A fault diagnosis method and system based on wideband data compression processing

Abstract

This invention belongs to the field of signal analysis and processing technology, and provides a fault diagnosis method and system based on broadband data compression processing. The method includes: acquiring the fault current signal of the optical-pile-load system; decomposing the fault current signal using wavelet transform to obtain the wavelet packet coefficients and decomposition levels for each frequency band of the fault current; calculating the corresponding wavelet energy entropy based on the wavelet packet decomposition results of the fault current signal under various combinations of wavelet functions and decomposition levels, thereby determining the optimal combination of wavelet mother function and decomposition level; performing wavelet packet decomposition on the normal current signal and the fault current signal based on the optimal combination of wavelet mother function and decomposition level, reconstructing the current signals of each frequency band before and after the fault, and calculating the Tsallis entropy ratio of the current signals of each frequency band before and after the fault to determine the characteristic frequency bands of the fault current signal. This invention can accurately extract the fault characteristic frequency bands from the MHz-level broadband electrical state data of the optical-pile-load system.

Description

Technical Field

[0001] This invention belongs to the field of signal analysis and processing technology, and in particular relates to a fault diagnosis method and system based on broadband state data compression processing. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] With the development of new energy consumption links such as distributed photovoltaic, new energy vehicles, and energy-saving frequency conversion loads, a new energy consumption pattern of distributed photovoltaic-charging pile-frequency conversion load has gradually formed on the customer side. However, accidents such as photovoltaic DC arc faults, short circuits during electric vehicle charging, and abnormal off-grid disconnection of frequency conversion loads occur frequently, seriously threatening the operational safety and power quality of the customer-side system.

[0004] Distributed photovoltaic systems, charging piles, and frequency conversion loads are connected to the customer-side system at a high proportion through power electronic devices. The wideband dynamic characteristics of power electronic devices cause a large number of low, medium, and high-order harmonics to be injected into the system when the equipment fails, making the fault electrical characteristics exhibit wideband dynamic distribution characteristics. For example, the DC fault arc harmonic component of photovoltaic systems can reach the MHz level.

[0005] However, the compression processing method for MHz-level wideband status data for fault identification of distributed photovoltaic-charging pile-frequency conversion load equipment is not yet perfect, and it is impossible to accurately and efficiently extract data of fault characteristic frequency bands, which poses a huge challenge to the identification of safety hazards in customer-side systems. Summary of the Invention

[0006] To address the technical problems mentioned above, this invention provides a fault diagnosis method and system based on broadband data compression processing. It accurately and efficiently extracts fault characteristic frequency bands from the MHz-level broadband electrical status data of the customer-side distributed photovoltaic-charging pile-frequency conversion load system, thereby providing an effective data foundation for identifying safety hazards and diagnosing faults in the customer-side system.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] The first aspect of the present invention provides a fault diagnosis method based on broadband data compression processing.

[0009] A fault diagnosis method based on broadband state data compression processing includes:

[0010] Collect fault current signals from the AC busbar of the optical-charging-equipment device;

[0011] The fault current signal is decomposed by wavelet packet transform to obtain the wavelet packet coefficients of each frequency band of the fault current.

[0012] Based on the wavelet packet decomposition results of the fault current signal under various combinations of wavelet functions and decomposition levels, calculate the corresponding wavelet energy entropy and wavelet energy distribution variance, and use these two indicators to determine the optimal combination of wavelet mother function and decomposition level.

[0013] Based on the optimal wavelet function and the combination of decomposition levels, wavelet packet decomposition is performed on the normal current signal and the fault current signal respectively. The current signals of each frequency band before and after the fault are reconstructed, and the Tsallis entropy ratio of the current signals of each frequency band before and after the fault is calculated to determine the characteristic frequency band of the fault current signal.

[0014] Furthermore, the specific process of decomposing the fault current signal using wavelet packet transform to obtain the wavelet packet coefficients of each frequency band of the fault current includes: constructing a wavelet packet base library based on the selected wavelet function; and decomposing the fault current signal using the wavelet packet base library to obtain the wavelet packet coefficients of each frequency band of the fault current.

