Intelligent comprehensive test method and system for power transformer

Abstract

The invention relates to the technical field of power transformer testing, in particular to an intelligent comprehensive testing method and system for a power transformer, and the method comprises the steps: determining an estimated value of the number of vibration sources of the power transformer at the current moment, and analyzing the complexity of each IMF component in a vibration signal at each testing point, separating all vibration separation signals from the reconstructed virtual multi-channel observation signals; comparing the difference between the fundamental frequency of each vibration separation signal and the voltage frequency and the difference between the fundamental frequency of each vibration separation signal and the current frequency, determining an iron core vibration signal and a winding vibration signal, analyzing the complexity of the iron core vibration signal and the winding vibration signal of the vibration signals at each detection point, determining an iron core detection coefficient and a winding detection coefficient, and determining the detection precision of the iron core and the winding. And testing the conditions of an iron core and a winding of the power transformer. The invention aims to improve the accuracy of the comprehensive test result of the power transformer.

Description

Technical Field

[0001] The present application relates to the technical field of power transformer testing, and in particular to an intelligent comprehensive testing method and system for power transformers. Background Art

[0002] The existing comprehensive test method of power transformer usually performs multiple tests on the power transformer to evaluate the abnormal conditions of different components in the power transformer. Among them, the vibration test of the power transformer is an important test method, which is used to detect whether the operating status of the winding and the core in the transformer is abnormal. However, the vibration generated on the surface of the oil tank of the transformer when it is loaded is mainly caused by the mixed vibration signal generated by the winding and the core. Therefore, in order to effectively evaluate the operating status of the winding and the core of the transformer, it is necessary to separate the winding vibration signal and the core vibration signal of the transformer from the vibration signal on the surface of the oil tank.

[0003] However, the existing methods do not take into account that the vibrations of the on-load tap changer, fan rotation, etc. in the transformer will also cause the oil tank vibration, as well as the influence of noise in the collected transformer vibration signal, so that the separated signal cannot accurately reflect the operating conditions of the windings and core of the transformer, and it is difficult to accurately evaluate the operating status of the windings and core in the transformer through vibration testing, which reduces the accuracy of the comprehensive test results of the transformer. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide an intelligent comprehensive testing method and system for power transformers. The technical solutions adopted are as follows: In a first aspect, an embodiment of the present application provides an intelligent comprehensive testing method for a power transformer, the method comprising the following steps: Obtain the vibration signal at each measuring point on the power transformer at the current moment, as well as the load voltage signal and load current signal of the power transformer; The modal decomposition algorithm is used to decompose the vibration signal at each measuring point at the current moment into multiple IMF components and a residual component. The vibration characteristic value of the vibration signal at each measuring point is determined by analyzing the vibration signal at each measuring point, the residual component of each vibration signal and the discrete degree of correlation between all IMF components to obtain the number of vibration sources of the vibration signal at each measuring point; based on the number of vibration sources of the vibration signal at all measuring points, the estimated value of the number of vibration sources of the power transformer at the current moment is determined; By analyzing the complexity of each IMF component in the vibration signal at each measuring point, the reconstructed virtual multi-channel observation signal of the vibration signal at each measuring point is determined, and combined with the estimated value of the number of vibration sources, all vibration separation signals are obtained from the reconstructed virtual multi-channel observation signal; the voltage frequency of the load voltage signal at the current moment, the current frequency of the load current signal and the fundamental frequency of each vibration separation signal are obtained, and the differences between the fundamental frequency of each vibration separation signal and the voltage frequency and the current frequency are compared respectively, to determine the core vibration signal and the winding vibration signal of the vibration signal at each measuring point at the current moment; The complexity of the core vibration signal and the winding vibration signal of the vibration signal at each measuring point is analyzed respectively, the core detection coefficient and the winding detection coefficient of the power transformer at the current moment are determined, and the core and winding conditions of the power transformer at the current moment are tested.

[0005] Preferably, the method for determining the vibration characteristic value of the vibration signal at each measuring point is: The vibration signal at each measuring point, the residual component of the vibration signal and the set of all IMF components are used as a virtual multi-channel observation signal of the vibration signal at each measuring point; The covariance matrix of the virtual multi-channel observation signal of the vibration signal at each measuring point is obtained, and all eigenvalues ​​of the covariance matrix are calculated as the vibration eigenvalues ​​of the vibration signal at each measuring point.

