Partial discharge diagnosis method based on phase correlation and phase statistical characteristics of ultrasonic signals

Abstract

The application discloses a partial discharge diagnosis method based on phase correlation and phase statistical characteristics of ultrasonic signals, comprising the following steps: S01, based on the RLS algorithm, the collected ultrasonic signals are subjected to noise reduction processing to eliminate white noise, and Fourier method is used to filter out interference signals below 20 kHz; S02, while the step 1 operation is being performed, the starting point of the collected ultrasonic signals is found out; S03, the original signal is down-sampled to 500 data points by using the discrete Fourier transform, the frequency spectrum is windowed and then inverse transformed, the fuzzy entropy of the down-sampled signal is calculated, and a first diagnosis model is constructed; S04, the sampling points of the original data are reduced to 3600 data points in the form of taking the maximum value at a certain interval. The partial discharge diagnosis method based on phase correlation and phase statistical characteristics of ultrasonic signals can effectively determine whether the switch cabinet has a partial discharge phenomenon, effectively reduces the false positive rate, and can be used for online detection and daily inspection of electrical equipment such as switch cabinets in multiple scenes.

Description

Technical Field

[0001] This invention relates to the field of ultrasonic application technology, and specifically to a method for diagnosing partial discharge based on the phase correlation and phase statistical characteristics of ultrasonic signals. Background Technology

[0002] Power systems are composed of numerous electrical devices, whose close connections form a vast power network. Partial discharge is one of the most common and destructive types of faults in various electrical devices, such as switchgear. Therefore, timely diagnosis of partial discharge is of great significance for the stable operation of equipment.

[0003] Based on the characteristics of partial discharge, various detection methods have been developed. These can be categorized by detection type into electrical detection methods (such as pulsed current methods and ultra-high frequency methods) and non-electrical detection methods (such as ultrasonic methods and ultra-high frequency methods). Among these, non-electrical detection methods are gaining increasing attention due to their strong resistance to electromagnetic interference and the ability to perform online detection without affecting normal equipment operation. For example, according to publication (announcement) number CN115421007A, publication (announcement) date: 2022-12-02, an ultrasonic partial discharge detection device is disclosed. Ultrasonic detection, as a non-electrical detection method, is a detection approach that has gradually emerged in recent years. Its basic principle is to couple partial discharge ultrasonic signals through a sensor, thereby reflecting the internal condition of the equipment.

[0004] In the prior art, including the aforementioned patents, since there are various types of partial discharge inside electrical equipment, the accompanying ultrasonic signals contain rich feature information. Therefore, by analyzing the ultrasonic signals, it is possible to diagnose whether the equipment has experienced partial discharge in a timely and accurate manner, which is expected to be well resolved! Summary of the Invention

[0005] The purpose of this invention is to provide a partial discharge diagnosis method based on the phase correlation and phase statistical characteristics of ultrasound signals, in order to solve the above-mentioned problems.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for diagnosing partial discharge based on the phase correlation and phase statistical characteristics of ultrasound signals, comprising the following steps:

[0007] S01. Based on the RLS algorithm and Fourier method, the acquired ultrasonic signal is denoised to remove white noise and interference signals in the frequency band below 20kHz.

[0008] S02. While performing step 1, locate the starting point of the acquired ultrasound signal;

[0009] S03. Use Discrete Fourier Transform to downsample the original signal to 500 data points, calculate the fuzzy entropy of the downsampled signal, and construct the first diagnostic model.

[0010] S04. Reduce the number of sampling points of the original data to 3600 data points by taking the maximum value at certain intervals;

[0011] S05. Based on the 50Hz correlation principle, obtain the position of the maximum value in each cycle, and combine the phase statistical characteristics of the partial discharge signal to construct the second and third diagnostic models respectively.

[0012] S06. Obtain the diagnostic results of the first diagnostic model, the second diagnostic model, and the third diagnostic model, and determine whether partial discharge has occurred based on the diagnostic results.

