Isolation switch mechanical defect early warning method and system
By dynamically integrating vibration harmonic characteristics and temperature changes, and adaptively adjusting the weights of the disconnecting switch early warning method, the accuracy of early warning has been improved and the false alarm rate has been reduced, thus achieving efficient monitoring of mechanical defects in disconnecting switches.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-01-21
- Publication Date
- 2026-04-17
AI Technical Summary
Existing technologies for monitoring the status of disconnecting switches have low accuracy in early warning and high false alarm rate. They fail to effectively utilize temperature changes and vibration harmonic characteristics in the contact area, and the weight adjustment is not dynamic and adaptive enough.
Data is collected using a piezoelectric vibration acceleration sensor and an infrared temperature sensor. Low-frequency vibration signals and temperature difference values in the contact area are extracted through high-frequency filtering. A dynamic weight vector is constructed, and the feature weights are adaptively adjusted. An early warning index is calculated by combining information entropy to determine early faults.
It improved the accuracy of early warning to 92.3% and reduced the false alarm rate to 2.8%, and can effectively monitor various mechanical defects, such as contact jamming, connecting rod loosening and bearing wear.
Smart Images
Figure CN121878449A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to early warning of power equipment faults, and in particular to an early warning method and system for mechanical defects in disconnect switches. Background Technology
[0002] Disconnecting switches are critical switching equipment in power systems, and the reliability of their mechanical components (such as contacts and connecting rods) directly affects power grid safety. Mechanical defects (such as contact jamming or connecting rod loosening) that are not detected early can easily lead to equipment failure to operate, arcing faults, or even power outages.
[0003] Currently, there are two main technical approaches for monitoring the status of disconnecting switches: single-parameter monitoring method: analyzing vibration amplitude or frequency characteristics (such as total vibration energy) through vibration acceleration sensors, but without considering the indicative role of temperature changes in the contact area on the mechanical status; multi-parameter fusion method: some systems attempt to collect vibration and temperature data simultaneously, but use fixed weights (such as equal weights) for feature fusion (e.g., vibration energy weight = temperature weight = 0.5), without dynamically adjusting the weights according to real-time operating conditions.
[0004] Therefore, existing technologies suffer from two significant drawbacks: low accuracy and high false alarm rate. Thus, there is an urgent need for an early warning method that can dynamically integrate vibration harmonic characteristics with relative temperature changes and adaptively adjust feature weights to overcome the bottlenecks in accuracy and adaptability of existing technologies. Summary of the Invention
[0005] This application provides a method and system for early warning of mechanical defects in disconnect switches. The invention addresses the shortcomings of existing methods, such as low accuracy and high false alarm rate, by providing an early warning method that dynamically integrates vibration harmonic characteristics with relative temperature changes and adaptively adjusts the characteristic weights.
[0006] The objective of this invention is achieved through the following technical solutions: An early warning method for mechanical defects in disconnect switches, characterized by the following steps: Step 1: Start the high-speed data acquisition card to acquire the output signal of the piezoelectric vibration acceleration sensor attached to the housing of the disconnect switch and the output signal of the infrared temperature sensor that can measure the temperature of the contact area of the disconnect switch. Then, perform high-frequency filtering on the two output signals. The data after high-frequency filtering retains only the low-frequency vibration signal of the housing of the disconnect switch and the relative temperature difference value of the contact area of the disconnect switch. Step 2: Calculate the total vibration energy and vibration harmonic distortion rate from the low-frequency vibration signal; Step 3: Repeat steps 1 and 2. N Next, among them NThe value is an integer power of 2, and N≥16, and will be repeated. N The data obtained this time constitutes a N A matrix of 3 rows and 3 columns, wherein the data in the first to third columns are the total vibration energy, vibration harmonic distortion rate and the relative temperature difference of the contact area of the disconnecting switch, respectively, and the data in each column are normalized and weighted. Step 4: Calculate using normalization and proportion. N A matrix with 3 rows and 3 columns forms a dynamic weight vector; Step 5: Use the aforementioned dynamic weight vector with the first... N A warning index is calculated by multiplying the row matrix data and then performing a weighted sum. Step Six: Compare the aforementioned warning index with the preset warning threshold. If the warning index is higher than the preset warning threshold, the mechanical components of the disconnect switch are determined to be in an early fault state; otherwise, they are in a normal state.