[0006] Preferably, the method for obtaining the number of vibration sources of the vibration signal at each measuring point is: All vibration characteristic values ​​of the vibration signal at each measuring point are used as the input of the threshold segmentation algorithm, and the segmentation threshold is output. The number of all vibration characteristic values ​​greater than the segmentation threshold is used as the number of vibration sources of the vibration signal at each measuring point.

[0007] Preferably, the estimated value of the number of vibration sources of the power transformer at the current moment is the mode of the number of vibration sources of the vibration signals at all measuring points on the power transformer at the current moment.

[0008] Preferably, the method for determining the reconstructed virtual multi-channel observation signal of the vibration signal at each measuring point is: In the vibration signal at each measuring point, the approximate entropy of each IMF component is calculated, and the set consisting of the first preset number of IMF components and the corresponding vibration signals in the results of the approximate entropies of all IMF components in ascending order is used as the reconstructed virtual multi-channel observation signal of the vibration signal at each measuring point.

[0009] Preferably, the step of obtaining all vibration separation signals from the reconstructed virtual multi-channel observation signals includes: The reconstructed virtual multi-channel observation signal of the vibration signal at each measuring point is used as the input of the independent component analysis algorithm, wherein the estimated value of the number of vibration sources of the power transformer is used as the number of independent components in the independent component analysis algorithm, and all separated signals are output as vibration separation signals of the vibration signal at each measuring point.

[0010] Preferably, the method for determining the core vibration signal and the winding vibration signal of the vibration signal at each measuring point at the current moment is: In the vibration signal at each measuring point, the absolute value of the difference between the fundamental frequency of each vibration separation signal and twice the voltage frequency is calculated and recorded as the first absolute value of each vibration separation signal. Among the first absolute values ​​of all vibration separation signals, the vibration separation signal corresponding to the smallest first absolute value is used as the core vibration signal at each measuring point; Calculate the absolute value of the difference between the fundamental frequency of each vibration separation signal and twice the voltage frequency, and record it as the second absolute value of each vibration separation signal. Among the second absolute values ​​of all vibration separation signals, take the vibration separation signal corresponding to the smallest second absolute value as the winding vibration signal at each measuring point.

[0011] Preferably, the method for determining the core detection coefficient and the winding detection coefficient of the power transformer at the current moment is: The core vibration signal and the winding vibration signal at each measuring point in the power transformer are respectively used as the input of the Lempel-Ziv complexity algorithm, and the frequency complexity of the core vibration signal and the frequency complexity of the winding vibration signal at each measuring point are output; The average value of the frequency complexity of the core vibration signal in the vibration signals at all measuring points in the power transformer and the average value of the frequency complexity of the winding vibration signal in the vibration signals at all measuring points are respectively used as the core detection coefficient and the winding detection coefficient of the power transformer.

[0012] Preferably, the testing of the core and winding conditions of the power transformer at the current moment includes: If the core detection coefficient of the power transformer at the current moment is less than the preset first threshold, the core is abnormal, otherwise the core is healthy; if the winding detection coefficient of the power transformer at the current moment is less than the preset second threshold, the winding is abnormal, otherwise the winding is healthy.

[0013] In a second aspect, an embodiment of the present application also provides an intelligent comprehensive testing system for a power transformer, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the steps of any one of the above-mentioned intelligent comprehensive testing methods for the power transformer are implemented.

[0014] This application has at least the following beneficial effects: The present application sequentially performs preliminary denoising processing on the collected vibration signals, and uses the obtained vibration source number estimation value and approximate entropy to filter out the remaining noise in the vibration signals, which can effectively reduce the influence of the noise in the vibration signals on the vibration signals generated by the vibration sources separated in the subsequent vibration test process, thereby improving the accuracy of the winding vibration signals and the core vibration signals obtained by subsequent separation; further, blind source signal separation is performed by reorganizing the virtual multi-channel observation signal composed of the IMF components screened out from the vibration signal, and the fundamental frequency of the obtained vibration separation signal is used to separate the winding vibration signals and the core vibration signals in the collected vibration signals, which can effectively reduce the influence of the vibration of the on-load tap changer, fan and other components in the transformer on the subsequent separation of the winding vibration signals and the core vibration signals, and improve the accuracy of the separated winding vibration signals and the core vibration signals, thereby improving the accuracy of the evaluation of the operating status of the windings and the cores of the power transformer in the vibration test, and thereby improving the accuracy of the comprehensive test results of the power transformer. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 A flowchart of the steps of an intelligent comprehensive testing method for a power transformer provided in one embodiment of the present application; Figure 2 A schematic diagram of a virtual multi-channel observation signal construction process provided by an embodiment of the present application; Figure 3 A schematic diagram of a process for obtaining a core vibration signal and a winding vibration signal provided in one embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the intelligent comprehensive testing method and system for power transformers proposed in the present application, its specific implementation, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0019] The specific scheme of the intelligent comprehensive testing method and system for power transformers provided by the present application is described in detail below with reference to the accompanying drawings.

