Method for calculating insulation resistance value of power equipment under action of multiple disturbance factors of transformer substation

Abstract

The invention provides a power equipment insulation resistance value calculation method under the action of multiple disturbance factors of a transformer substation, and relates to the field of power equipment insulation resistance calculation. The method comprises the following steps: firstly, calculating a main disturbance factor sequence # imgabs0 # of a transformer substation according to an IICA algorithm, then carrying out numerical sampling on the main disturbance factor sequence # imgabs1 # to obtain discrete sampling values of main disturbance factors, obtaining a real-time model continuous curve of the main disturbance factors by using an MCD algorithm, then obtaining a function relation y of a main disturbance factor model by using an AUKF algorithm, and finally obtaining a real-time model continuous curve of the main disturbance factors by using a real-time model continuous curve of the main disturbance factors. And finally, measuring an applied voltage and a leakage current of the power equipment, obtaining an insulation resistance value phi, and obtaining a corrected insulation resistance value phi c of the power equipment through the function relation y of the main disturbance factor model. The calculation method provided by the invention can eliminate the influence of a disturbance signal on an applied voltage signal, greatly improves the accuracy and effectiveness of accurate insulation resistance calculation, and greatly improves the personal safety of operators and the operation reliability of a power system.

Description

Technical Field

[0001] The invention relates to the technical field of calculation of insulation resistance of electric power equipment, and in particular to a method for calculating insulation resistance value of electric power equipment under the action of multiple disturbance factors of a substation. Background Art

[0002] The insulation problem of power equipment is the main factor causing equipment failure, and insulation resistance is one of the most important insulation indicators of power equipment. It indicates the ability of electrical equipment to prevent leakage current from passing through. The larger the resistance value, the better the effect of preventing leakage current. It is usually measured in megaohms. By calculating the insulation resistance of power equipment, obvious insulation defects in the insulation materials of power equipment can be discovered in advance, and defects such as insulation foreign matter in the insulation medium of power equipment, partial or overall moisture and dirt on the insulation, severe deterioration of insulating oil, insulation breakdown and severe thermal aging can be discovered based on the measured insulation resistance. Therefore, accurate insulation resistance calculation methods and results are crucial to determining the normal operation of power equipment and the reliability of the power grid.

[0003] The current measurement and calculation of the insulation resistance of power equipment usually involves applying a corresponding DC voltage to the device under test, obtaining the leakage current flowing through the power equipment, and calculating the insulation resistance value of the equipment using Ohm's law. This method has a simple calculation principle and is easy to implement, but does not have strong anti-interference capabilities. Especially in the complex environment of a substation, the insulation resistance measurement results are often interfered with by a variety of disturbance signals. For example, live equipment in a substation can induce multi-band voltages and other electromagnetic interference signals in the power-off equipment under test, resulting in large errors in the final test results.

[0004] In order to accurately measure and calculate the insulation resistance of power equipment in the substation under the influence of multiple disturbance factors, it is necessary to analyze, extract and calculate the interference signals on the equipment, and finally exclude the influence of the disturbance signal on the applied voltage signal in the calculation result, which will greatly improve the accuracy and effectiveness of accurate insulation resistance calculation. Therefore, it is particularly important to design a method for calculating the insulation resistance value of power equipment under the influence of multiple disturbance factors in the substation. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the deficiencies in the prior art, the present invention provides a method for calculating the insulation resistance value of power equipment under the action of multiple disturbance factors in a substation, which solves the technical problems mentioned in the background technology.

[0007] (II) Technical solution

[0008] A method for calculating the insulation resistance value of power equipment under the action of multiple disturbance factors in a substation, the calculation steps are as follows:

[0009] Step 1: According to the improved independent component analysis (IICA) algorithm, various disturbance factors of power equipment in the substation environment are analyzed and the weight of each disturbance factor is calculated to extract the main disturbance factor series of the substation.

[0010] Step 2: By calculating the main perturbation factor series Numerical sampling is performed to obtain the sampling values ​​of each main disturbance factor, and a multi-object collaborative data fitting algorithm (MCD) is proposed. The algorithm is used to fit the sampling series of the main disturbance factors to obtain the real-time model curve of the main disturbance factors.