[0015] Furthermore, the wavelet energy entropy and wavelet energy distribution variance are:

[0016]

[0017] Among them, W E Let σ be the wavelet energy entropy. ED Let be the variance of the wavelet energy distribution, j be the number of decomposition levels, and n be the index of each sub-band component obtained from the j-level wavelet packet decomposition (n = 0, 1, ... 2). j -1), Let X be the (n+1)th sub-band component sequence obtained from the j-th layer wavelet packet decomposition, N be the number of sub-bands with non-zero wavelet packet coefficients among all frequency band components, i be the index of the sub-band component with non-zero wavelet packet coefficients (n = 0, 1, ..., N), and P(X) be the probability density of sequence X. Specifically, E represents the energy contained in the (n+1)th sub-band component. n The energy contained in all sub-band components and

[0018] Furthermore, the process of calculating the corresponding wavelet energy entropy to determine the optimal combination of wavelet mother function and decomposition level specifically includes: the larger the wavelet energy entropy, the more sub-bands containing energy, and the more random the energy distribution of each band; the smaller the wavelet energy entropy, the fewer sub-bands containing energy, and the more regular the energy distribution of each sub-band. When the wavelet energy entropy is the smallest, the corresponding wavelet mother function and decomposition level are the optimal combination of wavelet mother function and decomposition level.

[0019] Furthermore, when the difference between the energy entropies of two wavelets is less than a set threshold, the corresponding wavelet energy distribution variance is calculated. When the wavelet energy distribution variance is minimized, the corresponding wavelet mother function and decomposition level are the optimal combination of wavelet mother function and decomposition level.

[0020] Furthermore, the Tsallis entropy ratio of the current signals in each sub-band before and after the fault is:

[0021]

[0022] in, This represents the Tsallis entropy ratio of the current signal in the (n+1)th sub-band before and after the fault. The probability density of the wavelet packet coefficients in the (n+1)th sub-band is... M is the number of wavelet packet coefficients in the sub-band component, q is the non-extensibility parameter, and N and F represent the normal state and fault state, respectively.

[0023] Furthermore, the calculation of the Tsallis entropy ratio of the current signals in each frequency band before and after the fault, in order to determine the characteristic frequency band of the fault current signal, specifically includes: selecting the frequency band with the largest Tsallis entropy ratio as the characteristic frequency band of the fault current signal.

[0024] A second aspect of the present invention provides a fault diagnosis system based on broadband data compression processing.

[0025] A fault diagnosis system based on broadband data compression processing includes:

[0026] The data acquisition module is configured to acquire the fault current signal of the optical-charging-load system;

[0027] The wavelet decomposition module is configured to decompose the fault current signal using wavelet packet transform to obtain the wavelet packet coefficients of each frequency band of the fault current.

[0028] The optimal combination determination module is configured to: calculate the corresponding wavelet energy entropy and wavelet energy distribution variance based on the wavelet packet decomposition results of the fault current signal at various combinations of wavelet functions and decomposition levels, thereby determining the optimal combination of wavelet functions and decomposition levels.

[0029] The fault frequency band determination module is configured to: perform wavelet packet decomposition on the normal current signal and the fault current signal based on the optimal wavelet mother function and the combination of decomposition levels, reconstruct the current signals of each frequency band before and after the fault, calculate the Tsallis entropy ratio of the current signals of each frequency band before and after the fault, and determine the characteristic frequency band of the fault current signal.

[0030] A third aspect of the present invention provides a computer-readable storage medium.

[0031] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the fault diagnosis method based on broadband data compression processing as described in the first aspect above.

[0032] A fourth aspect of the present invention provides a computer device.

[0033] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the steps in the fault diagnosis method based on broadband data compression processing as described in the first aspect above.

