GIS operating mechanism mechanical property multi-parameter evaluation method and system

Abstract

The invention relates to a GIS operating mechanism mechanical property multi-parameter evaluation method and system. The method comprises the following steps: setting basic weights of various monitoring parameter data of a GIS high-voltage circuit breaker; according to the average mechanical fault time of each mechanical fault, fitting a time change curve of degradation degrees of different mechanical faults, and generating a self-adaptive weight changing along with time; acquiring each environmental parameter, and correcting the various monitoring parameter data according to the deviation between each environmental parameter and a set corresponding environmental parameter standard value, the set coupling coefficient of every two environmental parameters and the set influence coefficient of each environmental parameter on the various monitoring parameter data; and calculating and normalizing the difference values between the corrected monitoring parameter data and the set corresponding monitoring parameter data threshold values, and weighting all the normalized difference values according to the self-adaptive weight to serve as the mechanical performance evaluation index of the GIS operating mechanism. According to the invention, the operation decline of equipment is considered, and the evaluation precision is improved.

Description

Technical Field

[0001] This invention belongs to the field of GIS evaluation technology, and more specifically, relates to a multi-parameter evaluation method and system for the mechanical performance of GIS operating mechanisms. Background Technology

[0002] As a core device of the power system, GIS (Gas Insulated Switchgear) plays a crucial role in power transmission, distribution, and fault isolation. The operating mechanism is the key structure for GIS to achieve opening and closing operations, and its mechanical performance directly determines the reliability of GIS and the safety of the power grid.

[0003] In the mechanical performance evaluation of GIS operating mechanisms, the traditional static weight allocation has significant shortcomings. Under different operating stages (such as the initial break-in stage, the intermediate stabilization stage, and the late aging stage) and different failure modes (spring fatigue, contact wear, transmission jamming, etc.), the importance of each monitoring parameter in indicating the equipment status of the GIS operating mechanism will change significantly.

[0004] CN113933038A provides a method for monitoring the mechanical performance of GIS based on a multi-source information fusion algorithm. This method fuses mechanical vibration signals, moving contact path signals, and opening / closing coil current signals, and applies the fused image information to a VGG-16 neural network to achieve online monitoring of the mechanical performance of GIS equipment. However, it does not consider the deterioration of the GIS operating mechanism due to time variations. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a multi-parameter evaluation method and system for the mechanical performance of GIS operating mechanisms.

[0006] The present invention adopts the following technical solution.

[0007] The first aspect of this invention provides a multi-parameter evaluation method for the mechanical performance of a GIS operating mechanism, comprising:

[0008] Set the basic weights for various monitoring parameters of GIS high-voltage circuit breakers;

[0009] Taking into account the degradation of mechanical performance caused by operation, the time variation curve of the degradation degree of different mechanical faults is fitted according to the average mechanical fault time of each mechanical fault. The basic weights are modified according to the time variation curve of the degradation degree of mechanical faults to generate adaptive weights that change with time.

[0010] Obtain various environmental parameters, and correct various monitoring parameter data based on the deviation of each environmental parameter from the set standard value of the corresponding environmental parameter, the coupling coefficient between each pair of environmental parameters, and the influence coefficient of each environmental parameter on various monitoring parameter data.

[0011] The differences between the corrected monitoring parameter data and the set corresponding monitoring parameter data thresholds are calculated and normalized. All normalized differences are weighted according to adaptive weights and used as the mechanical performance evaluation index of the GIS operating mechanism.

[0012] Preferably, the monitoring parameter data includes the corresponding opening or closing time of the GIS starting at the current moment, and the starting current, maximum current, corresponding time of maximum current, current envelope kurtosis value, peak value of moving contact stroke, and peak time of moving contact stroke during the GIS opening or closing process.

[0013] Preferably, the basic weights of the various monitoring parameters of the GIS high-voltage circuit breaker are set as follows:

[0014] The entropy weight method is used to set the basic weights of various monitoring parameters of the GIS high-voltage circuit breaker; historical monitoring parameter data of the normal state of the GIS high-voltage circuit breaker are obtained.

