avoid the expansion of faults.

[0037] 3. The present invention adopts a multi-level warning mechanism through the warning execution module, matches different response strategies according to the warning level value (YJZ), and gradually upgrades from low to high level, including warning light prompts, alarm frequency adjustment and other functions. This grading method can not only meet the needs of routine maintenance, but also issue strong warnings in emergency situations, thereby improving the safety and reliability of system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0039] Figure 1 It is a principle block diagram of the present invention;

[0040] Figure 2 It is a diagram of the method steps of the present invention. DETAILED DESCRIPTION

[0041] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] It should be understood that the terms “include” and “comprising” used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0043] It should also be understood that the terms used in this disclosure are only for the purpose of describing specific embodiments and are not intended to limit the disclosure. As used in this disclosure and claims, the singular forms of "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" used in this disclosure and claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations.

[0044] See also Figure 1 As shown, the present invention provides a high-voltage switch cabinet fault monitoring and early warning system on one hand, including a detection and acquisition module, a temperature monitoring module, a live monitoring module, a fault analysis module and an early warning execution module;

[0045] The detection and acquisition module obtains the working status data of the high-voltage switchgear through the sensors carried by it. The working status data includes: environmental parameters, partial discharge signals, switch status and vibration signals, etc.; the collected raw data is cleaned and formatted, and the processed data is transmitted to the monitoring center for further analysis;

[0046] The temperature monitoring module is used to monitor the temperature of the devices and facilities in the high-voltage switch cabinet. If there are n devices and facilities in the high-voltage switch cabinet, and n = 1, 2, 3, ..., n; then the partial discharge signals of n devices and facilities are retrieved from the monitoring center, and the correlation between the discharge signals corresponding to the temperature of each device and facility is calculated: first, the discharge signal D i and temperature data T i After normalization, the formula is: Output the linear correlation r between the temperature of the i-th device and the discharge signal i , and is the mean of the corresponding data, and then set a threshold | r i |>r0, determine whether the discharge signal is significantly correlated with the temperature change (usually, this threshold is determined based on the sensitivity of the actual application scenario and empirical data); if |r i ∣<r0, there is no significant correlation between temperature and discharge signal, which may indicate that the discharge signal is more affected by other factors (such as equipment failure); if ∣r i ∣>r0, the temperature is significantly correlated with the discharge signal, indicating that the discharge signal of the device is mainly caused by the temperature rise; then the threshold Ty is set according to the collected ambient temperature Th and historical data as well as the temperature range of normal operation of the equipment, and the temperature of each device facility is compared: ΔT i =T i -Th, if ΔT i >Ty, it is judged that the device has temperature abnormality;

[0047] Combined with the discharge signal, if the temperature is abnormal and D i If the discharge signal strength exceeds the normal range, it is judged that the device has a serious partial discharge problem; if the temperature is abnormal but D i If the signal is normal, it may be caused by other factors (such as environmental factors or poor heat dissipation);

[0048] The live monitoring module is used to detect whether the surface of the high-voltage switchgear and the device facilities are live. The specific detection process is as follows:

[0049] Use non-contact sensors to collect electric field signals on the surface of high-voltage switchgear and equipment facilities; if the sampling signal is s(t), the continuous sampling time is T1, and the sampling frequency is fs; then the total number of sampling points N is: N = T × fs; then normalize the signal amplitude; and then convert the time domain signal to the frequency domain through fast Fourier transform, the formula is: Output the complex value S(fk) of the frequency domain signal at frequency fk, where k = 0, 1, ..., N-1, fk = k × fs / N is the corresponding frequency component, and s[n1] is the signal value of the n1th sampling point; then calculate the frequency fk 1 The corresponding amplitude Ak 1 ; Through the formula: Output corresponding amplitude Ak 1 , only take k=k 1 The result is that the negative frequency part is symmetrically redundant; the frequency corresponding to the maximum amplitude within the corresponding amplitude is obtained as the main frequency: It is obtained that, where fmain is the main frequency, representing the basic frequency component of the signal, and Amain is the amplitude of this frequency, representing the intensity of the signal at this frequency; it should be noted that: for a periodic electrical signal, in addition to the fundamental wave (main frequency), the signal usually also contains components with integer multiples of its frequency, which are called harmonics; for example, for a fundamental frequency signal of 50 Hz, 100 Hz, 150 Hz, 200 Hz, etc. are its harmonic components; to extract the harmonic components, through the formula: The amplitude Ax of the n2th harmonic is obtained, where S(n2fmain) is the Fourier transform result corresponding to the frequency n2fmain;