[0020] See also Figure 1 , which shows a flowchart of the steps of an intelligent comprehensive testing method for a power transformer provided by an embodiment of the present application, the method comprising the following steps: Step S1: Obtain the vibration signal at each measuring point on the power transformer at the current moment, as well as the load voltage signal and load current signal of the power transformer.

[0021] The intelligent comprehensive test system for the power transformer in the present application is composed of multiple test modules, which include insulation resistance test, DC resistance test, ratio test, temperature rise test and vibration test of the power transformer, wherein the insulation resistance test evaluates the insulation performance of the power transformer winding by measuring the insulation resistance between the high-voltage winding and the low-voltage winding of the power transformer to ensure that there is no leakage or short circuit during operation; the DC resistance test measures the resistance value of the power transformer winding to ensure that there is no open circuit or poor contact; the ratio test verifies whether the ratio of the power transformer meets the design requirements by measuring the voltage ratio between the high and low voltage sides of the power transformer; the temperature rise test monitors the temperature of the winding and oil of the power transformer running under full load conditions to ensure that it will not overheat under normal working conditions; the vibration test detects whether the operating status of the winding and core of the power transformer is abnormal by measuring the vibration signal on the surface of the oil tank when the power transformer is in load operation.

[0022] During the vibration test, multiple measuring points are set on the surface of the oil tank of the power transformer, and a vibration acceleration sensor is installed at each measuring point to collect the vibration signal generated on the surface of the oil tank during the vibration test of the transformer to be tested, and the load voltage signal and load current signal of the power transformer within a preset time before and adjacent to the current moment, as well as the vibration signal at each measuring point on the power transformer are respectively obtained as the load voltage signal, load current signal of the power transformer at the current moment, and the vibration signal at each measuring point on the power transformer, wherein the position of the measuring point can be set by the implementer according to the specific situation. In this embodiment, 1 / 4, 1 / 2, and 3 / 4 of the height of the vertical surface of the high-voltage side box body of the transformer to be tested and directly opposite to the winding position of the transformer to be tested are selected as the measuring points of the power transformer.

[0023] Furthermore, the vibration signals at all measuring points are used as inputs of the filtering algorithm to perform noise reduction on the vibration signals. It should be noted that there are many commonly used filtering algorithms. In this embodiment, the Gaussian filtering algorithm is used to perform noise reduction on the vibration signals. In actual application, as other implementation methods, the implementer may also use a wavelet denoising algorithm or a Kalman filtering algorithm. This embodiment does not impose any special restrictions on the selection of the filtering algorithm.

[0024] Among them, the Gaussian filtering algorithm is a well-known technology, and the specific process of performing noise reduction processing on the signal will not be described in detail.

[0025] Step S2: Decompose the vibration signal at each measuring point at the current moment into multiple IMF components and a residual component by using a modal decomposition algorithm; determine the vibration characteristic value of the vibration signal at each measuring point by analyzing the vibration signal at each measuring point, the residual component of each vibration signal and the discrete degree of correlation between all IMF components to obtain the number of vibration sources of the vibration signal at each measuring point; determine the estimated value of the number of vibration sources of the power transformer at the current moment based on the number of vibration sources of the vibration signal at all measuring points.

[0026] Since the vibration signal of the power transformer is a mixture of vibration signals generated by multiple vibration sources in the vibration test, the existing separation problem of each independent source signal in the multi-mixed signal is mainly solved by using blind source separation technology, such as the FastICA algorithm based on multi-channel blind source separation and the SSA-ICA algorithm based on single-channel blind source separation. The multi-channel blind source separation method needs to use the signal matrix formed by multiple observation signals to realize signal separation, while the single-channel blind source separation method needs to decompose the mixed signal collected by a single channel into multiple virtual observation signals, such as using a signal decomposition algorithm to decompose the single-channel signal and convert it into a multi-channel observation signal, and then use the multi-channel blind source separation method to solve it.