[0034] Compared with the prior art, the beneficial effects of the present invention are:

[0035] This invention calculates the corresponding wavelet energy entropy and wavelet energy distribution variance based on the wavelet packet decomposition results of the fault current signal under various combinations of wavelet mother functions and decomposition levels, and determines the optimal combination of wavelet mother functions and decomposition levels accordingly. Based on the optimal combination of wavelet mother functions and decomposition levels, wavelet packet decomposition is performed on normal and fault current signals to reconstruct the current signals of each sub-frequency band and normalize them. The Tsallis entropy ratio of the current in each sub-frequency band before and after the fault is calculated to determine the characteristic frequency band of the fault current signal. This invention can accurately and efficiently extract the fault characteristic frequency bands of MHz-level broadband electrical status data from customer-side distributed photovoltaic-charging pile-frequency converter load systems, thereby providing an effective data foundation for identifying safety hazards and diagnosing faults in customer-side systems. Attached Figure Description

[0036] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0037] Figure 1 This is a flowchart of the fault diagnosis method based on broadband data compression processing according to the present invention. Detailed Implementation

[0038] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0039] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, 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 invention pertains.

[0040] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0041] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of this disclosure. It should be noted that each block in a flowchart or block diagram may represent a module, segment, or portion of code, which may include one or more executable instructions for implementing the logical functions specified in the various embodiments. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutively represented blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, may be implemented using a dedicated hardware-based system that performs the specified functions or operations, or using a combination of dedicated hardware and computer instructions.

[0042] Example 1

[0043] like Figure 1 As shown, this embodiment provides a fault diagnosis method based on broadband data compression processing. This embodiment uses the application of this method to a server as an example for illustration. It is understood that this method can also be applied to terminals, and can also be applied to systems including terminals, servers, and other components, and can be implemented through interaction between the terminal and the server. The server can be an independent physical server, a server cluster composed of multiple physical servers, or a distributed system. It can also be a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network servers, cloud communication, middleware services, domain name services, CDN security services, and big data and artificial intelligence platforms. The terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. The terminal and server can be directly or indirectly connected via wired or wireless communication, which is not limited herein. In this embodiment, the method includes the following steps:

[0044] Acquire fault current signals from the optical-pile-load system;

[0045] The fault current signal is decomposed by wavelet packet transform to obtain the wavelet packet coefficients of each frequency band of the fault current.

[0046] Based on the wavelet packet decomposition results of the fault current signal under various combinations of wavelet functions and decomposition levels, the corresponding wavelet energy entropy and wavelet energy distribution variance are calculated to determine the optimal combination of wavelet mother function and decomposition level.

[0047] Based on the optimal wavelet mother function and the combination of decomposition levels, wavelet packet decomposition is performed on the normal current signal and the fault current signal respectively. The current signals of each frequency band before and after the fault are reconstructed, and the Tsallis entropy ratio of the current signals of each frequency band before and after the fault is calculated to determine the characteristic frequency band of the fault current signal.

[0048] The specific solution of this embodiment can be implemented using the following methods:

[0049] Wavelet packet decomposition is performed on the fault current signal to obtain the wavelet packet coefficients of each frequency band. The corresponding normalized sub-energy of each frequency band is calculated, and then the corresponding wavelet energy entropy and wavelet energy distribution variance are calculated. Based on this, the optimal combination of wavelet mother function and decomposition level is determined.

[0050] Wavelet packet decomposition is performed on normal and fault current signals to obtain wavelet packet coefficients for each frequency band. The normalized current for each frequency band is calculated, and then the Tsallis entropy ratio of the current in each sub-frequency band before and after the fault is calculated. Based on this, the fault characteristic frequency band is determined.

[0051] Furthermore, the step of performing wavelet packet decomposition on the fault current signal to obtain wavelet packet coefficients for each frequency band, calculating the corresponding normalized sub-energy for each frequency band, and then calculating the corresponding wavelet energy entropy and wavelet energy distribution variance, and using these as a basis to determine the optimal combination of wavelet mother function and decomposition level, includes:

[0052] Step 1: Compare the properties of common wavelet mother functions and make an initial selection of a suitable wavelet mother function;

[0053] As shown in Table 1, the vanishing moments of Haar and Meyr wavelets are relatively small, while those of BiorN... r .N d Since wavelets are asymmetric and none of them are suitable for extracting the characteristic frequency bands of fault current signals, we initially consider selecting a suitable wavelet mother function from dbN, symN, and coifN wavelets.

