A method and system for detecting insulation performance of an insulation layer of a power device

Abstract

The application relates to the technical field of insulation performance detection, in particular to a method and system for detecting the insulation performance of an insulation layer of a power device, which comprises the following steps: acquiring a partial discharge signal, current data and temperature data of the insulation layer of the power device; acquiring a significant value of pulse characteristics of the partial discharge signal and a significant value of periodic similar characteristics of the partial discharge signal, then obtaining abnormal values of the window discharge signals affected by insulation deterioration, and acquiring abnormal values of the current data affected by insulation deterioration through the change randomness of harmonic components of the current data of the power device and the difference degree between the fundamental wave component of the current and the current data; calculating a consistency coefficient of the abnormal characteristic change of insulation deterioration, then obtaining an insulation deterioration degree coefficient to detect the insulation performance of the insulation layer of the power device. The application can improve the insulation performance detection precision of the insulation layer of the power device.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of insulation performance detection, and in particular to an insulation performance detection method and system for an insulation layer of a power device. BACKGROUND

[0002] The power device is a key equipment in industrial production, and the insulation performance reliability thereof directly affects the production efficiency and safety. Since the power device is in long-term high-load operation, the insulation layer is prone to degradation. Therefore, insulation performance detection of the insulation layer of the power device is a key link for guaranteeing stable and efficient operation of industrial production.

[0003] Traditional periodic detection cannot realize real-time monitoring of the degradation state of the insulation. Nowadays, through real-time data analysis, the life cycle management of the insulation layer can be better realized, and the reliability of the equipment can be improved. In the analysis process, the conventional method mainly performs state evaluation by extracting features from collected data. The collected data are mostly partial discharge signals and current signals. In actual application, the features extracted from the collected data cannot accurately reflect the degradation state of the insulation layer due to the influence of factors such as uneven insulation material and environmental electromagnetic interference, and there is a defect of insufficient accuracy of insulation performance detection. SUMMARY

[0004] To solve the above technical problems, the purpose of the application is to provide an insulation performance detection method and system for an insulation layer of a power device, and the technical solution adopted is as follows:

[0005] The application embodiment provides an insulation performance detection method for an insulation layer of a power device, which comprises the following steps:

[0006] Obtaining partial discharge signals, current data of the power device and temperature data of the insulation layer of the power device;

[0007] According to the amplitude fluctuation and distribution level of the partial discharge signals of the power device, the significant value of the pulse characteristics of the partial discharge signals in each window is obtained, and the periodic fluctuation characteristics of the partial discharge signals are analyzed through the correlation between the partial discharge signals in any two extreme points, so as to obtain the significant value of the periodic similar characteristics of the partial discharge signals in each window, and then obtain the abnormal value of the discharge signals in each window affected by insulation degradation. The abnormal value of the current data in each window affected by insulation degradation is obtained through the change randomness of the harmonic component of the current data of the power device and the difference degree between the current fundamental component and the current data.

[0008] According to the correlation between the abnormal value of the discharge signal of each window affected by insulation deterioration, the abnormal value of the current data affected by insulation deterioration, and the temperature change trend, a consistency coefficient of the insulation deterioration abnormal characteristic change of each window is obtained, and an insulation deterioration degree coefficient is obtained by using the change characteristic of the consistency coefficient, so as to detect the insulation performance of the insulation layer of the power device.

[0009] Preferably, the preset time length is taken as a single window, the partial discharge signals in each window are fitted, the position of the maximum amplitude in the extreme points on the fitted curve is taken as a pulse peak point, the curve between the two adjacent minimum points on the left and right of the pulse peak point is taken as a pulse wave peak interval, and the remaining curve is taken as a non-pulse wave peak interval.

[0010] Preferably, the process of obtaining the significant value of the pulse characteristic of the partial discharge signal in each window is as follows: , wherein, is the significant value of the pulse characteristic of the partial discharge signal in the i th window, is the amplitude of the pulse peak point, is the mean value of all partial discharge signals in the non-pulse wave peak interval in the i th window, is a constant to avoid a denominator of 0.