[0015]

[0016] Where, ω i y represents the basic weight of the i-th type of historical monitoring parameter data; iq H represents the q-th data point in the ith historical monitoring parameter data under normal conditions after normalization, where m is the total number of monitoring parameter data types. i Let be the information entropy of the i-th monitoring parameter data.

[0017] Preferably, the step of fitting time variation curves of different mechanical fault deterioration degrees based on the average mechanical fault time of each type of mechanical fault specifically involves:

[0018]

[0019] Where, η j (t) represents the time-varying curve of the deterioration degree of the j-th type of mechanical fault; T j Let a be the average mechanical failure time for the j-th type of mechanical failure; j b j c j , are the curve parameters for the j-th type of mechanical fault, obtained through experiments; t is time t.

[0020] Preferably, the step of correcting the basic weights based on the time-varying curve of the mechanical fault's deterioration degree to generate adaptive weights that change over time specifically involves:

[0021]

[0022] Where, ω iω′(t) represents the adaptive weight of the i-th monitoring parameter data at time t; i The basic weight of the i-th monitoring parameter data; J is the total number of mechanical fault types; c i,j Let be the correlation coefficient between the i-th monitoring parameter data and the j-th mechanical fault.

[0023] Preferably, the correlation coefficient between the i-th monitoring parameter data and the j-th mechanical fault is specifically as follows:

[0024] Acquire all historical monitoring parameter data for all states, including normal operation and various mechanical fault states. Calculate the average value of each historical monitoring parameter data under normal operation and the average value of each historical monitoring parameter data under each mechanical fault state. Calculate the total standard deviation of each historical monitoring parameter data for all states.

[0025] The correlation coefficient between the i-th monitoring parameter data and the j-th mechanical fault is obtained by subtracting the average value of the i-th historical monitoring parameter data under normal operating conditions from the average value of the i-th historical monitoring parameter data under the j-th mechanical fault condition, and then dividing the calculated difference by the total standard deviation of the i-th historical monitoring parameter data under all conditions.

[0026] Preferably, the calibration of various monitoring parameter data specifically includes:

[0027]

[0028] ρ k,l (E k E l )=τ k,l ×φ(E k )×φ(E l )

[0029]

[0030] Wherein, φ(E) k ), φ(E l ) are the standardized values ​​of the deviations of the k-th and l-th environmental parameters from the set standard values ​​of the corresponding environmental parameters, respectively. The l-th environmental parameter is the environmental parameter with the largest coupling coefficient to the k-th environmental parameter. τ k,l ρ is the coupling coefficient between the l-th environmental parameter and the k-th environmental parameter; k,l (E k E l ) represents the coupling term between the l-th environmental parameter and the k-th environmental parameter; E k,norm E l,norm These are the standard values ​​for the k-th and l-th environmental parameters, respectively; E k E lLet K be the k-th and l-th environmental parameters, respectively; K is the total number of environmental parameters; M i,k Let be the influence coefficient of the k-th environmental parameter on the i-th monitoring parameter data.

[0031] The second aspect of the present invention proposes a multi-parameter evaluation system for the mechanical performance of GIS operating mechanisms based on the method described in the first aspect of the present invention, comprising a basic weight setting module, an adaptive weight setting module, a correction module, and a mechanical performance evaluation index calculation module, specifically as follows:

[0032] Basic weight setting module: Sets the basic weights of various monitoring parameter data of GIS high-voltage circuit breakers;

[0033] Adaptive weight setting module: Taking into account the degradation of mechanical performance caused by operation, it fits the time change curve of the degradation degree of different mechanical faults based on the average mechanical fault time of each mechanical fault, and corrects the basic weights based on the time change curve of the degradation degree of mechanical faults to generate adaptive weights that change with time.