[0007] Preferably, the calculation formulas for "obtaining the total vibration energy and vibration harmonic distortion rate by processing the low-frequency vibration signal" are as follows: in, It is the total vibrational energy. It is the vibration harmonic distortion rate, It is the first vibration signal n A discrete value n The value range is 1 to N , A 1 represents the amplitude of the 100Hz fundamental frequency component of the vibration signal after FFT transformation. A k The amplitude of each high-frequency harmonic component above 100Hz.
[0008] Preferably, the "will repeat" N The data obtained this time constitutes a N The formulas for calculating "a matrix with 3 rows and 3 columns, and normalizing and weighting the data in each column" are as follows: in, It is normalized matrix data. x ij For the repeat N The data obtained this time constitutes a N The first row of a 3-column matrix i row and number j Column data, max(x j ) for in the N In a matrix with 3 rows and 3 columns, the first j The maximum value and minimum value of the data in the column. x j ) for in the N In a matrix with 3 rows and 3 columns, the first j Minimum value of the column data P ij This is the matrix data after the weight calculation.
[0009] Preferably, the "using the dynamic weight vector and the first" N The calculation method for the "early warning index" obtained by multiplying the row matrix data and then performing a weighted summation is as follows: Set a vibration energy noise floor threshold a First, determine the current total vibrational energy. E RMS Is it less than the noise floor threshold? a ;like E RMS < a If so, the current vibration harmonic distortion rate will be forcibly set to 0, and the dynamic weight vector will be forcibly set to balanced weights to suppress false alarms caused by system static noise; if E RMS > a Perform the calculation: in, E j The first j Information entropy of each indicator, when j When =1, it is the information entropy of the total vibration energy; when j When = 2, it is the information entropy of the vibration harmonic distortion rate and when j When =3, it represents the information entropy of the relative temperature difference value in the contact area of the disconnector switch. D j For the first j The coefficient of difference for each indicator W j For the first j The dynamic weights of each indicator H This is an early warning index.
[0010] Furthermore, the present invention also provides an early warning system for mechanical defects in disconnect switches, characterized in that it includes the following modules: High-frequency filtering module: used to perform high-frequency filtering on the output signal of the piezoelectric vibration acceleration sensor and the output signal of the infrared temperature sensor of the contact area of the disconnect switch; The module for calculating the total vibration energy and vibration harmonic distortion rate of the low-frequency vibration signal is used to obtain the total vibration energy and vibration harmonic distortion rate of the isolating switch housing from the output signal of the piezoelectric vibration acceleration sensor. Matrix normalization module: used to eliminate differences between physical dimensions of different indicators, while preventing overflow in subsequent logarithmic operations through non-zero shift processing, and providing compliant input data for subsequent entropy weight calculation. Dynamic weight vector calculation module: It is used to evaluate the sensitivity of each feature index in the current matrix in real time based on real-time data, and automatically generate dynamic weights and then perform weighted summation with real-time data to obtain the early warning index. Early warning judgment module: used to perform threshold logic judgment to distinguish between early latent faults and normal state. The output of the high-frequency filtering module is connected to the input of the total vibration energy and vibration harmonic distortion rate calculation module. The output of the total vibration energy and vibration harmonic distortion rate calculation module is connected to the input of the matrix normalization module. The output of the matrix normalization module is connected to the input of the dynamic weight vector calculation module. The output of the dynamic weight vector calculation module is connected to the input of the early warning judgment module. Attached Figure Description
[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the prior art and embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0012] Figure 1 A flowchart of an early warning method for mechanical defects in disconnect switches provided by the present invention.
[0013] Figure 2 This is the output result of step 1 in the early warning method for mechanical defects of disconnect switches.
[0014] Figure 3 This is the output result of step 2 in the early warning method for mechanical defects of disconnect switches.
[0015] Figure 4 This is the output result of step 4 in the early warning method for mechanical defects of disconnect switches.
[0016] Figure 5 This is the output of step 6 in the early warning method for mechanical defects in disconnect switches. Detailed Implementation
[0017] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.