[0011] Preferably, the process of obtaining the significant value of the periodic similar characteristic of the partial discharge signal in each window is as follows: the time length between the adjacent two minimum points on the corresponding fitted curve in each window is taken as a fluctuation period, and the mean value of the correlation coefficients between the curves corresponding to all arbitrary two fluctuation periods is taken as the significant value of the periodic similar characteristic of the partial discharge signal in each window.

[0012] Preferably, the process of obtaining the abnormal value of the discharge signal of each window affected by insulation deterioration is as follows: , wherein, is the abnormal value of the partial discharge signal of the i th window affected by insulation deterioration, is the significant value of the pulse characteristic of the partial discharge signal in the i th window, is the significant value of the periodic similar characteristic of the partial discharge signal in the i th window, is a constant to avoid a denominator of 0.

[0013] Preferably, the process of obtaining the abnormal value of the current data in each window affected by insulation deterioration is as follows: , wherein, is the abnormal value of the current data in the i th window affected by insulation deterioration; the harmonic component and the fundamental component of the current data in each window are extracted, the fractal dimension of the harmonic component of the current data in the i th window is denoted as , and the DTW distance between the fundamental component of the current data in the i th window and the current data is denoted as .

[0014] Preferably, the trend test results of all temperature data in each window are counted, the abnormal values of partial discharge signals affected by insulation deterioration in the preset proximity period of each window, the abnormal values of current data affected by insulation deterioration and the temperature trend test results are respectively arranged in sequence, the average of the correlation coefficients between any two sequences is taken as the consistency coefficient of insulation deterioration abnormal feature changes of each window.

[0015] Preferably, the consistency coefficients of insulation deterioration abnormal feature changes of all windows are arranged in a consistency sequence from small to large according to the average power in the window, and are taken as the input of the Theil-Sen estimation method, and the slope in the output result is taken as the insulation deterioration degree coefficient.

[0016] Preferably, the insulation deterioration degree coefficient is normalized, and if the normalized result is greater than or equal to a preset insulation deterioration evaluation threshold, the insulation layer of the power device is unqualified; otherwise, the insulation layer of the power device is qualified.

[0017] The embodiment of the present application also provides a power device insulation layer insulation performance detection system, which comprises a memory, a processor and a computer program stored in the memory and running on the processor, and the processor implements the steps of the power device insulation layer insulation performance detection method of any one of the above.

[0018] As can be seen from the above, the power device insulation layer insulation performance detection method and system provided by the present application at least has the following beneficial effects:

[0019] The present application further considers the consistency degree of the change of the abnormal features corresponding to the operation data under the influence of insulation deterioration and the change relationship with the load condition by deeply analyzing the abnormal features corresponding to the partial discharge signals, current data and insulation layer temperature in the operation process of the power device, analyzes the insulation deterioration degree, and performs real-time evaluation of the insulation performance based on this. The advantage is that it can reduce the influence of uneven insulation materials and environmental electromagnetic interference, accurately quantify the real-time deterioration degree of the insulation layer, and help to make up for the defects of insufficient accuracy of insulation performance detection. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings needed in the embodiment or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creating any creative labor.

[0021] Figure 1A step flow chart of a method for detecting insulation performance of an insulation layer of a power device is provided in the present application. DETAILED DESCRIPTION

[0022] In order to further illustrate the technical means and effects taken by the present application to achieve the predetermined object, the following describes in detail the specific implementation, structure, features and effects of a method and system for detecting insulation performance of an insulation layer of a power device according to the present application in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0023] Unless otherwise defined, the terms such as "comprise", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the circuit structure, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed or inherent to such article or device. Without more limitations, the element defined by the phrase "comprising one" does not exclude the presence of additional identical elements in the article or device comprising the element. In addition, the term "and / or" used herein includes any and all combinations of one or more related listed items. All technical and scientific terms used herein have the same meaning as understood by those skilled in the art to which the present application belongs.

[0024] The specific scheme of the method and system for detecting insulation performance of an insulation layer of a power device provided by the present application is described in detail below in combination with the accompanying drawings.