[0034] Calibration module: Acquires various environmental parameters, and calibrates various monitoring parameter data based on the deviation of each environmental parameter from the set standard value of the corresponding environmental parameter, the set coupling coefficient between each pair of environmental parameters, and the set influence coefficient of each environmental parameter on various monitoring parameter data.

[0035] Mechanical performance evaluation index calculation module: Calculates and normalizes the difference between the corrected monitoring parameter data and the set corresponding monitoring parameter data thresholds, and uses all normalized differences as the mechanical performance evaluation index of the GIS operating mechanism by adaptive weighting.

[0036] A third aspect of the invention provides an apparatus comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor performing steps using a multi-parameter evaluation method for the mechanical performance of a GIS operating mechanism as described in the first aspect of the invention.

[0037] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, uses the steps of the multi-parameter evaluation method for the mechanical performance of a GIS operating mechanism as described in the first aspect of the present invention.

[0038] The beneficial effects of this invention are as follows: Compared with the prior art, this invention adaptively corrects the weights by fitting the degradation curves of different types of mechanical faults, taking into account the operational degradation of the equipment, so that the weights can reflect the current actual mechanical performance state of the equipment, and incorporates time characteristics to improve the evaluation accuracy; it takes into account the influence of environmental factors on the measurement and corrects to eliminate data interference; it integrates various parameters of GIS high-voltage circuit breakers such as current and moving contact stroke, and calculates the evaluation index by weighted summation according to adaptively corrected weights, covering the electrical response and mechanical transmission characteristics of the operating mechanism, and avoiding the one-sidedness of single parameter evaluation. Attached Figure Description

[0039] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0041] like Figure 1 As shown, Embodiment 1 of the present invention proposes a multi-parameter evaluation method for the mechanical performance of a GIS operating mechanism, comprising:

[0042] Set the basic weights for various monitoring parameters of GIS high-voltage circuit breakers;

[0043] Taking into account the degradation of mechanical performance caused by operation, the time variation curve of the degradation degree of different mechanical faults is fitted according to the average mechanical fault time of each mechanical fault. The basic weights are modified according to the time variation curve of the degradation degree of mechanical faults to generate adaptive weights that change with time.

[0044] Obtain various environmental parameters, and correct various monitoring parameter data based on the deviation of each environmental parameter from the set standard value of the corresponding environmental parameter, the coupling coefficient between each pair of environmental parameters, and the influence coefficient of each environmental parameter on various monitoring parameter data.

[0045] The differences between the corrected monitoring parameter data and the set corresponding monitoring parameter data thresholds are calculated and normalized. All normalized differences are then weighted according to adaptive weights to obtain the mechanical performance evaluation index of the GIS operating mechanism. The larger the mechanical performance evaluation index of the GIS operating mechanism, the worse the mechanical performance and the closer it is to mechanical failure.

[0046] In this embodiment, the monitoring parameter data includes the corresponding opening or closing time of the GIS starting at the current moment, and the starting current, maximum current, corresponding time of maximum current, current envelope kurtosis value, peak value of moving contact stroke, and peak time of moving contact stroke during the GIS opening or closing process.

[0047] In this preferred embodiment, the basic weights of the various monitoring parameters of the GIS high-voltage circuit breaker are specifically set as follows:

[0048] The entropy weight method is used to set the basic weights of various monitoring parameters of the GIS high-voltage circuit breaker; historical monitoring parameter data of the normal state of the GIS high-voltage circuit breaker are obtained.

[0049]

[0050]

[0051] Where, ω i y represents the basic weight of the i-th type of historical monitoring parameter data; iq H represents the q-th data point in the ith historical monitoring parameter data under normal conditions after normalization, where m is the total number of monitoring parameter data types. i p is the information entropy of the i-th monitoring parameter data; n is the number of samples collected; p iq The percentage of the q-th data point relative to the i-th type of historical monitoring parameter data is denoted by q.

