High-voltage equipment partial discharge signal processing method

Abstract

The invention relates to a method for processing partial discharge signals of high-voltage equipment. The method comprises the following steps: collecting the partial discharge signals of the high-voltage equipment; performing signal decomposition on the acquired partial discharge signals through an improved adaptive noise complete set empirical mode decomposition algorithm to obtain a plurality of eigenmode functions; constructing a mixed white noise data set, and performing signal decomposition on mixed white noise in the mixed white noise data set through an improved adaptive noise complete set empirical mode decomposition algorithm to obtain a plurality of reference white noise eigenmode functions; counting the energy density based on all reference white noise eigenmode functions, and setting the upper and lower boundaries of the white noise energy density according to a preset confidence level; and comparing all the eigenmode functions with the upper and lower boundaries of the white noise energy density, eliminating the eigenmode functions in the upper and lower boundaries of the white noise energy density, and performing signal reconstruction on the remaining eigenmode functions to obtain a denoised partial discharge signal.

Description

TECHNICAL FIELD

[0001] The application relates to a high-voltage equipment partial discharge signal processing method and belongs to the technical field of insulation detection. BACKGROUND

[0002] Partial discharge is the core early feature of the deterioration of the insulation system of high-voltage equipment and is also the key inducement of equipment insulation failure and power system outage. The signal characteristics of partial discharge directly reflect the insulation state of high-voltage equipment and are the core indicators for evaluating the operation reliability and residual life of the equipment. As the core component of the power system, the stable operation of high-voltage equipment such as power transformers, high-voltage cables and circuit breakers directly determines the continuity and safety of power supply of the power grid. With the development of the power system towards high voltage and large capacity, the insulation of high-voltage equipment is subjected to multiple actions such as electric field stress, temperature and humidity changes and mechanical loss, and the problem of partial discharge caused by insulation deterioration is increasingly prominent. Once a fault occurs, it will cause huge maintenance costs and large-scale power outage losses. Therefore, accurate detection and analysis of the partial discharge signal of high-voltage equipment is of great importance.

[0003] Currently, partial discharge signal detection has become the mainstream technical path for monitoring the insulation state of high-voltage equipment. Partial discharge signals are essentially transient, nonlinear and non-stationary pulse signals. In actual detection scenarios, the collected signals are inevitably mixed with interference signals such as white noise and periodic pulses due to the influence of the complex electromagnetic environment in the substation. White noise is similar to the characteristics of partial discharge signals and becomes the main obstacle to signal extraction. In traditional detection methods, although the offline electrical detection method based on IEC60270 standard can quantify partial discharge parameters, the equipment needs to be removed from the power grid and disassembled, which cannot meet the requirement of modern power grid for power supply continuity. Traditional signal processing methods such as Fourier transform and wavelet transform either lose time-frequency domain information or rely on manual experience to select basis functions and threshold values, which are difficult to adapt to the complex characteristics of partial discharge signals, resulting in difficulty in balancing noise suppression and signal fidelity.

[0004] In the prior art, although non-traditional live detection methods based on high-frequency current transformers (HFCT) have appeared, which can collect signals from the grounding or neutral system without stopping the operation of the equipment, solving the limitations of offline detection, the signals collected by this kind of method are still interfered by strong noise, and existing signal denoising techniques have problems such as incomplete decomposition, modal aliasing and strong subjectivity in noise screening, making it difficult to realize accurate extraction and effective separation of partial discharge signals. Therefore, it is an urgent need to develop a partial discharge signal processing method that is suitable for non-traditional live detection scenarios and has high efficient noise suppression capability and signal fidelity, to break through the technical bottleneck of high-voltage equipment state monitoring and ensure the stable operation of the power grid. SUMMARY

[0005] In order to solve the problems existing in the prior art, the application provides a partial discharge signal processing method for high-voltage equipment.