[0054] Table 1 Common wavelet mother functions and their properties

[0055] category Biorthogonal Tight support symmetry Vanishing moment Haar yes yes symmetry 1 meyr yes no symmetry — dbN yes yes Approximately symmetric N symN yes yes Approximately symmetric N coifN yes yes Approximately symmetric 2N <![CDATA[biorN r .N d ]]> yes yes asymmetry <![CDATA[N r -1]]>

[0056] Step 2: Construct the corresponding wavelet packet basis library using the initially selected wavelet mother function, and apply it to the fault current i. F The sampled value i of (t)F (x) Perform j-level wavelet packet decomposition to obtain the corresponding wavelet packet coefficients;

[0057] Let the orthogonal scaling function be φ(x), and the wavelet function be... Let μ0(x) = φ(x), The wavelet packet μ is determined by φ(x) n (x) is shown in equation (1):

[0058]

[0059] Where, n, l∈Z + h k It is a high-pass filter sequence and g k It is a low-pass filter sequence and

[0060] small wavelet packet μ n The scaling and translation system of (x) {2 -j / 2 μ n (2 -j xk); n∈Z + ;j,k∈Z} constitute L 2 A set of orthonormal bases of (R) is used to form a wavelet packet space of scale j. For the fault current signal i F (x) can be obtained by performing j-level wavelet packet decomposition. The wavelet packet coefficients in the middle are The wavelet packet decomposition is shown in equation (2):

[0061]

[0062] Step 3: Calculate the corresponding sub-energy E of each sub-band using the wavelet packet coefficients of each sub-band. n,j and total energy E j And perform normalization processing;

[0063] Fault current signal i F (x) performs j-level wavelet packet decomposition to obtain 2 j There are n sub-bands. The wavelet packet coefficients corresponding to the nth sub-band are... Define the sub-energy E corresponding to this sub-band. n,j As shown in equation (3):

[0064]

[0065] Where M is the number of wavelet packet coefficients contained in the nth sub-band. The 2j wavelet packet decomposition results can be obtained. j The total energy of each sub-band is The sub-energy E of each sub-bandn,j After normalization, the corresponding normalized sub-energy is obtained.

[0066] When performing wavelet packet decomposition, if the wavelet mother function and the fault current i F The higher the waveform similarity of (t), the more concentrated the corresponding energy. Therefore, if the calculated sub-energys The larger the value, the more closely the corresponding wavelet mother function interacts with the fault current i in that sub-frequency band. F The more similar the (t) values ​​are, the better the decomposition effect will be in that frequency band.

[0067] Step 4, using the normalized sub-energy E n,j / E j Calculate the corresponding wavelet energy entropy W E and the variance σ of the wavelet energy distribution ED

[0068] Using wavelet mother function For the fault current signal i F (x) Obtain the wavelet packet coefficient sequence of each sub-band when performing j-level wavelet packet decomposition. and its sub-energy The distribution law is And there are Define wavelet energy entropy W E and the variance σ of the wavelet energy distribution ED As shown in equation (4):

[0069]

[0070] The wavelet energy entropy W is calculated based on the result of step 3. E and the variance of energy distribution σ ED According to equation (4) and the physical meaning of entropy, the larger the wavelet energy entropy, the more sub-bands containing energy, and the more random the energy distribution of each sub-band; the smaller the wavelet energy entropy, the fewer sub-bands containing energy, and the more regular the energy distribution of each sub-band.