[0050] Then, analyze the waveform and calculate the effective value (the effective value of the signal reflects the average intensity) through the formula: The average intensity Ay of the signal is obtained; then, detect the waveform distortion (the distortion rate is used to analyze the deviation degree of the waveform from the standard sine wave): The distortion rate SZL is output, K is the highest order of the harmonics, and k2 = 2, 3,..., K;

[0051] Judge the main frequency signal: Make a judgment based on the amplitude Amain and frequency fmain of the frequency signal: If Amain ≥ A0 and |fmain - f1| < f0, the device is energized; f1 is the preset power frequency, A0 is the lowest threshold of the signal amplitude, and f0 is the preset frequency deviation threshold; then, judge the harmonic abnormality: If Then there is partial discharge or leakage, and H0 is usually set to 10%;

[0052] The fault analysis module is used to retrieve the data analyzed by the temperature monitoring module and the live monitoring module to lock the abnormal position:

[0053] If U sensors are arranged in the high-voltage switchgear to collect the electric field intensity E j (t) (the change of the electric field intensity at the jth position with time), the temperature difference ΔT j (t) (the difference between the device temperature and the ambient temperature), the live signal amplitude A j (t) (the main frequency amplitude of the detected live signal) and the harmonic ratio H j (t) (the ratio of the harmonic amplitude to the main frequency amplitude in the live signal); calculate the comprehensive anomaly index S j (t) at each monitoring point through the established formula: The comprehensive anomaly index S j (t) is output, where ω1, ω2, ω3 are the weights of each feature, A0 j is the expected value of the preset main frequency signal amplitude, EO j is the preset electric field intensity response threshold, H0 jis the response threshold of the harmonic ratio; the comprehensive abnormal index S j (t) is compared with the preset abnormal index corresponding threshold, and the preset abnormal index corresponding threshold is recorded as SO j , if S j (t)>SO j , it is preliminarily determined that there is an abnormality at position j; it should be noted that: the charged signal will attenuate with distance when propagating in space, so the signal source distance is calculated by using the signal strength attenuation model, according to the formula: Get the distance d from the signal source to the sensor j j , A j is the signal strength received by the jth sensor, A0 is the initial strength of the signal source, q is the signal attenuation index, usually 2; the change of the electric field strength E with the spatial coordinates (x, y, z) satisfies: in, is the electric field gradient, ρ is the space charge density, ε 0 is the vacuum dielectric constant; and according to the distance from the signal source to the sensor j and the electric field measurement value at the sensor arrangement position, the charge distribution is inferred and the position of the charged anomaly is located, which is recorded as the initial position;

[0054] Then, by calculating the amplitude of the charged signal A j (t) and temperature difference ΔT j The correlation of (t) is r j ; and the obtained r j Correlation with preset response value r0 j For comparison, if r j >r0 j , it is determined that the abnormality of live signal is related to the abnormal temperature; the time synchronization of abnormal live signal and abnormal temperature is: |tA j -tΔT j |<Δtmax; if the synchronization condition is met, the abnormal point is further locked;

[0055] Finally, combined with the comprehensive abnormality index S j (t), spatial positioning results d j , correlation j And time synchronization, filter the best matching abnormal position, specifically: through the established formula: Output final positioning coefficient P j (t); a1, a2 and a3 are the preset weights of each factor; lock the abnormal point: the final abnormal point set W = {j|P j (t)>P0 j}; P0 j It is the preset final positioning coefficient critical value.

[0056] The fault analysis module is also used to perform abnormal trend analysis on the abnormal points in the final abnormal point set; specifically:

[0057] Retrieve the number of outliers in the outlier set W and number the outliers. m is the total number of outliers. Then W = {p 1 , p 2 , ..., p m}, and x = 1, 2, 3, ... m; and from each abnormal point p x Collect historical and real-time data, including: electric field strength Live signal amplitude Harmonic ratio and temperature difference Divide the data into several windows according to time and analyze the trend: Similarly, calculate the comprehensive abnormal index of the collected historical and real-time data To get Perform curve feature extraction: linear fitting: Among them, β 0 is the intercept, indicating the exponential value at the initial time t = 0, β 1 is the slope, The speed of change over time t; if β 1 >0, it is an upward trend and generates an early warning signal; otherwise, a nonlinear trend analysis is performed: γ 0 is the initial value, similar to the intercept in the linear model, γ 1 is the coefficient of the first-order term, indicating the initial change rate, γ 2 is the quadratic term coefficient, describing the curvature of the nonlinear change; if γ 2 >0, the index changes in an accelerating upward trend (the parabola opens upward), generating a warning signal;