[0052] In this preferred embodiment, the step of fitting time variation curves of different mechanical fault deterioration degrees based on the average mechanical fault time of each type of mechanical fault specifically involves:

[0053]

[0054] Where, η j (t) represents the time-varying curve of the deterioration degree of the j-th type of mechanical fault; T j Let a be the average mechanical failure time for the j-th type of mechanical failure; j b j c j , are the curve parameters for the j-th type of mechanical fault, obtained through experiments; t is time t.

[0055] In this preferred embodiment, the step of correcting the basic weights based on the time-varying curve of the mechanical fault's deterioration degree to generate adaptive weights that change over time specifically involves:

[0056]

[0057] Where, ω i ω′(t) represents the adaptive weight of the i-th monitoring parameter data at time t;i The basic weight of the i-th monitoring parameter data; J is the total number of mechanical fault types; c i,j Let be the correlation coefficient between the i-th monitoring parameter data and the j-th mechanical fault.

[0058] In this preferred embodiment, the correlation coefficient between the i-th monitoring parameter data and the j-th mechanical fault is specifically:

[0059] Acquire all historical monitoring parameter data for all states, including normal operation and various mechanical fault states. Calculate the average value of each historical monitoring parameter data under normal operation and the average value of each historical monitoring parameter data under each mechanical fault state. Calculate the total standard deviation of each historical monitoring parameter data for all states.

[0060] The correlation coefficient between the i-th monitoring parameter data and the j-th mechanical fault is obtained by subtracting the average value of the i-th historical monitoring parameter data under normal operating conditions from the average value of the i-th historical monitoring parameter data under the j-th mechanical fault condition, and then dividing the calculated difference by the total standard deviation of the i-th historical monitoring parameter data under all conditions.

[0061] In this preferred embodiment, the calibration of various monitoring parameter data specifically includes:

[0062]

[0063] ρ k,l (E k E l )=τ k,l ×φ(E k )×φ(E l )

[0064]

[0065] Wherein, φ(E) k ), φ(E l ) are the standardized values ​​of the deviations of the k-th and l-th environmental parameters from the set standard values ​​of the corresponding environmental parameters, respectively. The l-th environmental parameter is the environmental parameter with the largest coupling coefficient to the k-th environmental parameter. τ k,l ρ is the coupling coefficient between the l-th environmental parameter and the k-th environmental parameter; k,l (E k E l ) represents the coupling term between the l-th environmental parameter and the k-th environmental parameter; E k,norm E l,norm These are the standard values ​​for the k-th and l-th environmental parameters, respectively; E k E lLet K be the k-th and l-th environmental parameters, respectively; K is the total number of environmental parameters; M i,k Let be the influence coefficient of the k-th environmental parameter on the i-th monitoring parameter data.

[0066] It should be noted that environmental factors can affect the sensitivity, zero point, and linearity of sensors used for measuring and monitoring parameters, resulting in errors. Therefore, a correction is made here to subtract these errors, M. i,k This refers to the proportion of the error in the monitoring parameter measured by the corresponding sensor due to the corresponding environmental factor. It is obtained through experiments, and the error can be positive or negative, i.e., M. i,k It can be a positive value, 0 or a negative value. The coupling coefficient is the effect caused by the coupling between two environmental parameters. In order to simplify the calculation, this embodiment only takes the common binary coupling and only takes the environmental parameter that is most likely to be coupled with its own parameter (i.e.) to calculate its coupling term. Specifically, the environmental parameters in this embodiment are only temperature and humidity, so the coupling term of temperature and humidity is calculated.

[0067] Embodiment 2 of the present invention proposes a multi-parameter evaluation system for the mechanical performance of GIS operating mechanisms based on the method described in Embodiment 1 of the present invention, including a basic weight setting module, an adaptive weight setting module, a correction module, and a mechanical performance evaluation index calculation module, specifically as follows:

[0068] Basic weight setting module: Sets the basic weights of various monitoring parameter data of GIS high-voltage circuit breakers;

[0069] Adaptive weight setting module: Taking into account the degradation of mechanical performance caused by operation, it fits the time change curve of the degradation degree of different mechanical faults based on the average mechanical fault time of each mechanical fault, and corrects the basic weights based on the time change curve of the degradation degree of mechanical faults to generate adaptive weights that change with time.