[0006] The technical scheme of the application is as follows: In one aspect, the application provides a partial discharge signal processing method for high-voltage equipment, comprising the following steps: Collecting a partial discharge signal of high-voltage equipment; Performing signal decomposition on the collected partial discharge signal by using an improved adaptive complete ensemble empirical mode decomposition algorithm to obtain a plurality of intrinsic mode functions; Constructing a mixed white noise data set, performing signal decomposition on mixed white noise in the mixed white noise data set by using the improved adaptive complete ensemble empirical mode decomposition algorithm to obtain a plurality of reference white noise intrinsic mode functions; Statistically calculating energy density based on all reference white noise intrinsic mode functions, and setting upper and lower boundaries of white noise energy density according to a preset signal level; Comparing all intrinsic mode functions with the upper and lower boundaries of white noise energy density, eliminating intrinsic mode functions within the upper and lower boundaries of white noise energy density, and then performing signal reconstruction on the remaining intrinsic mode functions to obtain a denoised partial discharge signal.

[0007] Preferably, the partial discharge signal of the high-voltage equipment is collected from the ground or the neutral system of the high-voltage equipment by a high-frequency current transformer sensor.

[0008] Preferably, the specific steps of performing signal decomposition on the collected partial discharge signal by using the improved adaptive complete ensemble empirical mode decomposition algorithm are as follows: Defining an empirical mode decomposition operator of different modes; Defining a local mean operator; Constructing a plurality of white noise sets and setting an initial noise amplitude coefficient and a plurality of mode noise amplitude coefficients; Calculating a first-order intrinsic mode function, and the specific steps are as follows: Based on the partial discharge signal, constructing a noise-added signal for each white noise set, and the specific formula is as follows:

[0009] Wherein: represents at the moment The noise-added signal is constructed; represents the partial discharge signal; represents the initial noise amplitude coefficient; represents the white noise set; Calculating the local mean of each noise-added signal by using the local mean operator, and the specific formula is as follows:

[0010] in: express The local mean; Represents the local mean operator; The arithmetic mean of the local means of all noisy signals is taken as follows:

[0011] in: This represents the arithmetic mean of the local means of all noisy signals; Indicates the total number of white noise groups; Define the first-order eigenmode function The specific formula is as follows:

[0012] Update the first-order residual The specific formula is as follows:

[0013] Determine if the residuals meet the stopping condition; if not, continue the decomposition process. Calculate the first The specific steps for determining the order intrinsic modulus function are as follows: Through the first The first-order empirical mode decomposition operator decomposes each group of white noise, obtaining the first-order corresponding white noise for each group. The order intrinsic modulus function is shown in the following equation:

[0014] in: Indicates the first The first group of white noise corresponding to the eigenmode functions of order; Indicates the first Order-order empirical mode decomposition operator; pass The first-order residual and the first-order residual corresponding to each group of white noise Construction of eigenmodular functions of order The noise-added residual for each group of white noise is shown in the following formula:

[0015] in: express Rank The noise-added residuals corresponding to the white noise group; express Order residual; express order noise amplitude coefficient; for The local mean of the noise-added residual error corresponding to each group of white noise is calculated as follows:

[0016] wherein: represents The local mean of the noise-added residual error corresponding to the first group of white noise; for The local mean of the noise-added residual error corresponding to each group of white noise is taken as an arithmetic mean , as follows:

[0017] The first eigenmode function is defined as follows:

[0018] The first residual error is updated as follows:

[0019] It is determined whether the residual error meets the stopping condition, and if not, the decomposition is continued.

[0020] Preferably, the stopping condition further includes an evaluation function threshold and a remaining duration threshold ; The evaluation function is specifically as follows:

[0021]

[0022] wherein: represents the evaluation function value; represents the average envelope of all eigenmode functions; represents the modal amplitude of the eigenmode function obtained by the latest decomposition; represents the lower envelope of the eigenmode function obtained by the latest decomposition; represents the upper envelope of the eigenmode function obtained by the latest decomposition; If the evaluation function value of the eigenmode function obtained by the latest decomposition is less than , the duration of the signal occupies of the total duration, and the remaining duration is less than , the decomposition is stopped.

[0023] In another aspect, the application also provides a partial discharge signal processing system for high-voltage equipment, comprising a signal acquisition module, a signal decomposition module, a reference intrinsic mode function construction module, an energy density boundary construction module, and a denoising module. The signal acquisition module is configured to acquire the partial discharge signal of the high-voltage equipment. The signal decomposition module is configured to perform signal decomposition on the acquired partial discharge signal by using an improved adaptive complete ensemble empirical mode decomposition algorithm to obtain a plurality of intrinsic mode functions. The reference intrinsic mode function construction module is configured to construct a mixed white noise data set, perform signal decomposition on the mixed white noise in the mixed white noise data set by using the improved adaptive complete ensemble empirical mode decomposition algorithm, and obtain a plurality of reference white noise intrinsic mode functions. The energy density boundary construction module is configured to statistically analyze the energy density based on all the reference white noise intrinsic mode functions, and set the upper and lower boundaries of the white noise energy density according to a preset signal level. The denoising module is configured to compare all the intrinsic mode functions with the upper and lower boundaries of the white noise energy density, remove the intrinsic mode functions within the upper and lower boundaries of the white noise energy density, and reconstruct the remaining intrinsic mode functions to obtain the denoised partial discharge signal.