[0025] Please refer to Figure 1 which shows a step flow chart of a method for detecting insulation performance of an insulation layer of a power device provided by one embodiment of the present application, including the following steps:

[0026] Step one: obtaining partial discharge signals, current data of the power device and temperature data of the insulation layer of the power device.

[0027] When the electric field intensity at the damage of the insulation layer is higher than the dielectric strength of the medium during the operation of the power device, partial discharge will occur. Monitoring the partial discharge state of the device helps to determine the degree of deterioration of the insulation layer. Therefore, in this embodiment, a partial discharge sensor is installed to collect the partial discharge signal. In addition, insulation layer deterioration will cause abnormal operation temperature and electrical data of the device. Therefore, in this embodiment, a temperature sensor is installed on the insulation layer of the power device to collect temperature data of the power device, and a high-precision current sensor is installed on the input and output lines of the power device to collect current data. To avoid the problem of inconsistent dimensions, all collected data in this embodiment are normalized to realize dimensionless data analysis and avoid the influence of inconsistent dimensions on subsequent data analysis and insulation performance detection.

[0028] It should be noted that in this embodiment, all the above sensors continuously collect various operating data of the power device at a sampling frequency of 200HZ. After A / D conversion, all collected data are transmitted to the data analysis module through Ethernet.

[0029] Step two: According to the amplitude fluctuation and distribution level of the partial discharge signal of the power device, the significant value of the pulse characteristics of the partial discharge signal in each window is obtained, and the periodic fluctuation characteristics of the partial discharge signal are analyzed through the correlation between the partial discharge signals in any two extreme points, to obtain the significant value of the periodic similar characteristics of the partial discharge signal in each window, and then obtain the abnormal value of the partial discharge signal in each window affected by insulation deterioration. The abnormal value of the current data in each window affected by insulation deterioration is obtained through the change randomness of the harmonic component of the current data and the difference degree between the fundamental component of the current and the current data.

[0030] The data analysis module is mainly responsible for feature extraction and diagnosis of the collected operating data. Due to the influence of non-uniform insulation materials and environmental factors in the actual monitoring process, the accurate extraction of insulation deterioration characteristics is affected. To reduce the interference of external factors and improve the detection accuracy, the following processing is performed in the data analysis module.

[0031] Firstly, when the insulation layer has defects, local discharge phenomenon is easy to occur due to its high electric field intensity, and in the initial stage of deterioration, the energy of the partial discharge is small, which also causes the gradual deterioration of the insulation layer. As the degree of insulation layer deterioration is higher, the energy of the partial discharge is larger, and finally the insulation layer is broken down. Therefore, the energy of the partial discharge signal can reflect the degree of deterioration of the insulation layer. To obtain the short-term energy characteristics of the partial discharge, the time length of a single window is set to 0.5 seconds in this embodiment.

[0032] Further, taking the i-th window as an example, a fitting curve of the partial discharge signal data in the window is obtained by using a least square fitting technique. Under normal operating conditions, the fitting curve of the partial discharge signal periodically fluctuates around 0, and when the partial discharge phenomenon occurs, there is a high-amplitude pulse in the fitting curve, and the greater the amplitude of the pulse, the greater the energy of the partial discharge. Further, all extreme points in the obtained fitting curve are obtained, and the position of the maximum amplitude among all the extreme points is taken as the pulse peak point. The curve part between the pulse peak point and the two adjacent minimum points on the left and right is taken as the pulse peak interval, and the remaining curve is taken as the non-pulse peak interval. The partial discharge signal amplitude in the pulse peak interval is generally high, which reflects the size of the discharge energy. Further, the average of all partial discharge signals in the non-pulse peak interval in the i-th window is calculated and denoted as . Further, according to the amplitude at the pulse peak point and the average level of the signal amplitude in the non-pulse peak interval, the saliency of the pulse feature of the partial discharge signal is obtained, and specifically, in this embodiment, the calculation formula is: , wherein is the saliency of the pulse feature of the partial discharge signal in the i-th window, is the average of all partial discharge signals in the non-pulse peak interval in the i-th window, represents the amplitude of the pulse peak point, is a constant to avoid division by zero, and the value range is 0.001 to 0.1, and the value is 0.05. The obtained is greater, indicating that the pulse feature of the partial discharge in the window is more obvious.