[0013] Preferably, the sampling frequency of the ultrasonic signal in step 1 is 500K and the sampling time is 1s.

[0014] Preferably, the noise reduction process includes filtering out white noise and eliminating various periodic and non-periodic interferences.

[0015] Preferably, the RLS algorithm filters out white noise mixed in the original ultrasonic signal. If the amplitude of a certain data point is too large, it is considered to be interference and is set to 0. The correlation coefficient between two adjacent signal periods is calculated to determine whether it is periodic interference. Then, the original signal is subjected to Fourier transform to set the frequency band below 20kHz to 0, and then the inverse transform is performed to obtain the partial discharge ultrasonic signal.

[0016] Preferably, in step 2, the correlation between two adjacent windows is calculated with a window length of 20ms per period and a step size of 1. Preferably, in step 3, the downsampling of the original signal in the ultrasound signal is performed using Fourier transform to downsample the original signal to 500 data points.

[0017] Wherein: the embedding dimension of fuzzy entropy is set to 1, the dimension for calculating fuzzy membership degree is set to 3, and the similarity tolerance is set to 0.15.

[0018] Preferably, in the 50Hz correlation execution judgment, if the difference between the position of the maximum value of other cycles is less than or equal to the threshold, it is considered to be the same phase; if the number of times the phase occurs is divided by 50 and is greater than or equal to the threshold r, it is considered to have partial discharge.

[0019] Preferably, in step 5, the downsampled 3600 data points are divided into 72 points per period, for a total of 50 periods, and then 50 are constructed. Given a matrix of 72, calculate the mean of each column to form a matrix of 1. The eigenvector T_mean of 72 is then calculated, and its variance / mean is calculated. If the variance / mean is greater than the threshold R, then the presence of partial discharge is considered.

[0020] In the above technical solution, the present invention provides a partial discharge diagnosis method based on the phase correlation and phase statistical characteristics of ultrasonic signals, which has the following beneficial effects: by analyzing the characteristics of partial discharge ultrasonic signals, a partial discharge identification system is constructed by fusing three diagnostic models. Compared with other diagnostic methods, this method not only effectively improves the accuracy of partial discharge diagnosis, but also greatly reduces the false alarm rate of the models. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0022] Figure 1 This is a schematic diagram of the process structure provided in an embodiment of the present invention;

[0023] Figure 2 This is a schematic diagram illustrating the synchronization effect provided in an embodiment of the present invention;

[0024] Figure 3 A schematic diagram of the fuzzy entropy distribution of partial discharge signal and no partial discharge signal provided in an embodiment of the present invention;

[0025] Figure 4 This is a schematic diagram showing the distribution of the variance / mean of the eigenvector T_mean, which is composed of the phase mean values ​​with and without partial discharge, according to an embodiment of the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0027] like Figure 1-4 As shown, a partial discharge diagnosis method based on the phase correlation and phase statistical characteristics of ultrasound signals includes the following steps:

[0028] S01. Based on the RLS algorithm and Fourier method, noise reduction processing is performed on the acquired ultrasonic signal (sampling frequency of 500K and sampling time of 1s) (including filtering out white noise and eliminating various periodic and non-periodic interferences) to eliminate interference signals in the frequency band below 20kHz.

[0029] The S11 and RLS algorithms filter out white noise mixed in the original ultrasonic signal. If the amplitude of a certain data point is too large, it is considered to be interference and is set to 0. The correlation coefficient between two adjacent signal periods is calculated to determine whether it is periodic interference. Then, the original signal is subjected to Fourier transform to set the frequency band below 20kHz to 0, and then the inverse transform is performed to obtain the partial discharge ultrasonic signal.

[0030] S02. While performing step 1, find the starting point of the acquired ultrasound signal;

[0031] S21. Calculate the correlation between two adjacent windows with a window length of 20ms as one period and a step size of 1. Determine the starting point based on the magnitude of the correlation coefficient cor.