[0058] If an early warning signal is generated, and the fluctuation range and periodicity are calculated, specifically:

[0059] The fluctuation range is Indicates the maximum range of variation of the comprehensive abnormal index; is the maximum value of the abnormal index; is the minimum value of the anomaly index, if This indicates that the state of the abnormal point changes dramatically, there is intermittent or periodic external interference, and an early warning instruction is generated;

[0060] Then analyze the periodicity: through the Fourier transform formula: The Fourier transform result F(υ) at the output frequency υ represents the signal strength at this frequency; e -jυtis the frequency basis function, which is used to map the time signal to the frequency domain. After Fourier transform, the main frequency υmax of the signal is found through the spectrum diagram, and the period corresponding to the main frequency is: The period T1 corresponding to the main frequency is compared with the period corresponding to the main frequency of the preset normal working state. If the similarity between the two does not reach 99%, an early warning instruction is generated.

[0061] The warning execution module is used to receive and generate warning instructions and execute alarms; specifically:

[0062] Obtain the warning instructions or warning signals from the fault analysis module or monitoring center and calculate the warning intensity by setting up the formula: Output the warning level value YJZ, where c1, c2, c3 and c4 are their respective corresponding preset weights, and 1=c1+c2+c3+c4; xsd is the similarity between the two; compare the obtained warning level value YJZ with the corresponding preset level interval, the preset level interval includes: low-level warning interval, medium-level warning interval and high-level warning interval; if the warning level value YJZ belongs to the low-level warning interval, a short low-frequency alarm is executed, the LED warning light is started to flash green, and attention is prompted; if the warning level value YJZ belongs to the medium-level warning interval, a continuous medium-frequency alarm is executed, the LED warning light is started to flash yellow, and a warning is prompted; if the warning level value YJZ belongs to the high-level warning interval, an urgent high-frequency alarm is executed, the LED warning light is started to continuously light up in red, and an emergency is prompted.

[0063] See also Figure 2 As shown, another aspect of the present invention provides a high-voltage switch cabinet fault monitoring and early warning method, the steps are:

[0064] Step 1: The detection and acquisition module obtains working status data through sensors, including environmental parameters, partial discharge signals, switch status, vibration signals, etc., cleans and formats the raw data, and transmits the processed data to the monitoring center for further analysis;

[0065] Step 2: Real-time monitoring of device temperature and calculation of the correlation between temperature and discharge signal r j , and judge the temperature abnormality and combine it with the discharge signal analysis;

[0066] Step 3: Detect the electric field signal, extract the main frequency and harmonics; calculate the effective value, distortion rate, etc., to determine whether the equipment is energized and the harmonics are abnormal;

[0067] Step 4: Calculate the comprehensive abnormality index S j (t) Determine the abnormal points; use signal attenuation and electric field gradient for spatial positioning; analyze correlation and time synchronization, and lock the final abnormal point set;

[0068] Step 5: Extract historical and real-time data trends; calculate fluctuation amplitude and periodic characteristics, and identify periodic anomalies;

[0069] Step 6: Calculate the warning level based on comprehensive indicators, implement low, medium and high level alarms, and provide corresponding warning light prompts.

[0070] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A high-voltage switch cabinet fault monitoring and early warning system, comprising a temperature monitoring module, a live monitoring module and a fault analysis module; characterized in that: The temperature monitoring module is used to monitor the temperature of the devices and facilities in the high-voltage switch cabinet; and analyze the correlation between the discharge signals corresponding to the temperature of each device and facility to obtain the linear correlation r between the device and facility temperature and the discharge signal i , if |r i |>r0, the temperature is significantly correlated with the discharge signal; Then collect the ambient temperature Th and historical data as well as the temperature range for normal operation of the equipment to set the threshold Ty, ΔT i =T i -Th, if ΔT i >Ty, it is judged that the device has temperature abnormality; The live monitoring module is used to detect whether the surface of the high-voltage switch cabinet and the device facilities are live, convert the time domain signal to the frequency domain, and calculate the complex value S(fk) of the frequency domain signal at the frequency; calculate the amplitude corresponding to the frequency to obtain the corresponding amplitude Ak1, and the frequency corresponding to the maximum amplitude within the corresponding amplitude is the main frequency, and then judge the existence of partial discharge or leakage based on the amplitude and frequency of the frequency signal; The fault analysis module is used to retrieve the data analyzed by the temperature monitoring module and the live monitoring module, and perform abnormal position locking analysis to obtain the final abnormal point set W; The fault analysis module is also used to perform abnormal trend analysis on the abnormal points in the final abnormal point set to generate early warning signals and early warning instructions.