[0071] The characteristic frequencies of actual fault signals often concentrate in a certain frequency band, i.e., the fault characteristic frequency band, so their energy distribution has a certain regularity. Therefore, the smaller the wavelet energy entropy, the better the wavelet mother function and fault current signal i are across the entire frequency band. F The higher the waveform similarity of (t), the optimal combination of wavelet mother function and decomposition level. Therefore, wavelet energy entropy can be used as a basis for selecting wavelet mother function and decomposition level.

[0072] Furthermore, the smaller the variance of the wavelet energy distribution, the more concentrated the energy, meaning the more regular the energy distribution. Therefore, the variance of the wavelet energy distribution can serve as another basis for selecting the wavelet mother function and the number of decomposition layers.

[0073] Step 5: Determine the optimal combination of the wavelet mother function and the number of decomposition levels;

[0074] Comparing the wavelet energy entropies obtained by using various combinations of wavelet mother functions and decomposition levels, the combination with the minimum wavelet energy entropy is selected as the optimal wavelet mother function. And the number of decomposition layers j best Combination. If the wavelet energy entropy of two combinations is very close, the combination with the smaller wavelet energy distribution variance is selected as the optimal combination.

[0075] Compared to other methods for determining the optimal combination of wavelet mother function and decomposition level, this method considers the contributions of energy and information entropy to the signal orderliness, thereby deriving two criteria: wavelet energy entropy and wavelet energy distribution variance, to determine the optimal combination. Furthermore, the wavelet energy entropy has better convergence characteristics, thus optimizing the traditional method.

[0076] Furthermore, wavelet packet decomposition is performed on the normal and fault current signals to obtain the wavelet packet coefficients of each frequency band. The normalized current of each frequency band is calculated, and then the Tsallis entropy ratio of the current in each sub-frequency band before and after the fault is calculated. Based on this, the fault characteristic frequency bands are determined, including:

[0077] Based on the optimal wavelet mother function And the number of decomposition layers j best For normal current signal i N (x) and fault current signal i F (x) Perform wavelet packet decomposition to obtain the wavelet packet coefficients corresponding to each sub-band.

[0078] Using wavelet packet coefficients of each sub-band Reconstruct and extract the current signals of each sub-frequency band. and And perform normalization processing;

[0079] The wavelet packet reconstruction formula is shown in equation (5):

[0080]

[0081] The current signal reconstruction formula for each sub-band is shown in equation (6):

[0082]

[0083] The normal current signals of each sub-band are reconstructed according to equation (6). and fault current signal and The current signals of each sub-frequency band are normalized to obtain the corresponding normalized current signals. and This is equivalent to normalizing the wavelet packet coefficients of each sub-band.

[0084] Calculate the Tsallis entropy ratio of the current before and after the fault based on the normalized current of each frequency band under normal and fault conditions.

[0085] Using wavelet mother function Perform j on the current signal i(x) best The wavelet packet coefficient sequence of each sub-band is obtained during layer wavelet packet decomposition. Its distribution law is obtained as follows make The Tsallis entropy ratio of the current before and after the fault for each sub-frequency band is shown in equation (7):

[0086]

[0087] Where q is the extensive parameter, and here we take q = 1.2.

[0088] According to equation (7) and the negative entropy theory, the larger the Tsallis entropy increment of the reconstructed current signal in a certain frequency band before and after a fault, the greater the amount of fault information contained in this frequency band, which corresponds to the fault characteristic frequency band. Therefore, the Tsallis entropy increment can be used as the basis for determining the fault characteristic frequency band.

[0089] A reasonable selection of the extensive parameter q of the Tsallis entropy can better reflect the complexity of current signal harmonics, and thus better reflect the corresponding fault characteristics. Furthermore, the Tsallis entropy can handle spectral aliasing and energy leakage during wavelet packet transform, making the extraction of feature frequency bands more accurate. Therefore, this method has higher accuracy compared to traditional feature frequency band extraction methods.

[0090] Step 6: Determine the characteristic frequency band of the fault current signal.

[0091] By comparing the ratio of the reconstructed current signal Tsallis entropy of each sub-band before and after the fault, the sub-band with the largest Tsallis entropy ratio is selected as the characteristic frequency band.