[0024] Preferably, the partial discharge signal of the high-voltage equipment is acquired from the ground or the neutral system of the high-voltage equipment by using a high-frequency current transformer sensor.

[0025] Preferably, the specific steps of performing signal decomposition on the acquired partial discharge signal by using the improved adaptive complete ensemble empirical mode decomposition algorithm are as follows: Define an empirical mode decomposition operator of different modes; Define a local mean operator; Construct a plurality of white noise sets and set an initial noise amplitude coefficient and a plurality of noise amplitude coefficients of different modes; Calculate the first-order intrinsic mode function, and the specific steps are as follows: Construct a noise-added signal based on the partial discharge signal for each white noise set, and the specific formula is as follows:

[0026] Wherein: represents at the moment The noise-added signal is constructed; represents the partial discharge signal; represents the initial noise amplitude coefficient; represents the white noise set; Calculate the local mean of each noise-added signal by using the local mean operator, and the specific formula is as follows:

[0027] wherein: denotes the local mean; denotes the local mean operator; the local means of all the noisy signals are arithmetically averaged, specifically as shown in the following formula:

[0028] wherein: denotes the arithmetically averaged value of the local means of all the noisy signals; denotes the total number of white noise groups; the first-order intrinsic mode function is defined specifically as shown in the following formula:

[0029] the first-order residual is updated specifically as shown in the following formula:

[0030] it is determined whether the residual satisfies a stopping condition, and if not, the decomposition is continued: the order intrinsic mode function is calculated, and the specific steps are as follows: each group of white noise is decomposed by the order empirical mode decomposition operator to obtain the order intrinsic mode function corresponding to each group of white noise, specifically as shown in the following formula:

[0031] wherein: denotes the order intrinsic mode function corresponding to the order intrinsic mode function corresponding to the denotes the order empirical mode decomposition operator; the order noisy residual corresponding to each group of white noise is constructed from the order intrinsic mode function corresponding to each group of white noise and the order residual, specifically as shown in the following formula:

[0032] wherein: denotes the order noisy residual corresponding to the order intrinsic mode function corresponding to the denotes the order residual; denotes​ order noise amplitude coefficient; for The local mean of the noise-added residual error corresponding to each group of white noise is calculated as follows:

[0033] wherein: denotes The local mean of the noise-added residual error corresponding to the first group of white noise; for The local mean of the noise-added residual error corresponding to each group of white noise is taken as an arithmetic mean , as follows:

[0034] The first eigenmode function is defined as follows:

[0035] The first residual error is updated as follows:

[0036] It is determined whether the residual error satisfies the stopping condition, and if not, the decomposition is continued.

[0037] Preferably, the stopping condition further comprises an evaluation function threshold and a residual duration threshold ; The evaluation function is specifically as follows:

[0038]

[0039] wherein: denotes the evaluation function value; denotes the average envelope line of all eigenmode functions; denotes the modal amplitude of the eigenmode function obtained by the latest decomposition; denotes the lower envelope line of the eigenmode function obtained by the latest decomposition; denotes the upper envelope line of the eigenmode function obtained by the latest decomposition; If the evaluation function value of the eigenmode function obtained by the latest decomposition is less than the duration occupies of the total duration of the signal, and the residual duration is less than , the decomposition is stopped.

[0040] ​In still another aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to the present application when executing the program.

[0041] In still another aspect, the present application provides a computer readable storage medium having stored thereon a computer program, wherein the program is executable on a processor to implement the method according to the present application.