[0027] The vibration signal is decomposed into multiple signal components, and the decomposed signal components are combined into a virtual multi-channel observation signal, so that the vibration signals generated by different vibration sources in the vibration signal can be separated. However, the signal components directly decomposed will not only contain the vibration components generated by the vibration source, but also the noise components caused by the noise interference to the vibration signal during its data acquisition. The virtual multi-channel observation signal synthesized using these noise components will affect the separation accuracy of independent vibration signals generated by different vibration sources in the vibration signal, such as the windings of the power transformer, the core, the on-load tap changer, the rotation of the fan, etc.

[0028] Therefore, in order to reduce the influence of the residual noise in the vibration signal on the subsequent separation of vibration signals from different vibration sources, the following processing is performed, specifically: Each vibration signal is used as the input of an improved adaptive noise complete set empirical mode signal decomposition algorithm, and multiple IMF components of each vibration signal and a residual component are output, wherein the IMF component corresponds to a vibration component or a noise component in the vibration signal.

[0029] Among them, the improved adaptive noise complete set empirical mode signal decomposition algorithm is a well-known technology, and its specific principle will not be repeated here.

[0030] Furthermore, each vibration signal, all IMF components of each vibration signal and the rest are combined to form a virtual multi-channel observation signal, which is specifically expressed as follows: B(i)= , where B(i) represents the virtual multi-channel observation signal of the vibration signal at the i-th measuring point; represents the vibration signal at the i-th measuring point; represents the kth IMF component of the vibration signal at the i-th measuring point; res(i) represents the residual component of the vibration signal at the i-th measuring point; T represents the transpose of the matrix.

[0031] Preferably, the schematic diagram of the virtual multi-channel observation signal construction process provided in this embodiment is as follows: Figure 2 shown.

[0032] Since the number of vibration sources appearing in the vibration test of the power transformer is fixed, the number of vibration sources can be estimated, all IMF components containing vibration information of the vibration sources in the vibration signal are retained, and the number of retained IMF components is equal to the number of vibration sources, thereby reducing the influence of residual noise in the vibration signal on the subsequent separation of vibration signals of different vibration sources.

[0033] Specifically, since the vibration signals and noise signals generated by the windings, cores, on-load tap changers and fan rotations of the power transformer are usually uncorrelated, and the signal power of the vibration signal is greater than the noise power, there is an obvious difference between the vibration signal eigenvalues ​​and the noise eigenvalues ​​in the covariance matrix of the virtual multi-channel observation signal, specifically, the difference between the noise eigenvalues ​​is small, while the difference between the vibration signal eigenvalues ​​and the noise eigenvalues ​​is large.

[0034] Based on the above analysis, the covariance matrix of the virtual multi-channel observation signal of the vibration signal at each measuring point is obtained, and all eigenvalues ​​of the covariance matrix are calculated as the vibration eigenvalues ​​of the vibration signal at each measuring point.

[0035] It should be noted that there are many methods for calculating the eigenvalues ​​of a matrix. In this embodiment, a singular value decomposition algorithm is used to obtain all eigenvalues ​​in the covariance matrix. In actual application, as other implementation methods, implementers may also use other eigenvalue decomposition algorithms such as the QR algorithm. This embodiment does not impose any special restrictions on the selection of the method for calculating the eigenvalues ​​of the matrix.

[0036] Among them, the method for obtaining the covariance matrix of the signal and the singular value decomposition algorithm are both well-known technologies, and their specific principles are not repeated here.

[0037] Furthermore, all vibration characteristic values ​​of the vibration signal at each measuring point are used as input of the threshold segmentation algorithm, and the segmentation threshold is output. The number of all vibration characteristic values ​​greater than the segmentation threshold is used as the number of vibration sources of the vibration signal at each measuring point.

[0038] It should be noted that there are many commonly used threshold segmentation algorithms. The maximum inter-class variance algorithm is used in this embodiment. In actual application, as other implementation methods, implementers may also use other threshold segmentation algorithms. Regarding the selection of threshold segmentation algorithms, this embodiment does not make any special restrictions.