2. A high-voltage switch cabinet fault monitoring and early warning system according to claim 1, characterized in that: It also includes a detection and acquisition module and an early warning execution module; The detection and acquisition module obtains the working status data of the high-voltage switch cabinet through the onboard sensors, cleans and formats the collected raw data, and then transmits the processed data to the monitoring center for further analysis; The warning execution module is used to receive and generate warning instructions and execute alarms; specifically: obtain warning instructions or warning signals from the fault analysis module or the monitoring center, perform warning intensity calculation to obtain a warning level value YJZ; and issue an alarm according to the warning level value.

3. A high-voltage switch cabinet fault monitoring and early warning system according to claim 1, characterized in that: The specific process of calculating the complex value S(fk) of the frequency domain signal at the frequency is: The sampling signal is s(t), the continuous sampling time is T1, and the sampling frequency is fs; then the total number of sampling points N is: N = T × fs; then the signal amplitude is normalized; and then the time domain signal is converted to the frequency domain through fast Fourier transform, the formula is: The complex value S(fk) of the output frequency domain signal at frequency fk, where k=0, 1, ..., N-1, fk=k×fs / N is the corresponding frequency component, and s[n1] is the signal value of the n1th sampling point.

4. A high-voltage switch cabinet fault monitoring and early warning system according to claim 3, characterized in that: The specific process of determining the presence of partial discharge or leakage based on the amplitude and frequency of the frequency signal is as follows: For a periodic electrical signal, in addition to the main frequency, the signal also contains components at integer multiples of its frequency, which are called harmonics; the harmonic components are extracted through the formula: The amplitude Ax of the n2th harmonic is obtained, where S(n2fmain) is the result of the Fourier transform corresponding to the frequency n2fmain; then the waveform is analyzed and the effective value is calculated through the formula: The average intensity Ay of the signal is obtained; then the waveform distortion is detected: The distortion rate SZL is output, K is the highest order of the harmonics, and k2 = 2, 3,..., K; the main frequency signal is judged: it is judged according to the amplitude Amain and the frequency fmain of the frequency signal: if Amain ≥ A0 and |fmain - f1| < f0, the device is energized; f1 is the preset power frequency, A0 is the lowest threshold of the signal amplitude, and f0 is the preset frequency deviation threshold; then the harmonic abnormality is judged: if then there is partial discharge or leakage.

5. A high-voltage switch cabinet fault monitoring and early warning system according to claim 1, characterized in that: The specific process of performing abnormal position locking analysis to obtain the final abnormal point set W is as follows: If U sensors are arranged in the high-voltage switch cabinet, the electric field strength E at each monitoring point is collected. j (t), temperature difference ΔT j (t), the amplitude of the charged signal A j (t) Harmonic wave ratio H j (t); Calculate the comprehensive abnormal index S for each monitoring point j (t), by establishing the formula: Output comprehensive abnormality index S j (t), ω1, ω2, ω3 are the weights of each feature, A0 j is the expected value of the preset main frequency signal amplitude, EO j is the preset electric field intensity response threshold, H0 j is the response threshold of the harmonic ratio; the comprehensive abnormal index S j (t) is compared with the preset abnormal index corresponding threshold, and the preset abnormal index corresponding threshold is recorded as SO j , if S j (t)>SO j , it is preliminarily determined that there is an abnormality at position j; The charged signal attenuates with distance when propagating in space, so the signal strength attenuation model is used to calculate the signal source distance, according to the formula: Get the distance d from the signal source to the sensor j j , A j is the signal strength received by the jth sensor, A0 is the initial strength of the signal source, and q is the signal attenuation index; the change of the electric field strength E with the spatial coordinates (x, y, z) satisfies: is the electric field gradient, ρ is the space charge density, and ε0 is the vacuum dielectric constant; and according to the distance from the signal source to the sensor j and the electric field measurement value at the sensor arrangement position, the charge distribution is inferred and the position of the charged anomaly is located, which is recorded as the initial position; By calculating the amplitude of the charged signal A j (t) and temperature difference ΔT j The correlation of (t) is r j ; The obtained r j Correlation with preset response value r0 j For comparison, if r j >r0 j , it is determined that the abnormality of live signal is related to the abnormal temperature; the time synchronization of abnormal live signal and abnormal temperature is calculated: |tA j -tΔT j |<Δtmax; if the synchronization condition is met, the abnormal point is further locked; combined with the comprehensive abnormal index S j (t), spatial positioning results d j , correlation j And time synchronization, filter the best matching abnormal position, specifically: through the established formula: Output final positioning coefficient P j (t); a1, a2 and a3 are the preset weights of each factor; lock the abnormal points to obtain the final abnormal point set W, W = {j | P j (t)>P0 j }, P0 j It is the preset final positioning coefficient critical value.