[0011] Step 3: Use the Adaptive Unscented Kalman Filter (AUKF) algorithm to estimate the parameters of the real-time model curve of the main disturbance factor and obtain the functional relationship of the main disturbance factor model.

[0012] Step 4: Measure the applied voltage and leakage current of the power equipment, calculate the uncorrected insulation resistance value φ of the power equipment, correct the uncorrected insulation resistance value through the functional relationship y of the main disturbance factor, and obtain the corrected insulation resistance value φ of the power equipment c ;

[0013] Preferably, in step 1, the components of various disturbance factors to which the power equipment is subjected in the substation environment are analyzed according to the IICA algorithm, and the weight of each disturbance factor is calculated to extract the main disturbance factor series of the substation. The specific steps are:

[0014] Step 1.1: Preprocess the input signal x through the whitening matrix W to reduce data redundancy:

[0015]

[0016] In the formula, is the diagonal matrix of the right eigenvalues, Λ is the covariance matrix of the input signal matrix x, and I is the identity matrix;

[0017] Step 1.2: Initialize the discrete vector w according to the identity matrix;

[0018] Step 1.3: Calculate the Lagrangian gradient vector L(w,λ) and the corresponding Hessian matrix H w :

[0019] L(w,λ)=B(w)+λ(w H w-1)

[0020]

[0021] Where B() is the cost function, λ is the Lagrangian parameter, w is the discrete vector, and w H is the conjugate transposed matrix of w, is the partial differential equation for w;

[0022] Step 1.4: Update the discrete vector w(k+1) and Lagrangian parameter λ(k+1) in real time:

[0023]

[0024]

[0025] In the formula, τ is the step size, k is the time level, is the augmented gradient vector;

[0026] Step 1.5: If the algorithm does not converge, go to step 1.2 to recalculate the Lagrangian gradient vector and the corresponding Hessian matrix H w ;

[0027] Step 1.6: If the algorithm converges, determine the weight of each perturbation factor and determine the number of main perturbation factors

[0028]

[0029] In the formula, The fundamental component of the induced voltage of the 220kV equipment. The third harmonic component of the induced voltage of 220kV equipment, The fundamental component of the induced voltage of the 110kV equipment. The third harmonic component of the induced voltage of 110kV equipment, is the residual charge value of the device under test;

[0030] Preferably, in step 2, the main disturbance factor series Numerical sampling is performed to obtain discrete sampling values ​​of each main disturbance factor. An MCD algorithm is proposed. The algorithm is used to fit the sampling series of the main disturbance factors to obtain the continuous curve of the real-time model of the main disturbance factors. The specific steps are as follows:

[0031] Step 2.1: Set the convergence target values ​​of the multi-objective collaborative data fitting algorithm, which are: mean square error fitness α, graphic similarity fitness β, peak-valley consistency fitness

[0032] Step 2.2: Calculate the mean square error fitness α:

[0033]

[0034] Where n is the total number of sampling points, is the disturbance signal amplitude at the i-th sampling point, is the phase of the disturbance signal at the i-th sampling point, is the amplitude of the fitted disturbance signal at the i-th sampling point, is the phase of the fitted disturbance signal at the i-th sampling point, μ is the weight coefficient;

[0035] Step 2.3: Calculate the graph similarity fitness β:

[0036]

[0037] in, is the graphic value at the i-th sampling point, The value of the fitted graph at the i-th sampling point;

[0038] Step 2.4: Calculate peak-valley consistency fitness

[0039]

[0040] Among them, e is the base of natural logarithm, γ is the penalty coefficient, E max and E min Setting a threshold for the disturbance signal;

[0041] Step 2.5: Calculate the optimization target Maximumg(i) of the multi-objective collaborative data fitting algorithm and iterate:

[0042]

[0043] If the algorithm does not converge, then re-enter step 2.2 to step 2.4 to calculate the fitness. If the algorithm converges, the real-time model continuous curve of the main disturbance factor is obtained;

[0044] Preferably, in step 3, the AUKF algorithm is used to estimate the parameters of the real-time model curve of the main disturbance factor to obtain the functional relationship y of the main disturbance factor model, and the specific steps are:

[0045] Step 3.1: Calculate the state probability model z according to the input disturbance factor real-time model curve ij :