[0039] Among them, the maximum between-class variance algorithm is a well-known technology, and its specific principle and process will not be repeated here.

[0040] Furthermore, the mode of the number of vibration sources of the vibration signals at all measuring points on the power transformer at the current moment is used as the estimated value of the number of vibration sources of the power transformer at the current moment, and is used to characterize the estimated result of the number of vibration sources appearing in the vibration test of the power transformer.

[0041] Step S3: by analyzing the complexity of each IMF component in the vibration signal at each measuring point, the reconstructed virtual multi-channel observation signal of the vibration signal at each measuring point is determined, and in combination with the estimated value of the number of vibration sources, all vibration separation signals are separated from the reconstructed virtual multi-channel observation signal; the voltage frequency of the load voltage signal at the current moment, the current frequency of the load current signal and the fundamental frequency of each vibration separation signal are obtained, and the differences between the fundamental frequency of each vibration separation signal and the voltage frequency and the current frequency are compared respectively, to determine the core vibration signal and the winding vibration signal of the vibration signal at each measuring point at the current moment.

[0042] Since the noise signal is usually more disordered than the vibration signals generated by the windings, cores, on-load tap changers, and fan rotation in the power transformer, the noise component in the vibration signal has a greater signal complexity than the vibration component.

[0043] Therefore, the approximate entropy of each IMF component in the vibration signal at each measuring point is calculated respectively to characterize the complexity of the signal in the IMF component. The smaller the approximate entropy of the IMF component, the more vibration information of the power transformer vibration source the IMF component contains, and the less likely it is to be a noise component.

[0044] The calculation of the approximate entropy of the signal is a well-known technique, and the specific process will not be described in detail.

[0045] Furthermore, in the vibration signal at each measuring point, a set consisting of a preset number of IMF components and corresponding vibration signals in the results of arranging the approximate entropies of all IMF components in ascending order is used as a reconstructed virtual multi-channel observation signal of the vibration signal at each measuring point.

[0046] The value of the preset number in this embodiment is numerically equal to the estimated value of the number of vibration sources of the power transformer.

[0047] Furthermore, the reconstructed virtual multi-channel observation signal of the vibration signal at each measuring point is used as the input of an independent component analysis algorithm (FastICA), wherein the estimated value of the number of vibration sources of the power transformer is used as the number of independent components in the independent component analysis algorithm, and all separated signals are output as vibration separation signals of the vibration signal at each measuring point.

[0048] Among them, the independent component analysis algorithm is a well-known technology, and its specific principle will not be repeated here.

[0049] Since the winding vibration of the power transformer is caused by the electromagnetic force generated by the current flowing in the winding, the vibration amplitude is proportional to the square of the winding current, so that the fundamental frequency of the vibration signal generated by the winding is twice the current frequency; and the iron core of the power transformer is composed of stacked silicon steel sheets. The magnetostriction of the silicon steel sheets under a strong magnetic field causes the vibration of the iron core. The vibration amplitude is proportional to the square of the voltage, so that the fundamental frequency of the vibration signal generated by the iron core is twice the voltage frequency.

[0050] Based on the above analysis, the voltage frequency of the load voltage signal and the current frequency of the load current signal are obtained, and the fundamental frequency of each vibration separation signal in each vibration signal is extracted. In the vibration signal at each measuring point, the absolute value of the difference between the fundamental frequency of each vibration separation signal and twice the voltage frequency is calculated and recorded as the first absolute value of each vibration separation signal. Among the first absolute values ​​of all vibration separation signals, the vibration separation signal corresponding to the smallest first absolute value is used as the core vibration signal at each measuring point; Calculate the absolute value of the difference between the fundamental frequency of each vibration separation signal and twice the voltage frequency, and record it as the second absolute value of each vibration separation signal. Among the second absolute values ​​of all vibration separation signals, take the vibration separation signal corresponding to the smallest second absolute value as the winding vibration signal at each measuring point.

[0051] Preferably, the schematic diagram of the process of obtaining the core vibration signal and the winding vibration signal provided in this embodiment is as follows: Figure 3 shown.

[0052] Step S4: Analyze the complexity of the core vibration signal and the winding vibration signal of the vibration signal at each measuring point, determine the core detection coefficient and the winding detection coefficient of the power transformer at the current moment, and perform a comprehensive test on the power transformer at the current moment.