[0033] In addition, when the partial discharge phenomenon occurs, the waveform of the partial discharge signal shows extremely irregular periodic fluctuation characteristics, which is caused by the serious distortion of the electric field at the damaged insulation layer. Therefore, in this embodiment, taking the i-th window as an example, the time length between two adjacent minimum points on the fitting curve is taken as a fluctuation period, and further, preferably, in this embodiment, the Spearman correlation coefficient of the curve corresponding to any two fluctuation periods is calculated, and the average of all obtained Spearman correlation coefficients is taken as the saliency of the periodic similarity feature of the partial discharge signal in the window, denoted as . The smaller the obtained , the more different the fluctuations of the partial discharge signals between different periods. It should be noted that in actual application scenarios, the implementer of the correlation coefficient can use other existing measurement methods, and in this embodiment, the Spearman correlation coefficient is used, and no special limitation is made here.

[0034] Further, according to the saliency of the pulse feature of the partial discharge signal and the saliency of the periodic similarity feature of the partial discharge signal, the abnormal value of the partial discharge signal affected by insulation deterioration in each window is calculated, and the formula is: wherein, is the abnormal value of the partial discharge signal of the i-th window affected by insulation deterioration, is the significant value of the pulse characteristics of the partial discharge signal of the i-th window, is the significant value of the periodic similar characteristics of the partial discharge signal in the i-th window, and the result reflects the pulse characteristics of the partial discharge signal in the window and the abnormal fluctuation characteristics between different periods.

[0035] Step three: According to the correlation between the abnormal value of the partial discharge signal of each window affected by insulation deterioration, the abnormal value of the current data affected by insulation deterioration, and the temperature change trend, obtain the consistency coefficient of the insulation deterioration abnormal characteristic change of each window, and use the change characteristics of the consistency change coefficient to obtain the insulation deterioration degree coefficient, to detect the insulation performance of the insulation layer of the power device.

[0036] Further, considering that when insulation deterioration occurs, the current data collected during the operation of the power device has a corresponding harmonic current. If the detected harmonic current characteristics are more significant, it indicates that the operation of the power device is more likely to be affected by insulation deterioration. In view of this, taking the current data in the i-th window as an example, in this embodiment, the harmonic component in the current data is extracted by using the sliding window iterative algorithm (SDFT), and the harmonic component is consistent with the size of the current data. The current data is composed of harmonic components and fundamental components, and the result obtained by subtracting the harmonic component from the current data is the fundamental component of the current data. It should be noted that the process of obtaining the harmonic component and the fundamental component of the current data in each window can be realized by the prior art, and will not be described in detail herein.

[0037] For the harmonic component of the current data of the power device, if the change of the harmonic component is more random, and the deviation between the current data and the fundamental component is greater, the insulation layer is more likely to be damaged. Further, first, the fractal dimension of the harmonic component of the current data in the i-th window is calculated by using the Higuchi algorithm, denoted as , the result is greater, indicating that the fluctuation of the current data corresponding to the harmonic component in the window is more chaotic. Then, the DTW distance between the fundamental component obtained in the i-th window and the current data is calculated, denoted as . The result is greater, indicating that the deviation between the fundamental component and the current data in the window is higher. Thus, the abnormal value of the current data affected by insulation deterioration in the i-th window is calculated, and the formula is: , the result reflects the harmonic abnormality of the current data and the deviation degree characteristics of the fundamental signal.

[0038] Further, under the influence of insulation deterioration, as the number of partial discharges increases, part of the electrical energy is converted into heat energy, causing the temperature of the insulation layer to increase. To monitor the temperature change characteristics of the insulation layer, in the embodiment, the trend test result of all temperature data in the i-th window is obtained using the Theil-Sen estimation method, and is denoted as , the obtained reflects the temperature change trend characteristics of the power device in the window. It should be noted that the trend test implementer can select other existing trend test algorithms for the temperature data in the window in actual application scenarios, and the embodiment does not make special restrictions on this.