[0046]

[0047] In the formula, i and j are the number of sampling points;

[0048] Step 3.2: According to the state probability model z ij , calculate the normalization factor g and the matching probability η k :

[0049]

[0050] In the formula, n is the total number of sampling points, k is the time level;

[0051] Step 3.3: Update the matching probability model η in real time k :

[0052]

[0053] In the formula, Z k is the observation model, Z k is the observation model with weight coefficients, H(·) is the feedback function, is the observed covariance parameter, d is the constant coefficient;

[0054] Step 3.4: Assemble and non-aggregate To standardize:

[0055]

[0056]

[0057] Where p is the design parameter;

[0058] Step 3.5: Initialize the system parameter estimation mean And its mean square error P k :

[0059] In the formula, is the predicted mean, P k is the error covariance, Q k is the system noise matrix;

[0060] Step 3.6: Update sampling points

[0061]

[0062] In the formula, function(·) is a nonlinear function, Q y,k is the maximum period of time level k;

[0063] Step 3.7: Update the system parameter estimate mean

[0064]

[0065] Step 3.8: Calculate the system uncertainty θ j :

[0066]

[0067] Step 3.8: Calculate the system output y:

[0068]

[0069] Preferably, in step 4, the applied voltage and leakage current of the power equipment are measured, and the uncorrected insulation resistance value φ of the power equipment is calculated, and the uncorrected insulation resistance value is corrected by the functional relationship y of the main disturbance factor model to obtain the corrected insulation resistance value φ of the power equipment c , the specific steps are:

[0070] Step 4.1: Measure the applied voltage value U and leakage current I of the power equipment;

[0071] Step 4.2: Calculate the uncorrected insulation resistance value φ based on the applied voltage U and leakage current I of the power equipment:

[0072] φ=U / I

[0073] Step 4.3: Use the disturbance factor function relationship y to correct the uncorrected insulation resistance value φ to obtain the corrected insulation resistance value φ c :

[0074]

[0075] (III) Beneficial effects

[0076] The present invention provides a method for calculating the insulation resistance value of power equipment under the action of multiple disturbance factors in a substation. It has the following beneficial effects:

[0077] The method for calculating the insulation resistance value of power equipment under the influence of multiple disturbance factors in the substation improves the accuracy of insulation resistance measurement and calculation of power equipment under multiple disturbance conditions in the substation, can more accurately judge whether the insulation performance of the power equipment is damaged, and greatly improves the personal safety of operators and the reliability of power system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 A flowchart of a method for calculating the insulation resistance value of power equipment under the action of multiple disturbance factors in a substation provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0079] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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.

[0080] In this embodiment, a method for calculating the insulation resistance value of power equipment under the action of multiple disturbance factors in a substation is provided. The algorithm flow is as follows: Figure 1 As shown, the method for calculating the insulation resistance value of the power equipment specifically includes the following steps:

[0081] Step 1: According to the improved independent component analysis (IICA) algorithm, various disturbance factors of power equipment in the substation environment are analyzed and the weight of each disturbance factor is calculated to extract the main disturbance factor series of the substation.

[0082] Step 2: By calculating the main perturbation factor series Numerical sampling is performed to obtain the sampling values ​​of each main disturbance factor, and a multi-object collaborative data fitting algorithm (MCD) is proposed. The algorithm is used to fit the sampling series of the main disturbance factors to obtain the real-time model curve of the main disturbance factors.

[0083] Step 3: Use the Adaptive Unscented Kalman Filter (AUKF) algorithm to estimate the parameters of the real-time model curve of the main disturbance factor and obtain the functional relationship of the main disturbance factor model.