[0092] This embodiment addresses the analysis needs of customer-side distributed photovoltaic-charging pile-frequency conversion load systems containing MHz-level broadband fault electrical characteristics. It studies compression processing techniques related to the status data of photovoltaic charging equipment to accurately and efficiently extract the electrical characteristics of characteristic frequency bands during normal and fault operation. This helps to clarify the broadband dynamic characteristics of the customer-side system under normal operation and various fault conditions, and to construct a typical fault broadband feature library. This, in turn, assists the customer-side system in high-speed and high-precision safety hazard identification and fault diagnosis, laying a model foundation for safety hazard identification and risk assessment.

[0093] Example 2

[0094] This embodiment provides a fault diagnosis system based on broadband data compression processing.

[0095] A fault diagnosis system based on broadband data compression processing includes:

[0096] The data acquisition module is configured to acquire the fault current signal of the optical-charging-load system;

[0097] The wavelet decomposition module is configured to decompose the fault current signal using wavelet packet transform to obtain the wavelet packet coefficients of each frequency band of the fault current.

[0098] The optimal combination determination module is configured to: calculate the corresponding wavelet energy entropy and wavelet energy distribution variance based on the wavelet packet decomposition results of the fault current signal at various combinations of wavelet functions and decomposition levels, thereby determining the optimal combination of wavelet mother function and decomposition level.

[0099] The fault frequency band determination module is configured to: perform wavelet packet decomposition on the normal current signal and the fault current signal based on the optimal wavelet function and the combination of decomposition levels, reconstruct the current signals of each frequency band before and after the fault, calculate the Tsallis entropy ratio of the current signals of each frequency band before and after the fault, and determine the characteristic frequency band of the fault current signal.

[0100] It should be noted that the data acquisition module, wavelet packet decomposition module, optimal combination determination module, and fault frequency band determination module described above are the same examples and application scenarios implemented in the steps of Embodiment 1, but are not limited to the content disclosed in Embodiment 1. It should also be noted that the above modules, as part of the system, can be executed in a computer system such as a set of computer-executable instructions.

[0101] Example 3

[0102] This embodiment provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps in the fault diagnosis method based on broadband data compression processing as described in Embodiment 1 above.

[0103] Example 4

[0104] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the fault diagnosis method based on broadband data compression processing as described in Embodiment 1 above.

[0105] 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 hardware embodiments, software embodiments, or embodiments 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 and optical storage) containing computer-usable program code.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0110] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A fault diagnosis method based on broadband data compression processing, characterized in that, include: Acquire fault current signals from the optical-pile-load system; Wavelet transform is used to decompose the fault current signal to obtain the wavelet packet coefficients and decomposition levels of each frequency band of the fault current. Based on the wavelet packet decomposition results of the fault current signal under various combinations of wavelet functions and decomposition levels, the corresponding wavelet energy entropy is calculated to determine the optimal combination of wavelet mother function and decomposition level. Determine the optimal combination of wavelet mother function and decomposition level; Comparing the wavelet energy entropies obtained by using various combinations of wavelet mother functions and decomposition levels, the combination with the minimum wavelet energy entropy is selected as the optimal wavelet mother function. and decomposition layer number Combination; if the wavelet energy entropy of two combinations is very close, then the combination with the smaller wavelet energy distribution variance is selected as the optimal combination; Based on the optimal wavelet mother function and the combination of decomposition levels, wavelet packet decomposition is performed on the normal current signal and the fault current signal respectively. The current signals of each frequency band before and after the fault are reconstructed, and the Tsallis entropy ratio of the current signals of each frequency band before and after the fault is calculated to determine the characteristic frequency band of the fault current signal.

2. The fault diagnosis method based on broadband data compression processing according to claim 1, characterized in that, The specific process of using wavelet transform to decompose the fault current signal and obtain the wavelet packet coefficients and decomposition level of each frequency band of the fault current includes: constructing a wavelet packet base library based on the selected wavelet mother function; and using the wavelet packet base library to decompose the fault current signal and obtain the wavelet packet coefficients and decomposition level of each frequency band of the fault current.