[0018] This embodiment provides an early warning method for mechanical defects in disconnect switches based on the dynamic entropy weight method, such as... Figure 1 As shown, this method uses a piezoelectric vibration acceleration sensor and an infrared temperature sensor to achieve real-time monitoring of the mechanical condition of the disconnector and early fault warning. The specific implementation process is as follows: Step 1: Data Acquisition and High-Frequency Filtering The system activates the high-speed data acquisition card, acquiring mechanical vibration signals via a piezoelectric vibration accelerometer mounted on the surface of the disconnector switch housing, and simultaneously acquiring temperature signals via an infrared temperature sensor aligned with the contact area. The raw signals contain significant ambient noise; this step performs high-frequency filtering on the vibration and temperature signals, retaining only low-frequency vibration signals (below 100Hz) that reflect mechanical movement characteristics, and the relative temperature difference values of the disconnector switch contact area. The filtered data contains only features sensitive to mechanical conditions, avoiding high-frequency noise interference. Step 2: Vibration Characteristic Calculation The filtered low-frequency vibration signal was analyzed in both the time and frequency domains to calculate the following two core characteristic indices: Total Energy of Vibration (ERMS): Reflects the overall intensity of vibration, and is calculated using the following formula: in, x[n] It is the first vibration signal n A discrete sampled value, N This represents the number of sampling points. Vibration harmonic distortion rate THD Calculation: Reflects the degree of distortion in the vibration waveform and is used to identify nonlinear vibrations caused by mechanical loosening or jamming. The calculation formula is: in, A 1 The amplitude of the fundamental frequency component after the vibration signal undergoes a fast Fourier transform. A k This represents the amplitude of the higher harmonic components. Step 3: Feature Matrix Construction and Normalization Repeat steps one and two a total of N times (N is an integer power of 2 and N≥16), and construct an N×3 historical feature matrix X from the three feature indicators obtained from the N collections and calculations: First column of the matrix: Total vibrational energy data sequence Second column of the matrix: Vibration harmonic distortion rate data sequence Matrix third column: Data sequence of relative temperature difference values in the contact area Because energy, distortion rate, and temperature have different physical dimensions, they need to be dimensionless. A range standardization method is used. j Liede i row data x ij The normalization formula is: in, It is the normalized data, max( x j ) and min( x j ) are the maximum and minimum values of the data in this column, respectively. Step 4: Calculation of Dynamic Weight Vector Noise floor suppression determination: Set a vibration energy noise floor threshold α. If the current total vibration energy... E RMS <α indicates that the disconnecting switch is in a static or extremely low background noise environment. In this case, the vibration harmonic distortion rate is forcibly set to 0, and the dynamic weight vector is set to a balanced weight [1 / 3, 1 / 3, 1 / 3] to prevent false alarms caused by static noise. Information entropy calculation: If E RMS If ≥α, then calculate the first... j Information entropy of each indicator E j in, P ij For the first j The first indicator i The proportion of each value is expressed as: Difference coefficient and dynamic weight: Calculation of the first j Coefficient of difference of each indicator D j and dynamic weights W j : Finally, the dynamic weight vector is obtained. W =[ W 1, W 2, W3], corresponding to the weights of vibration energy, harmonic distortion rate, and relative temperature difference value respectively. Step Five: Early warning index calculation Use the calculated dynamic weight vector W to perform weighted summation with the most recent normalized feature data to obtain the early warning index H : where, is the normalized data in the N row and the jth column of the matrix. Step Six: Early warning determination Compare the calculated early warning index H with the preset early warning threshold T as follows: If H ≥ T, it is determined that the mechanical components of the disconnector are in an early failure state or there is an abnormal trend, and the system outputs an early warning signal; If H < T, it is determined that the state is normal. Subsequently, the system sends the early warning signal to the monitoring terminal through the network to prompt the operation and maintenance personnel to conduct inspections.
[0019] This embodiment is verified by simulation in a certain 500 kV substation. The simulation results show that compared with the existing technology (fixed weight fusion method): Early warning accuracy rate: increased to 92.3% (the traditional method is 77.5%) False alarm rate: reduced to 2.8% (the traditional method is 10.6%) Defect coverage: can effectively monitor multiple types of mechanical defects such as contact jamming, connecting rod looseness, and bearing wear simultaneously.
Claims
1. A method for early warning of mechanical defects in disconnect switches, characterized by the following steps: Step 1: Start the high-speed data acquisition card to acquire the output signal of the piezoelectric vibration acceleration sensor attached to the housing of the disconnect switch and the output signal of the infrared temperature sensor that can measure the temperature of the contact area of the disconnect switch. Then, perform high-frequency filtering on the two output signals. The data after high-frequency filtering retains only the low-frequency vibration signal of the housing of the disconnect switch and the relative temperature difference value of the contact area of the disconnect switch. Step 2: Calculate the total vibration energy and vibration harmonic distortion rate from the low-frequency vibration signal; Step three: repeat step one and step two N times, wherein N the value of N is an integer power of 2, and N≥16, the data obtained from the repetition of N times constitute a N 3-column matrix, wherein the data in the first column to the third column are the total vibration energy, the vibration harmonic distortion rate and the relative temperature difference value of the contact area of the disconnecting switch respectively, and normalization and specific gravity calculation processing are performed on each column of data; Step 4: Calculate using normalization and proportion. N A matrix with 3 rows and 3 columns forms a dynamic weight vector; Step 5: Use the aforementioned dynamic weight vector with the first... N A warning index is calculated by multiplying the row matrix data and then performing a weighted sum. Step Six: Compare the aforementioned warning index with the preset warning threshold. If the warning index is higher than the preset warning threshold, the mechanical components of the disconnect switch are determined to be in an early fault state; otherwise, they are in a normal state.