[0084] Step 4: Measure the applied voltage and leakage current of the power equipment, calculate the uncorrected insulation resistance value φ of the power equipment, correct the uncorrected insulation resistance value through the functional relationship y of the main disturbance factor, and obtain the corrected insulation resistance value φ of the power equipment c ;

[0085] Preferably, in step 1, the components of various disturbance factors to which the power equipment is subjected in the substation environment are analyzed according to the IICA algorithm, and the weight of each disturbance factor is calculated to extract the main disturbance factor series of the substation. The specific steps are:

[0086] Step 1.1: Preprocess the input signal x through the whitening matrix W to reduce data redundancy:

[0087]

[0088] In the formula, is the diagonal matrix of the right eigenvalues, Λ is the covariance matrix of the input signal matrix x, and I is the identity matrix;

[0089] Step 1.2: Initialize the discrete vector w according to the identity matrix;

[0090] Step 1.3: Calculate the Lagrangian gradient vector L(w,λ) and the corresponding Hessian matrix H w :

[0091] L(w,λ)=B(w)+λ(w H w-1)

[0092]

[0093] Where B() is the cost function, λ is the Lagrangian parameter, w is the discrete vector, and w H is the conjugate transposed matrix of w, is the partial differential equation for w;

[0094] Step 1.4: Update the discrete vector w(k+1) and Lagrangian parameter λ(k+1) in real time:

[0095]

[0096] In the formula, τ is the step size, k is the time level, is the augmented gradient vector;

[0097] Step 1.5: If the algorithm does not converge, go to step 1.2 to recalculate the Lagrangian gradient vector and the corresponding Hessian matrix H w ;

[0098] Step 1.6: If the algorithm converges, determine the weight of each perturbation factor and determine the number of main perturbation factors

[0099]

[0100] In the formula, The fundamental component of the induced voltage of the 220kV equipment. The third harmonic component of the induced voltage of 220kV equipment, The fundamental component of the induced voltage of the 110kV equipment. The third harmonic component of the induced voltage of 110kV equipment, is the residual charge value of the device under test;

[0101] Preferably, in step 2, the main disturbance factor series Numerical sampling is performed to obtain discrete sampling values ​​of each main disturbance factor. An MCD algorithm is proposed. The algorithm is used to fit the sampling series of the main disturbance factors to obtain the continuous curve of the real-time model of the main disturbance factors. The specific steps are as follows:

[0102] Step 2.1: Set the convergence target values ​​of the multi-objective collaborative data fitting algorithm, which are: mean square error fitness α, graphic similarity fitness β, peak-valley consistency fitness

[0103] Step 2.2: Calculate the mean square error fitness α:

[0104]

[0105] Where n is the total number of sampling points, is the disturbance signal amplitude at the i-th sampling point, is the phase of the disturbance signal at the i-th sampling point, is the amplitude of the fitted disturbance signal at the i-th sampling point, is the phase of the fitted disturbance signal at the i-th sampling point, μ is the weight coefficient;

[0106] Step 2.3: Calculate the graph similarity fitness β:

[0107]

[0108] in, is the graphic value at the i-th sampling point, The value of the fitted graph at the i-th sampling point;

[0109] Step 2.4: Calculate peak-valley consistency fitness

[0110]

[0111] Among them, e is the base of natural logarithm, γ is the penalty coefficient, E max and E min Setting a threshold for the disturbance signal;

[0112] Step 2.5: Calculate the optimization target Maximumg(i) of the multi-objective collaborative data fitting algorithm and iterate:

[0113]

[0114] If the algorithm does not converge, then re-enter step 2.2 to step 2.4 to calculate the fitness. If the algorithm converges, the real-time model continuous curve of the main disturbance factor is obtained;

[0115] Preferably, in step 3, the AUKF algorithm is used to estimate the parameters of the real-time model curve of the main disturbance factor to obtain the functional relationship y of the main disturbance factor model, and the specific steps are:

[0116] Step 3.1: Calculate the state probability model z according to the input disturbance factor real-time model curve ij :

[0117]

[0118] In the formula, i and j are the number of sampling points;

[0119] Step 3.2: According to the state probability model z ij , calculate the normalization factor g and the matching probability η k :

[0120]

[0121] In the formula, n is the total number of sampling points, k is the time level;

[0122] Step 3.3: Update the matching probability model ηk in real time:

[0123]

[0124]

[0125] In the formula, Z k is the observation model, is the observation model with weight coefficients, H(·) is the feedback function, is the observed covariance parameter, d is the constant coefficient;

[0126] Step 3.4: Assemble and non-aggregate To standardize:

[0127]

[0128] Where p is the design parameter;

[0129] Step 3.5: Initialize the system parameter estimation mean And its mean square error P k :

[0130]