[0053] Since the vibration signals generated by the windings and cores of the power transformer under normal pressure are mainly composed of fundamental frequency, 1st, 3rd and 5th harmonic components, when the windings and cores are abnormal, 50Hz and odd-order multiple frequency components will be generated, making the vibration signals generated by the abnormal windings and cores have more complex signal frequency components than the vibration signals generated by normal windings and cores.

[0054] Based on the above analysis, the core vibration signal and the winding vibration signal at each measuring point in the power transformer are used as the input of the Lempel-Ziv complexity algorithm, and the frequency complexity of the core vibration signal and the frequency complexity of the winding vibration signal at each measuring point are output; The average value of the frequency complexity of the core vibration signal in the vibration signals at all measuring points in the power transformer and the average value of the frequency complexity of the winding vibration signal in the vibration signals at all measuring points are respectively used as the core detection coefficient and the winding detection coefficient of the power transformer.

[0055] The Lempel-Ziv complexity algorithm is a well-known technology, and the specific process of obtaining the frequency complexity using the Lempel-Ziv complexity algorithm will not be described in detail.

[0056] During the vibration test, if the core detection coefficient of the power transformer at the current moment is less than the preset first threshold, the core is abnormal, otherwise the core is healthy; if the winding detection coefficient of the power transformer at the current moment is less than the preset second threshold, the winding is abnormal, otherwise the winding is healthy.

[0057] There is no restriction on the size relationship between the preset first threshold and the preset second threshold. In this embodiment, the values ​​of the preset first threshold and the preset second threshold are both 0.5. The implementer can also set them according to the specific situation. This embodiment does not impose any special restrictions.

[0058] In addition, the test results of the remaining test modules of the power transformer are obtained respectively. In the insulation resistance test module, if the measured insulation resistance should meet the insulation resistance value specified in the standard, usually required to be at least 1MΩ / kV, the output is normal, otherwise the output is abnormal; in the DC resistance test module, if the measured winding resistance is within the error range specified in the standard (usually ±5%), and the deviation between the resistances of each phase winding shall not exceed 2%, the output is normal, otherwise the output is abnormal; in the ratio test module, if the ratio error should meet the requirements of the standard, usually ±0.5%, the output is normal, otherwise the output is abnormal; in the temperature rise test module, if the result meets the temperature rise limit in the standard (such as the winding temperature rise does not exceed 65K), the output is normal, otherwise the output is abnormal.

[0059] Based on the same inventive concept as the above method, an embodiment of the present application also provides an intelligent comprehensive testing system for a power transformer, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the steps of any one of the above-mentioned intelligent comprehensive testing methods for power transformers are implemented.

[0060] It should be noted that the above sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0061] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0062] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application should be included in the protection scope of the present application.

Claims

1. An intelligent comprehensive testing method for power transformers, characterized in that: The method comprises the following steps: Obtain the vibration signal at each measuring point on the power transformer at the current moment, as well as the load voltage signal and load current signal of the power transformer; The modal decomposition algorithm is used to decompose the vibration signal at each measuring point at the current moment into multiple IMF components and a residual component. The vibration characteristic value of the vibration signal at each measuring point is determined by analyzing the vibration signal at each measuring point, the residual component of each vibration signal and the discrete degree of correlation between all IMF components to obtain the number of vibration sources of the vibration signal at each measuring point; based on the number of vibration sources of the vibration signal at all measuring points, the estimated value of the number of vibration sources of the power transformer at the current moment is determined; By analyzing the complexity of each IMF component in the vibration signal at each measuring point, the reconstructed virtual multi-channel observation signal of the vibration signal at each measuring point is determined, and combined with the estimated value of the number of vibration sources, all vibration separation signals are obtained from the reconstructed virtual multi-channel observation signal; the voltage frequency of the load voltage signal at the current moment, the current frequency of the load current signal and the fundamental frequency of each vibration separation signal are obtained, and the differences between the fundamental frequency of each vibration separation signal and the voltage frequency and the current frequency are compared respectively, to determine the core vibration signal and the winding vibration signal of the vibration signal at each measuring point at the current moment; The complexity of the core vibration signal and the winding vibration signal of the vibration signal at each measuring point is analyzed respectively, the core detection coefficient and the winding detection coefficient of the power transformer at the current moment are determined, and the core and winding conditions of the power transformer at the current moment are tested.