2. The method for early warning of mechanical defects in disconnect switches according to claim 1, characterized in that, The calculation formulas for "obtaining the total vibration energy and vibration harmonic distortion rate by processing the low-frequency vibration signal" are as follows: ; ； in, It is the total vibrational energy. It is the vibration harmonic distortion rate, It is the first vibration signal n A discrete value, n The value range is 1 to N , A 1 represents the amplitude of the 100Hz fundamental frequency component of the vibration signal after FFT transformation. A k The amplitude of each high-frequency harmonic component above 100Hz.
3. The method for early warning of mechanical defects in disconnecting switches according to claim 1, characterized in that, The phrase "will repeat" N The data obtained this time constitutes a N The formulas for calculating "a matrix with 3 rows and 3 columns, and normalizing and weighting the data in each column" are as follows: ; ； in, It is normalized matrix data. x ij For the repeat N The data obtained this time constitutes a N The first row of a 3-column matrix i row and number j Column data, max( x j ) for in the N In a matrix with 3 rows and 3 columns, the first j The maximum value and minimum value of the data in the column. x j ) for in the N In a matrix with 3 rows and 3 columns, the first j Minimum value of the column data P ij This is the matrix data after the weight calculation.
4. The method for early warning of mechanical defects in disconnecting switches according to claim 1, characterized in that, The phrase "using the dynamic weight vector and the first" refers to... N The calculation method for the "early warning index" obtained by multiplying the row matrix data and then performing a weighted summation is as follows: Set a vibration energy noise floor threshold a First, determine the current total vibrational energy. E RMS Is it less than the noise floor threshold? a ; like E RMS < a If so, the current vibration harmonic distortion rate will be forcibly set to 0, and the dynamic weight vector will be forcibly set to a balanced weight to suppress false alarms caused by static noise in the system. like E RMS > a Perform the calculation: ； ； ； ； in, E j The first j Information entropy of each indicator, when j When =1, it is the information entropy of the total vibration energy; when j When = 2, it is the information entropy of the vibration harmonic distortion rate and when j When =3, it represents the information entropy of the relative temperature difference value in the contact area of the disconnector switch. D j For the first j The coefficient of difference for each indicator W j For the first j The dynamic weights of each indicator H This is an early warning index.
5. An early warning system for mechanical defects in disconnect switches, characterized in that, The early warning method for mechanical defects of disconnecting switches according to any one of claims 1-4 includes the following modules: High-frequency filtering module: used to perform high-frequency filtering on the output signal of the piezoelectric vibration acceleration sensor and the output signal of the infrared temperature sensor of the contact area of the disconnect switch; The module for calculating the total vibration energy and vibration harmonic distortion rate of the low-frequency vibration signal is used to obtain the total vibration energy and vibration harmonic distortion rate of the isolating switch housing from the output signal of the piezoelectric vibration acceleration sensor. Matrix normalization module: used to eliminate the differences between physical dimensions of different indicators, while preventing overflow of subsequent logarithmic operations through non-zero shift processing, and providing compliant input data for subsequent entropy weight calculation; Dynamic weight vector calculation module: It is used to evaluate the sensitivity of each feature index in the current matrix in real time based on real-time data, and automatically generate dynamic weights and then perform weighted summation with real-time data to obtain the early warning index; Early warning judgment module: used to perform threshold logic judgment to distinguish between early latent faults and normal state; The output of the high-frequency filtering module is connected to the input of the total vibration energy and vibration harmonic distortion rate calculation module. The output of the total vibration energy and vibration harmonic distortion rate calculation module is connected to the input of the matrix normalization module. The output of the matrix normalization module is connected to the input of the dynamic weight vector calculation module. The output of the dynamic weight vector calculation module is connected to the input of the early warning judgment module.