[0032] S03. Use Discrete Fourier Transform to downsample the original signal to 500 data points, calculate the fuzzy entropy of the downsampled signal, and construct the first diagnostic model.

[0033] S31. Downsampling: The original signal in the ultrasound signal is downsampled to 500 data points using Fourier transform.

[0034] Wherein: the embedding dimension of fuzzy entropy is set to 1, the dimension for calculating fuzzy membership degree is set to 3, and the similarity tolerance is set to 0.15.

[0035] S04. Reduce the number of sampling points of the original data to 3600 data points by taking the maximum value at certain intervals;

[0036] S05. Based on the 50Hz correlation principle, obtain the position of the maximum value in each cycle, and combine the phase statistical characteristics of the partial discharge signal to construct the second and third diagnostic models respectively.

[0037] In the S51, 50Hz related execution judgment, if the difference between the position of the maximum value of other cycles is less than or equal to the threshold, it is considered to be the same phase; if the number of times the phase occurs is divided by 50 and is greater than or equal to the threshold r, it is considered to have partial discharge.

[0038] S52, the downsampled 3600 data points are divided into 72 points per cycle, for a total of 50 cycles, and then constituted as 50... Given a matrix of 72, calculate the mean of each column to form a matrix of 1. The eigenvector T_mean of 72 is then calculated, and its variance / mean is calculated. If the variance / mean is greater than the threshold R, then the presence of partial discharge is considered.

[0039] S06. Obtain the diagnostic results of the first diagnostic model, the second diagnostic model, and the third diagnostic model, and determine whether partial discharge has occurred based on the diagnostic results.

[0040] The diagnostic results of the three diagnostic models are statistically analyzed. If at least one model diagnoses the presence of partial discharge, then partial discharge is considered to have occurred; otherwise, it is considered that no partial discharge has occurred.

[0041] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0042] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0043] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0044] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0045] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

[0046] The embodiments of this application also provide a specific implementation of an electronic device capable of implementing all the steps in the methods described above. The electronic device specifically includes the following:

[0047] Processor, memory, communications interface, and bus;

[0048] The processor, memory, and communication interface communicate with each other through the bus.

[0049] The processor is used to call a computer program in the memory, and when the processor executes the computer program, it implements all the steps in the method described in the above embodiments.

[0050] Embodiments of this application also provide a computer-readable storage medium capable of implementing all the steps of the methods in the above embodiments, wherein the computer-readable storage medium stores a computer program that, when executed by a processor, implements all the steps of the methods in the above embodiments.

[0051] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on its differences from other embodiments. In particular, for hardware + program embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. Although the embodiments in this specification provide the method operation steps as shown in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive means. The order of steps listed in the embodiments is merely one possible execution order among many steps and does not represent the only execution order. In practice, when the device or terminal product executes, it can be executed in the order shown in the embodiments or drawings or in parallel (e.g., in a parallel processor or multi-threaded processing environment, or even a distributed data processing environment). The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, product, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, product, or apparatus. Without further limitations, it is not excluded that other identical or equivalent elements may exist in the process, method, product, or apparatus that includes said elements. For ease of description, the above devices are described in terms of function, divided into various modules. Of course, in implementing the embodiments of this specification, the functions of each module can be implemented in one or more software and / or hardware, or the module implementing the same function can be implemented by a combination of multiple sub-modules or sub-units, etc. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings or direct couplings or communication connections may be through some interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms. This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which are executable by the processor of the computer or other programmable data processing device, produce instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0052] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, the embodiments of this specification can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The various embodiments in this specification are described in a progressive manner, and similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be referred to the description of the method embodiments. In the description of this specification, the reference to the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., means that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the embodiments of this specification.