2. The intelligent comprehensive testing method for power transformer according to claim 1, characterized in that: The method for determining the vibration characteristic value of the vibration signal at each measuring point is: The vibration signal at each measuring point, the residual component of the vibration signal and the set of all IMF components are used as a virtual multi-channel observation signal of the vibration signal at each measuring point; The covariance matrix of the virtual multi-channel observation signal of the vibration signal at each measuring point is obtained, and all eigenvalues ​​of the covariance matrix are calculated as the vibration eigenvalues ​​of the vibration signal at each measuring point.

3. The intelligent comprehensive testing method for power transformer according to claim 1, characterized in that: The method for obtaining the number of vibration sources of the vibration signal at each measuring point is: All vibration characteristic values ​​of the vibration signal at each measuring point are used as the input of the threshold segmentation algorithm, and the segmentation threshold is output. The number of all vibration characteristic values ​​greater than the segmentation threshold is used as the number of vibration sources of the vibration signal at each measuring point.

4. The intelligent comprehensive testing method for power transformer according to claim 1, characterized in that: The estimated value of the number of vibration sources of the power transformer at the current moment is the mode of the number of vibration sources of the vibration signals at all measuring points on the power transformer at the current moment.

5. The intelligent comprehensive testing method for power transformer according to claim 1, characterized in that: The method for determining the reconstructed virtual multi-channel observation signal of the vibration signal at each measuring point is as follows: In the vibration signal at each measuring point, the approximate entropy of each IMF component is calculated, and the set consisting of the first preset number of IMF components and the corresponding vibration signals in the results of the approximate entropies of all IMF components in ascending order is used as the reconstructed virtual multi-channel observation signal of the vibration signal at each measuring point.

6. The intelligent comprehensive testing method for power transformer according to claim 1, characterized in that: The step of obtaining all vibration separation signals from the reconstructed virtual multi-channel observation signals includes: The reconstructed virtual multi-channel observation signal of the vibration signal at each measuring point is used as the input of the independent component analysis algorithm, wherein the estimated value of the number of vibration sources of the power transformer is used as the number of independent components in the independent component analysis algorithm, and all separated signals are output as vibration separation signals of the vibration signal at each measuring point.

7. The intelligent comprehensive testing method for power transformer according to claim 1, characterized in that: The method for determining the core vibration signal and the winding vibration signal of the vibration signal at each measuring point at the current moment is: In the vibration signal at each measuring point, the absolute value of the difference between the fundamental frequency of each vibration separation signal and twice the voltage frequency is calculated and recorded as the first absolute value of each vibration separation signal. Among the first absolute values ​​of all vibration separation signals, the vibration separation signal corresponding to the smallest first absolute value is used as the core vibration signal at each measuring point; Calculate the absolute value of the difference between the fundamental frequency of each vibration separation signal and twice the voltage frequency, and record it as the second absolute value of each vibration separation signal. Among the second absolute values ​​of all vibration separation signals, take the vibration separation signal corresponding to the smallest second absolute value as the winding vibration signal at each measuring point.

8. The intelligent comprehensive testing method for power transformer according to claim 1, characterized in that: The method for determining the core detection coefficient and the winding detection coefficient of the power transformer at the current moment is: The core vibration signal and the winding vibration signal at each measuring point in the power transformer are respectively used as the input of the Lempel-Ziv complexity algorithm, and the frequency complexity of the core vibration signal and the frequency complexity of the winding vibration signal at each measuring point are output; The average value of the frequency complexity of the core vibration signal in the vibration signals at all measuring points in the power transformer and the average value of the frequency complexity of the winding vibration signal in the vibration signals at all measuring points are respectively used as the core detection coefficient and the winding detection coefficient of the power transformer.

9. The intelligent comprehensive testing method for power transformer according to claim 1, characterized in that: The testing of the core and winding conditions of the power transformer at the current moment includes: If the core detection coefficient of the power transformer at the current moment is less than the preset first threshold, the core is abnormal, otherwise the core is healthy; if the winding detection coefficient of the power transformer at the current moment is less than the preset second threshold, the winding is abnormal, otherwise the winding is healthy.

10. An intelligent comprehensive test system for a power transformer, 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, the steps of the intelligent comprehensive testing method for the power transformer as described in any one of claims 1 to 9 are implemented.

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