[0131] In the formula, is the predicted mean, P k is the error covariance, Q k is the system noise matrix;

[0132] Step 3.6: Update sampling points

[0133]

[0134] In the formula, function(·) is a nonlinear function, Q y,k is the maximum period of time level k;

[0135] Step 3.7: Update the system parameter estimate mean

[0136]

[0137] Step 3.8: Calculate the system uncertainty θ j :

[0138]

[0139] Step 3.8: Calculate the system output y:

[0140]

[0141] Preferably, in step 4, the applied voltage and leakage current of the power equipment are measured, and the uncorrected insulation resistance value φ of the power equipment is calculated, and the uncorrected insulation resistance value is corrected by the functional relationship y of the main disturbance factor model to obtain the corrected insulation resistance value φ of the power equipment c , the specific steps are:

[0142] Step 4.1: Measure the applied voltage value U and leakage current I of the power equipment;

[0143] Step 4.2: Calculate the uncorrected insulation resistance value φ based on the applied voltage U and leakage current I of the power equipment:

[0144] φ=U / I

[0145] Step 4.3: Use the disturbance factor function relationship y to correct the uncorrected insulation resistance value φ to obtain the corrected insulation resistance value φ c :

[0146]

[0147] It should be noted that, in the description of the invention, the directions or positional relationships indicated by the terms "up", "down", "left", "right", "front", "back", etc. are descriptions of the structure of the present invention based on the accompanying drawings, and are only for the convenience of describing the present invention, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as a limitation on the present invention.

[0148] The "first" and "second" in this technical solution are only used to distinguish the names of the same or similar structures, or corresponding structures with similar functions, and are not an arrangement of the importance of these structures, nor do they have a ranking, comparison of size, or other meanings.

[0149] In addition, unless otherwise clearly specified and limited, the terms "installation" and "connection" should be understood in a broad sense. For example, the connection can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be a connection between the two structures. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood based on the overall idea of ​​the present invention and the specific context of the present solution.

Claims

1. A method for calculating the insulation resistance value of power equipment under the action of multiple disturbance factors in a substation, characterized in that: The steps include: Step 1: According to the improved independent component analysis algorithm, various disturbance factors of power equipment in the substation environment are analyzed and the weight of each disturbance factor is calculated to extract the main disturbance factor series of the substation. Step 2: By calculating the main perturbation factor series Numerical sampling is performed to obtain the sampling values ​​of each main disturbance factor, and a multi-objective collaborative data fitting algorithm is used to fit the sampling series of the main disturbance factors to obtain the real-time model curve of the main disturbance factors; Step 3: Use the adaptive unscented Kalman filter algorithm to estimate the parameters of the real-time model curve of the main disturbance factor and obtain the functional relationship of the main disturbance factor model. Step 4: Measure the applied voltage and leakage current of the power equipment, calculate the uncorrected insulation resistance value φ of the power equipment, correct the uncorrected insulation resistance value through the functional relationship y of the main disturbance factor, and obtain the corrected insulation resistance value φ of the power equipment c .

2. The method for calculating the insulation resistance value of power equipment under the action of multiple disturbance factors in a substation according to claim 1 is characterized in that: In step 1, the various disturbance factors to which the power equipment is subjected in the substation environment are analyzed according to the improved independent component analysis algorithm, and the weight of each disturbance factor is calculated to extract the main disturbance factor series of the substation. The specific steps are: Step 1.1: Preprocess the input signal x through the whitening matrix W to reduce data redundancy: In the formula, is the diagonal matrix of the right eigenvalues, Λ is the covariance matrix of the input signal matrix x, and I is the identity matrix; Step 1.2: Initialize the discrete vector w according to the identity matrix; Step 1.3: Calculate the Lagrangian gradient vector L(w,λ) and the corresponding Hessian matrix H w : L(w,λ)=B(w)+λ(w H w-1) Where B( ) is the cost function, λ is the Lagrangian parameter, w is the discrete vector, and w H is the conjugate transposed matrix of w, is the partial differential equation for w; Step 1.4: Update the discrete vector w(k+1) and Lagrangian parameter λ(k+1) in real time: In the formula, τ is the step size, k is the time level, is the augmented gradient vector; Step 1.5: If the algorithm does not converge, go to step 1.2 to recalculate the Lagrangian gradient vector and the corresponding Hessian matrix H w ; Step 1.6: If the algorithm converges, determine the weight of each perturbation factor and determine the number of main perturbation factors In the formula, The fundamental component of the induced voltage of the 220kV equipment. The third harmonic component of the induced voltage of 220kV equipment, The fundamental component of the induced voltage of the 110kV equipment. The third harmonic component of the induced voltage of 110kV equipment, is the residual charge value of the device under test.