[0039] During the insulation performance detection process, there may be electromagnetic interference of the environment or other devices, which may cause abnormal partial discharge or current harmonic, and reduce the accuracy of insulation performance detection. However, when the insulation is defective, the abnormal characteristic changes among the monitored operating data are usually consistent. Therefore, in order to reduce the influence of environmental electromagnetic interference, the insulation performance is monitored by analyzing the correlation of abnormal characteristic changes corresponding to different operating data.

[0040] Specifically, first, the near neighbor period of each window is preset. Preferably, in the embodiment, the time length of the i-th window and the N windows before it is set as the near neighbor period of the i-th window, wherein the value range of N is [30, 35], and all abnormal values of the partial discharge signals affected by insulation deterioration, abnormal values of the current data affected by insulation deterioration, and temperature trend test results in the near neighbor period of the i-th window are arranged in time sequence; further, the correlation coefficient between any two sequences is calculated, which is the Pearson correlation coefficient in the embodiment, and the mean value of all Pearson correlation coefficients is taken as the consistency coefficient of insulation deterioration abnormal characteristic changes, and the consistency coefficient of insulation deterioration abnormal characteristic changes of the i-th window is denoted as . The obtained reflects the consistency degree of the corresponding abnormal characteristic changes of different operating parameters under the influence of insulation deterioration.

[0041] In addition, under different load conditions, the insulation performance of the power device also changes. Specifically, when in a high load state, the energy of partial discharge, the harmonic characteristics of the current, and the temperature rise characteristics of the insulation layer are abnormally increased compared with those in a normal state. When the insulation performance state abnormally increases with the change of the load, it indicates that the deterioration degree of the insulation layer is more serious. Therefore, in the embodiment, the consistency coefficient of the insulation deterioration abnormal characteristic change of all windows and the corresponding average power in each window are obtained. Further, in the embodiment, the consistency coefficients of the insulation deterioration abnormal characteristic change of all windows are arranged in a consistency sequence from small to large according to the average power corresponding to the windows, and the change characteristics of the consistency sequence are analyzed by using the Theil-Sen estimation method. The slope in the output result is taken as the insulation deterioration degree coefficient L in the embodiment. The larger the obtained L is, the more significant the feature that the insulation performance deteriorates with the increase of the load is.

[0042] According to the above process, the insulation deterioration degree coefficient can be obtained. Further, in the embodiment, real-time evaluation of the insulation performance is performed based on this. The insulation deterioration degree coefficient is normalized. If the normalized result is greater than or equal to a preset insulation deterioration evaluation threshold, the insulation layer of the power device is unqualified; otherwise, the insulation layer of the power device is qualified. Specifically, first, the obtained insulation deterioration degree coefficient is normalized by using a tanh function, and the insulation deterioration evaluation threshold is set as the third quantile in the interval [0, 1]. If the normalized result is greater than or equal to the insulation deterioration evaluation threshold, the insulation layer is damaged, the insulation performance is poor, and unqualified; otherwise, if it is less than the insulation deterioration evaluation threshold, it is considered that the insulation layer is not damaged, and the insulation performance is good. In this way, it is helpful to make up for the defects of insufficient accuracy of the insulation performance detection.

[0043] Based on the same inventive concept as the above method, the embodiment of the present application also provides an insulation performance detection system for an insulation layer of a power device, which comprises a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of the insulation performance detection method for the insulation layer of the power device according to any one of the above embodiments when executing the computer program.

[0044] It can be understood that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The above describes a specific embodiment of the present application. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0045] The various embodiments in the specification are described in progressive manner, and the same or similar parts between the various embodiments can be mutually referred to, and each embodiment focuses on the difference from other embodiments.

[0046] The above is only the embodiment of the present application, and is not used to limit the scope of the present application. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the protection scope of the present application.