6. A high-voltage switch cabinet fault monitoring and early warning system according to claim 5, characterized in that: The specific process of performing abnormal trend analysis on the abnormal points in the final abnormal point set to generate an early warning signal is as follows: Retrieve the number of outliers in the outlier set W and number the outliers. m is the total number of outliers. Then W = {p1, p2, ..., p m }; From each outlier point p x Collect historical and real-time data, including: electric field strength, charged signal amplitude, harmonic ratio and temperature difference; divide the data into several windows according to time and analyze the trend: Similarly, collect historical and real-time data and calculate the comprehensive abnormal index to obtain Will get Perform curve feature extraction: linear fitting: β0 is the intercept, which represents the exponential value at the initial time t=0, and β1 is the slope. The speed of change over time t; if β1>0, it is an upward trend and an early warning signal is generated; otherwise, a nonlinear trend analysis is performed: γ0 is the initial value, γ1 is the linear term coefficient, and γ2 is the quadratic term coefficient, which describes the degree of curvature of the nonlinear change; if γ2>0, the exponential change shows an accelerating upward trend, generating a warning signal.

7. A high-voltage switch cabinet fault monitoring and early warning system according to claim 6, characterized in that: The specific process of performing abnormal trend analysis on the abnormal points in the final abnormal point set and generating early warning instructions is as follows: If an early warning signal is generated, and the fluctuation range and periodicity are calculated: the fluctuation range is Indicates the maximum range of variation of the comprehensive abnormality index; is the maximum value of the abnormal index; is the minimum value of the anomaly index, if Generate early warning instructions; analyze periodicity: through Fourier transform formula: The Fourier transform result F(υ) at the output frequency υ; e -jυt is the frequency basis function; then the main frequency υmax of the signal is queried through the spectrum diagram, and the period corresponding to the main frequency is: The period T1 corresponding to the main frequency is compared with the period corresponding to the preset normal working main frequency. If the similarity between the two does not reach 99%, an early warning instruction is generated.

8. A high-voltage switch cabinet fault monitoring and early warning system according to claim 2, characterized in that: The specific process of calculating the warning intensity to obtain the warning level value YJZ is as follows: By setting up the formula: Output warning level value YJZ, where c1, c2, c3 and c4 are preset weights, and xsd is the similarity between the two.

9. A high-voltage switch cabinet fault monitoring and early warning method, characterized in that: Applied to implement any one of claims 1-8 of the high-voltage switch cabinet fault monitoring and early warning system, the method comprises: Step 1: The detection and acquisition module obtains working status data through sensors; cleans and formats the raw data, and transmits the processed data to the monitoring center for further analysis; Step 2: Real-time monitoring of device temperature and calculation of the correlation between temperature and discharge signal r j , and judge the temperature abnormality and combine it with the discharge signal analysis; Step 3: Detect the electric field signal, extract the main frequency and harmonics; calculate the effective value and distortion rate, and determine whether the equipment is energized and the harmonics are abnormal; Step 4: Calculate the comprehensive abnormality index S j (t) Determine the abnormal points; use signal attenuation and electric field gradient for spatial positioning; analyze correlation and time synchronization, and lock the final abnormal point set; Step 5: Extract historical and real-time data trends; calculate fluctuation amplitude and periodic characteristics, and identify periodic anomalies; Step 6: Calculate the warning level based on comprehensive indicators, implement low, medium and high level alarms, and provide corresponding warning light prompts.

Images