3. The method for calculating the insulation resistance value of power equipment under the action of multiple disturbance factors in a substation according to claim 1 is characterized in that: In step 2, the main disturbance factor series Numerical sampling is performed to obtain discrete sampling values ​​of each main disturbance factor. An MCD algorithm is used to fit the sampling series of the main disturbance factors to obtain a continuous curve of the real-time model of the main disturbance factors. The specific steps are as follows: Step 2.1: Set the convergence target values ​​of the multi-objective collaborative data fitting algorithm, which are: mean square error fitness α, graphic similarity fitness β, peak-valley consistency fitness Step 2.2: Calculate the mean square error fitness α: Where n is the total number of sampling points, is the disturbance signal amplitude at the i-th sampling point, is the phase of the disturbance signal at the i-th sampling point, is the amplitude of the fitted disturbance signal at the i-th sampling point, is the phase of the fitted disturbance signal at the i-th sampling point, μ is the weight coefficient; Step 2.3: Calculate the graph similarity fitness β: in, is the graphic value at the i-th sampling point, The value of the fitted graph at the i-th sampling point; Step 2.4: Calculate peak-valley consistency fitness Among them, e is the base of natural logarithm, γ is the penalty coefficient, E max and E min Setting a threshold for the disturbance signal; Step 2.5: Calculate the optimization target Maximumg(i) of the multi-objective collaborative data fitting algorithm and iterate: If the algorithm does not converge, re-enter step 2.2 to step 2.4 to calculate the fitness. If the algorithm converges, the real-time model continuous curve of the main disturbance factor is obtained.

4. The method for calculating insulation resistance value of power equipment under the action of multiple disturbance factors of a substation according to claim 1 is characterized in that: In step 3, an adaptive unscented Kalman filter algorithm is used to estimate the parameters of the real-time model curve of the main disturbance factor to obtain the functional relationship y of the main disturbance factor model. The specific steps are: Step 3.1: Calculate the state probability model z according to the input disturbance factor real-time model curve ij : In the formula, i and j are the number of sampling points; Step 3.2: According to the state probability model z ij , calculate the normalization factor g and the matching probability η k : In the formula, n is the total number of sampling points, k is the time level; Step 3.3: Update the matching probability model η in real time k : Z k =H(X i,k -u k ) In the formula, Z k is the observation model, is the observation model with weight coefficients, H(·) is the feedback function, is the observed covariance parameter, d is the constant coefficient; Step 3.4: Assemble and non-aggregate To standardize: Where p is the design parameter; Step 3.5: Initialize the system parameter estimation mean And its mean square error P k : In the formula, is the predicted mean, P k is the error covariance, Q k is the system noise matrix; Step 3.6: Update sampling points In the formula, function(·) is a nonlinear function, Q y,k is the maximum period of time level k; Step 3.7: Update the system parameter estimate mean Step 3.8: Calculate the system uncertainty θ j : Step 3.8: Calculate the system output y:

5. The method for calculating insulation resistance value of power equipment under the action of multiple disturbance factors of a substation according to claim 1 is characterized in that: In the above-mentioned step 4, the applied voltage and leakage current of the power equipment are measured, and the uncorrected insulation resistance value φ of the power equipment is calculated. The uncorrected insulation resistance value is corrected by the functional relationship y of the main disturbance factor model to obtain the corrected insulation resistance value φ of the power equipment. c , the specific steps are: Step 4.1: Measure the applied voltage value U and leakage current I of the power equipment; Step 4.2: Calculate the uncorrected insulation resistance value φ based on the applied voltage U and leakage current I of the power equipment: φ=U / I Step 4.3: Use the disturbance factor function relationship y to correct the uncorrected insulation resistance value φ to obtain the corrected insulation resistance value φ c :

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