[0053] In this specification, the illustrative expressions of the terms used do not necessarily refer to the same embodiments or examples. Furthermore, those skilled in the art can combine and integrate different embodiments or examples described in this specification, as well as features of different embodiments or examples, without contradiction. The above descriptions are merely embodiments of this specification and are not intended to limit the embodiments of this specification. Various modifications and variations can be made to the embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the embodiments of this specification should be included within the scope of the claims of the embodiments of this specification.

Claims

1. A partial discharge diagnosis method based on phase correlation and phase statistical features of ultrasonic signals, characterized by, The method comprises the following steps: S01, based on the RLS algorithm and the Fourier method, the collected ultrasonic signal is denoised to remove white noise and interference signals below 20 kHz; S02, while step 1 is operating, the starting point of the collected ultrasonic signal is found out; S03, the original signal is down-sampled to 500 data points by using discrete Fourier transform, the fuzzy entropy of the down-sampled signal is calculated, a first diagnosis model is constructed, the first diagnosis model is to judge whether the fuzzy entropy of the signal time domain waveform is increased, if the fuzzy entropy is greater than 1.5, it is considered that partial discharge occurs, wherein: the embedding dimension of the fuzzy entropy is 1, the dimension when calculating the fuzzy membership degree is 3, and the similarity tolerance degree is 0.15; S04, the sampling points of the original data are reduced to 3600 data points in a certain interval by taking the maximum value; S05, according to the 50Hz correlation principle, the position of the maximum value in each cycle is obtained, and meanwhile, combined with the phase statistical characteristics of the partial discharge signal, a second diagnosis model and a third diagnosis model are respectively constructed, wherein: The second diagnosis model is to statistically analyze whether there is a rule in the phase of the maximum value according to the correlation principle, if the phase distribution of the maximum value exists a rule, it is considered that partial discharge occurs; The third diagnosis model is to divide the 3600 down-sampled data points into 72 points per cycle, a total of 50 cycles, then form a 50*72 matrix, calculate the mean value of each column of the matrix to form a 1*72 feature vector T_mean, then calculate the variance / mean value of the feature vector T_mean, if the variance / mean value is greater than a threshold R, it is considered that there is partial discharge; S06, the diagnosis results of the first diagnosis model, the second diagnosis model and the third diagnosis model are obtained, and whether partial discharge occurs is determined based on the diagnosis results.

2. The partial discharge diagnostic method based on phase correlation and phase statistical features of ultrasonic signals according to claim 1, characterized in that, The sampling frequency of the ultrasonic signal in step 1 is 500KHz, and the sampling time is 1s.

3. The partial discharge diagnostic method based on phase correlation and phase statistical features of ultrasonic signals according to claim 1, characterized in that, The denoising process includes filtering white noise and removing various periodic and aperiodic interference.

4. The partial discharge diagnostic method based on phase correlation and phase statistical features of ultrasonic signals according to claim 1, characterized in that, The RLS algorithm filters the white noise mixed in the original signal of the ultrasonic signal, if the amplitude of a certain data point is too large, it is considered to be interference and is set to 0, then the correlation coefficient of the adjacent two signal cycles is calculated to determine whether it is periodic interference, then the Fourier transform is performed on the original signal to set the frequency band below 20 kHz to 0, and then the inverse transform is performed to obtain the partial discharge ultrasonic signal.

5. The partial discharge diagnostic method based on phase correlation and phase statistical features of ultrasonic signals according to claim 1, characterized in that, In step 2, the correlation between adjacent two windows is calculated in a way that the window length is one cycle, i.e. 20ms, and the step length is 1, and the position of the starting point is determined according to the size of the correlation coefficient cor.

6. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to realize the steps of the partial discharge diagnosis method based on the phase correlation and phase statistical characteristics of the ultrasonic signal according to any one of claims 1-2.

7. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the partial discharge diagnosis method based on the phase correlation and phase statistical characteristics of the ultrasonic signal according to any one of claims 1-2.

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