Claims

1. A method for testing the insulation performance of an insulating layer in a power device, characterized in that, Includes the following steps: Acquire partial discharge signals, current data, and temperature data of the insulation layer of power devices; Based on the amplitude fluctuation and distribution level of the partial discharge signal of the power device, the significant values ​​of the pulse characteristics of the partial discharge signal in each window are obtained. The periodic fluctuation characteristics of the partial discharge signal are analyzed by the correlation between the partial discharge signals in any two extreme points, and the significant values ​​of the periodic similarity characteristics of the partial discharge signal in each window are obtained. Then, the abnormal values ​​of the discharge signal in each window affected by insulation degradation are obtained. Furthermore, the abnormal values ​​of the current data affected by insulation degradation in each window are obtained by the randomness of the change of the harmonic components of the current data of the power device and the degree of difference between the fundamental current component and the current data. Based on the correlation between the abnormal values ​​of discharge signals affected by insulation degradation in each window, the abnormal values ​​of current data affected by insulation degradation, and the temperature change trend, the consistency coefficient of the abnormal characteristics of insulation degradation in each window is obtained, and the insulation degradation degree coefficient is obtained by using the change characteristics of the consistency coefficient, so as to detect the insulation performance of the insulation layer of power devices. The preset duration is used as a single window to fit the partial discharge signal within each window. The position with the largest amplitude among the extreme points on the fitted curve is taken as the pulse peak point. The curve between the two adjacent minimum points on the left and right of the pulse peak point is taken as the pulse peak interval, and the remaining curve is taken as the non-pulse peak interval. The process for obtaining the significant values ​​of pulse characteristics in the partial discharge signals of each window is as follows: ,in, For the i-th window partial discharge signal, there is a significant value indicating the presence of pulse characteristics. Indicates the amplitude of the pulse peak point. Let be the mean of all partial discharge signals within the non-pulse peak interval of the i-th window. To avoid constants with a denominator of 0; The process of obtaining the significance value of the periodic similarity feature of partial discharge signals in each window is as follows: the time length between two adjacent minimum points on the fitted curve corresponding to each window is recorded as a fluctuation period, and the mean value of the correlation coefficient between the curves corresponding to any two fluctuation periods is calculated as the significance value of the periodic similarity feature of partial discharge signals in each window. The process for obtaining the outliers in the discharge signals of each window affected by insulation degradation is as follows: ;in, This represents the outlier value of the partial discharge signal in the i-th window affected by insulation degradation. For the i-th window partial discharge signal, there is a significant value indicating the presence of pulse characteristics. The is the significant value of the periodic similarity feature of the partial discharge signal within the i-th window. To avoid constants with a denominator of 0.

2. The method for testing the insulation performance of the insulating layer of a power device as described in claim 1, characterized in that, The process for obtaining outliers in the current data within each window due to insulation degradation is as follows: ;in, Let be the outlier of the current data within the i-th window due to insulation degradation; extract the harmonic and fundamental components of the current data within each window, and denote the fractal dimension of the harmonic components of the current data within the i-th window as . And calculate the DTW distance between the fundamental component of the current data and the current data within the i-th window, denoted as . .

3. The method for testing the insulation performance of the insulating layer of a power device as described in claim 1, characterized in that, The trend test results of all temperature data within each window are statistically analyzed. The outliers of partial discharge signals affected by insulation degradation, the outliers of current data affected by insulation degradation, and the temperature trend test results within the preset neighboring time periods of each window are arranged in chronological order. The mean of the correlation coefficients between any two sequences is used as the consistency coefficient of the abnormal changes in insulation degradation characteristics of each window.

4. The method for testing the insulation performance of the insulating layer of a power device as described in claim 1, characterized in that, The consistency coefficients of all window insulation degradation anomaly characteristics are arranged in ascending order of average power within the window to form a consistency sequence, which is used as the input of the Thiel-Sen estimation method. The slope in the output result is used as the insulation degradation degree coefficient.

5. The method for testing the insulation performance of the insulating layer of a power device as described in claim 1, characterized in that, The insulation degradation coefficient is normalized. If the normalization result is greater than or equal to the preset insulation degradation assessment threshold, the insulation of the power device insulation layer is unqualified; otherwise, the insulation of the power device insulation layer is qualified.

6. A system for testing the insulation performance of an insulating layer of a power device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method for detecting the insulation performance of the insulating layer of a power device as described in any one of claims 1-5.

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