[0070] Calibration module: Acquires various environmental parameters, and calibrates various monitoring parameter data based on the deviation of each environmental parameter from the set standard value of the corresponding environmental parameter, the set coupling coefficient between each pair of environmental parameters, and the set influence coefficient of each environmental parameter on various monitoring parameter data.

[0071] Mechanical performance evaluation index calculation module: Calculates and normalizes the difference between the corrected monitoring parameter data and the set corresponding monitoring parameter data thresholds, and uses all normalized differences as the mechanical performance evaluation index of the GIS operating mechanism by adaptive weighting.

[0072] Embodiment 3 of the present invention provides an apparatus including a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor performing steps using a multi-parameter evaluation method for the mechanical performance of a GIS operating mechanism as described in Embodiment 1 of the present invention.

[0073] Embodiment 4 of the present invention proposes a computer-readable storage medium storing a computer program that, when executed by a processor, uses the steps of the multi-parameter evaluation method for the mechanical performance of a GIS operating mechanism as described in Embodiment 1 of the present invention.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A multi-parameter evaluation method for the mechanical performance of a GIS operating mechanism, characterized in that, include: Set the basic weights for various monitoring parameters of GIS high-voltage circuit breakers; Taking into account the degradation of mechanical performance caused by operation, the time variation curve of the degradation degree of different mechanical faults is fitted according to the average mechanical fault time of each mechanical fault. The basic weights are modified according to the time variation curve of the degradation degree of mechanical faults to generate adaptive weights that change with time. Obtain various environmental parameters, and correct various monitoring parameter data based on the deviation of each environmental parameter from the set standard value of the corresponding environmental parameter, the coupling coefficient between each pair of environmental parameters, and the influence coefficient of each environmental parameter on various monitoring parameter data. The differences between the corrected monitoring parameter data and the set corresponding monitoring parameter data thresholds are calculated and normalized. All normalized differences are weighted according to adaptive weights and used as the mechanical performance evaluation index of the GIS operating mechanism.

2. The method for multi-parameter evaluation of the mechanical performance of a GIS operating mechanism according to claim 1, characterized in that: The monitoring parameter data includes the corresponding opening or closing time of the GIS starting from the current moment, and the starting current, maximum current, corresponding time of maximum current, current envelope kurtosis value, peak value of moving contact stroke, and peak time of moving contact stroke during the GIS opening or closing process.

3. The multi-parameter evaluation method for the mechanical performance of a GIS operating mechanism according to claim 1, characterized in that: The basic weights for various monitoring parameters of the GIS high-voltage circuit breaker are defined as follows: The entropy weight method is used to set the basic weights of various monitoring parameters of the GIS high-voltage circuit breaker; historical monitoring parameter data of the normal state of the GIS high-voltage circuit breaker are obtained. Where, ω i y represents the basic weight of the i-th type of historical monitoring parameter data; iq H represents the q-th data point in the ith historical monitoring parameter data under normal conditions after normalization, where m is the total number of monitoring parameter data types. i Let be the information entropy of the i-th monitoring parameter data.

4. The multi-parameter evaluation method for the mechanical performance of a GIS operating mechanism according to claim 1, characterized in that: The process of fitting time variation curves for the deterioration degree of different mechanical faults based on the average mechanical fault time for each type of mechanical fault is as follows: Where, η j (t) represents the time-varying curve of the deterioration degree of the j-th type of mechanical fault; T j Let a be the average mechanical failure time for the j-th type of mechanical failure; j b j c j , are the curve parameters for the j-th type of mechanical fault, obtained through experiments; t is time t.