3. The fault diagnosis method based on broadband data compression processing according to claim 1, characterized in that, The wavelet energy entropy and wavelet energy distribution variance are: in, For wavelet energy entropy, Let Variance be the wavelet energy distribution variance. The number of decomposition layers, for The sequence number of each sub-band component obtained by layer wavelet packet decomposition ( ), for The first layer of wavelet packet decomposition obtained Sub-band component sequence This represents the number of sub-bands with non-zero wavelet packet coefficients in all frequency band components. The index of the sub-band component with non-zero wavelet packet coefficients ( ), For sequence The probability density; specifically, , for The energy contained in each sub-band component The energy contained in all sub-band components and .

4. The fault diagnosis method based on broadband data compression processing according to claim 1, characterized in that, The process of calculating the corresponding wavelet energy entropy to determine the optimal combination of wavelet mother function and decomposition level specifically includes: the larger the wavelet energy entropy, the more sub-bands containing energy, and the more random the energy distribution of each band; the smaller the wavelet energy entropy, the fewer sub-bands containing energy, and the more regular the energy distribution of each sub-band. When the wavelet energy entropy is the smallest, the corresponding wavelet mother function and decomposition level are the optimal combination of wavelet mother function and decomposition level.

5. The fault diagnosis method based on broadband data compression processing according to claim 4, characterized in that, When the difference between the energy entropies of two wavelets is less than a set threshold, the corresponding wavelet energy distribution variance is calculated. When the wavelet energy distribution variance is minimized, the corresponding wavelet mother function and decomposition level are the optimal combination of wavelet mother function and decomposition level.

6. The fault diagnosis method based on broadband data compression processing according to claim 1, characterized in that, The Tsallis entropy ratios of the current signals in each frequency band before and after the fault are: in, Before and after the fault The Tsallis entropy ratio of each sub-band current signal For the first The first of the sub-bands The probability density of the wavelet packet coefficients is , This represents the number of wavelet packet coefficients in this sub-frequency band component. For non-extensive parameters, and These represent normal and fault states, respectively.

7. The fault diagnosis method based on broadband data compression processing according to claim 1, characterized in that, The calculation of the Tsallis entropy ratio of the current signals in each frequency band before and after the fault, in order to determine the characteristic frequency band of the fault current signal, specifically includes: selecting the frequency band with the largest Tsallis entropy ratio as the characteristic frequency band of the fault current signal.

8. A fault diagnosis system based on broadband data compression processing, characterized in that, include: The data acquisition module is configured to acquire the fault current signal of the optical-pile-load system; The wavelet decomposition module is configured to decompose the fault current signal using wavelet transform to obtain the wavelet packet coefficients and decomposition levels for each frequency band of the fault current. The optimal combination determination module is configured to: calculate the corresponding wavelet energy entropy based on the wavelet packet decomposition results of the fault current signal at various combinations of wavelet functions and decomposition levels, thereby determining the optimal combination of wavelet mother function and decomposition level. Determine the optimal combination of wavelet mother function and decomposition level; Comparing the wavelet energy entropies obtained by using various combinations of wavelet mother functions and decomposition levels, the combination with the minimum wavelet energy entropy is selected as the optimal wavelet mother function. and decomposition layer number Combination; if the wavelet energy entropy of two combinations is very close, then the combination with the smaller wavelet energy distribution variance is selected as the optimal combination; The fault frequency band determination module is configured to: perform wavelet packet decomposition on the normal current signal and the fault current signal based on the optimal wavelet mother function and the combination of decomposition levels, reconstruct the current signals of each frequency band before and after the fault, calculate the Tsallis entropy ratio of the current signals of each frequency band before and after the fault, and determine the characteristic frequency band of the fault current signal.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the fault diagnosis method based on broadband data compression processing as described in any one of claims 1-7.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the fault diagnosis method based on broadband data compression processing as described in any one of claims 1-7.

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