5. The multi-parameter evaluation method for the mechanical performance of a GIS operating mechanism according to claim 4, characterized in that: The process of correcting the basic weights based on the time-varying curve of the mechanical failure's deterioration degree to generate adaptive weights that change over time is as follows: Where, ω′ i (t) represents the adaptive weight of the i-th monitoring parameter data at time t; ω i The basic weight of the i-th monitoring parameter data; J is the total number of mechanical fault types; c i,j Let be the correlation coefficient between the i-th monitoring parameter data and the j-th mechanical fault.

6. The multi-parameter evaluation method for the mechanical performance of a GIS operating mechanism according to claim 5, characterized in that: The correlation coefficient between the i-th monitoring parameter data and the j-th mechanical fault is as follows: Acquire all historical monitoring parameter data for all states, including normal operation and various mechanical fault states. Calculate the average value of each historical monitoring parameter data under normal operation and the average value of each historical monitoring parameter data under each mechanical fault state. Calculate the total standard deviation of each historical monitoring parameter data for all states. The correlation coefficient between the i-th monitoring parameter data and the j-th mechanical fault is obtained by subtracting the average value of the i-th historical monitoring parameter data under normal operating conditions from the average value of the i-th historical monitoring parameter data under the j-th mechanical fault condition, and then dividing the calculated difference by the total standard deviation of the i-th historical monitoring parameter data under all conditions.

7. The method for multi-parameter evaluation of the mechanical performance of a GIS operating mechanism according to claim 1, characterized in that: The calibration of various monitoring parameter data specifically includes: ρ k,l (AND k ,AND l )=τ k,l ×φ(E k )×φ(E l ) Wherein, φ(E) k ), φ(E l ) are the standardized values ​​of the deviations of the k-th and l-th environmental parameters from the set standard values ​​of the corresponding environmental parameters, respectively. The l-th environmental parameter is the environmental parameter with the largest coupling coefficient to the k-th environmental parameter. τ k,l ρ is the coupling coefficient between the l-th environmental parameter and the k-th environmental parameter; k,l (E k E l ) represents the coupling term between the l-th environmental parameter and the k-th environmental parameter; E k,norm E l,norm These are the standard values ​​for the k-th and l-th environmental parameters, respectively; E k E l Let K be the k-th and l-th environmental parameters, respectively; K is the total number of environmental parameters; M i,k Let be the influence coefficient of the k-th environmental parameter on the i-th monitoring parameter data.

8. A multi-parameter evaluation system for the mechanical performance of a GIS operating mechanism based on the method of any one of claims 1-7, comprising a basic weight setting module, an adaptive weight setting module, a correction module, and a mechanical performance evaluation index calculation module, characterized in that: Basic weight setting module: Sets the basic weights of various monitoring parameter data of GIS high-voltage circuit breakers; Adaptive weight setting module: Taking into account the degradation of mechanical performance caused by operation, it fits the time change curve of the degradation degree of different mechanical faults based on the average mechanical fault time of each mechanical fault, and corrects the basic weights based on the time change curve of the degradation degree of mechanical faults to generate adaptive weights that change with time. Calibration module: Acquires various environmental parameters, and calibrates various monitoring parameter data based on the deviation of each environmental parameter from the set standard value of the corresponding environmental parameter, the set coupling coefficient between each pair of environmental parameters, and the set influence coefficient of each environmental parameter on various monitoring parameter data. Mechanical performance evaluation index calculation module: Calculates and normalizes the difference between the corrected monitoring parameter data and the set corresponding monitoring parameter data thresholds, and uses all normalized differences as the mechanical performance evaluation index of the GIS operating mechanism by adaptive weighting.

9. An apparatus comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor performing steps of a multi-parameter evaluation method for the mechanical performance of a GIS operating mechanism according to any one of claims 1-7.

10. A computer-readable storage medium storing a computer program that, when executed by a processor, uses the steps of the multi-parameter evaluation method for the mechanical performance of a GIS operating mechanism according to any one of claims 1-7.

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