[0042] The present application has the following advantages: 1、The high-voltage equipment partial discharge signal processing method and system based on ICEEMDAN and statistical significance test provided by the present application collects PD signals from the grounding or neutral system of high-voltage equipment through an HFCT sensor, without the need to shut down and disassemble the equipment from the power grid, which meets the requirements of modern power grids for power continuity and reliability, and solves the limitation of traditional offline detection methods that require power-off operation; at the same time, the ICEEMDAN algorithm is used to decompose the noisy original signal, which effectively avoids the loss of time-frequency domain information and the problem of modal aliasing compared with traditional Fourier transform and wavelet transform methods, and does not need to rely on manual experience to select decomposition parameters, greatly improving the stability and accuracy of signal decomposition; in combination with the statistical significance test with a pre-set signal level to filter significant IMF, white noise interference is accurately removed, achieving the dual goals of noise suppression and PD signal feature preservation, and overcoming the defects of strong subjectivity in noise filtering and low signal fidelity in the prior art. Finally, the denoised PD signal is obtained by reconstructing the significant IMF, providing a high-quality signal source for high-voltage equipment insulation state evaluation, effectively supporting early diagnosis of equipment faults, and reducing power loss and maintenance costs caused by insulation deterioration. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 The method flowchart of the present application is shown in the figure. DETAILED DESCRIPTION

[0044] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0045] It should be understood that the step numbers used herein are only for the convenience of description, and are not limited to the execution sequence of the steps.

[0046] It is to be understood that the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting thereof. As used in the description of the application and the appended claims, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

[0047] The terms "including" and "comprising" as used herein are meant to be open-ended terms that specify the presence of stated features, integers, steps, operations, elements, and / or components but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0048] The term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items, and vice versa.

[0049] Referring to Figure 1 In some embodiments, a partial discharge signal processing method for high voltage equipment is proposed, comprising the following steps: Collecting a partial discharge (PD) signal of a high voltage equipment; Performing signal decomposition on the collected partial discharge signal by an improved adaptive complete ensemble empirical mode decomposition algorithm to obtain a plurality of intrinsic mode functions (IMFs); Constructing a mixed white noise data set, performing signal decomposition on the mixed white noise in the mixed white noise data set by the improved adaptive complete ensemble empirical mode decomposition algorithm to obtain a plurality of reference white noise intrinsic mode functions; Statistically analyzing the energy density of all reference white noise intrinsic mode functions, and setting the upper and lower boundaries of the white noise energy density according to a preset signal level; Comparing all intrinsic mode functions with the upper and lower boundaries of the white noise energy density, eliminating the intrinsic mode functions within the upper and lower boundaries of the white noise energy density, and then reconstructing the remaining intrinsic mode functions to obtain a denoised partial discharge signal.

[0050] In a specific embodiment, the upper and lower boundaries of the white noise energy density are set at a 99% confidence level.

[0051] In some embodiments, the partial discharge signal of the high voltage equipment is collected from the ground or the neutral system of the high voltage equipment by a high frequency current transformer (HFCT) sensor.

[0052] In some embodiments, the specific steps for decomposing the acquired partial discharge signal using the improved CEEMDAN (ICEEMDAN) algorithm are as follows: Define empirical mode decomposition operators for different modes; Define the local mean operator; Construct multiple sets of white noise and set the initial noise amplitude coefficients and the noise amplitude coefficients for multiple modes; The specific steps for calculating the first-order intrinsic modulus function are as follows: A noisy signal is constructed for each group of white noise based on the partial discharge signal, as shown in the following formula:

[0053] in: express Time based The constructed noisy signal; Indicates a partial discharge signal; Indicates the initial noise amplitude coefficient; Indicates the first Group white noise; The local mean of each noisy signal is calculated using the local mean operator, as shown in the following formula:

[0054] in: express The local mean; Represents the local mean operator; The arithmetic mean of the local means of all noisy signals is taken as follows:

[0055] in: This represents the arithmetic mean of the local means of all noisy signals; Indicates the total number of white noise groups; Define the first-order eigenmode function The specific formula is as follows:

[0056] Update the first-order residual The specific formula is as follows:

[0057] Determine if the residuals meet the stopping condition; if not, continue the decomposition process. Calculate the first The specific steps for determining the order intrinsic modulus function are as follows: Through the first The EMD operator decomposes each group of white noise to obtain the corresponding order The order intrinsic mode function, specifically as follows:

[0058] Wherein: The order intrinsic mode function corresponding to the order The group of white noise is represented as: The order EMD operator is represented as: The order residual is represented as: The order noise amplitude coefficient is represented as: The order noise amplitude coefficient is represented as: The order noise amplitude coefficient is represented as: The order noise amplitude coefficient is represented as:

[0059] Wherein: The order noise amplitude coefficient is represented as: The order noise amplitude coefficient is represented as: The group of white noise is represented as: The order residual is represented as: The order noise amplitude coefficient is represented as: The order noise amplitude coefficient is represented as: The order noise amplitude coefficient is represented as: The order noise amplitude coefficient is represented as: The order noise amplitude coefficient is represented as:

[0060] Wherein: The order noise amplitude coefficient is represented as: The order noise amplitude coefficient is represented as: The group of white noise is represented as: The order noise amplitude coefficient is represented as: The order noise amplitude coefficient is represented as: The order noise amplitude coefficient is represented as:

[0061] The order intrinsic mode function is defined as: The order intrinsic mode function is defined as: The order intrinsic mode function is defined as:

[0062] The order residual is updated as: The order residual is updated as: The order residual is updated as: The order residual is updated as: The order residual is updated as:

[0063] The order residual is updated as:

[0064] In some embodiments, the stop condition further comprises an evaluation function threshold and a residual duration threshold ; The evaluation function is specifically as follows:

[0065]

[0066] Wherein: represents the evaluation function value; represents the average envelope of all intrinsic mode functions; represents the modal amplitude of the intrinsic mode function obtained by the latest decomposition; represents the lower envelope of the intrinsic mode function obtained by the latest decomposition; represents the upper envelope of the intrinsic mode function obtained by the latest decomposition; If the evaluation function value of the intrinsic mode function obtained by the latest decomposition is less than The duration of the time length occupies The total duration of the signal, and the residual duration is less than , stop the decomposition.

[0067] In a specific embodiment, , .

[0068] In some embodiments, a partial discharge signal processing system for high voltage equipment is proposed, characterized in that it comprises a signal acquisition module, a signal decomposition module, a reference intrinsic mode function construction module, an energy density boundary construction module, and a denoising module; The signal acquisition module is used to acquire the partial discharge signal of the high voltage equipment; The signal decomposition module is used to perform signal decomposition on the acquired partial discharge signal through an improved adaptive complete ensemble empirical mode decomposition algorithm to obtain a plurality of intrinsic mode functions; The reference intrinsic mode function construction module is used to construct a mixed white noise data set, and perform signal decomposition on the mixed white noise in the mixed white noise data set through an improved adaptive complete ensemble empirical mode decomposition algorithm to obtain a plurality of reference white noise intrinsic mode functions; The energy density boundary construction module is used to statistically analyze the energy density based on all reference white noise intrinsic mode functions, and set the upper and lower boundaries of white noise energy density according to the preset signal level; The denoising module is configured to compare all the eigenmode functions with upper and lower boundaries of white noise energy density, remove the eigenmode functions within the upper and lower boundaries of white noise energy density, and reconstruct signals of the remaining eigenmode functions to obtain the denoised partial discharge signal.

[0069] The system is configured to implement the functions in the method, and details are not described herein.

[0070] In some embodiments, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the method according to any of the embodiments of the application when executing the program.

[0071] In some embodiments, a computer readable storage medium is provided, which stores a computer program executable by a processor, and the program implements the method according to any of the embodiments of the application when executed by the processor.

[0072] In the embodiments of the present application, “at least one” means one or more, and “multiple” means two or more. “And / or” describes the association relationship of the associated objects, which means that there can be three kinds of relationships, for example, A and / or B, which can represent the cases of A alone, A and B together, and B alone. Wherein A and B can be singular or plural. The character “ / ” generally represents an “or” relationship between the front and rear associated objects. “At least one of the following” and the like means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, wherein a, b, and c can be single or multiple.

[0073] Those of ordinary skill in the art can realize that the units and algorithm steps described in the embodiments disclosed herein can be realized in electronic hardware, computer software and a combination of electronic hardware and computer software. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0074] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described herein.

[0075] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0076] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for processing partial discharge signals in high-voltage equipment, characterized in that, Includes the following steps: Acquire partial discharge signals from high-voltage equipment; The acquired partial discharge signal is decomposed using an improved adaptive noise complete set empirical mode decomposition algorithm to obtain multiple intrinsic mode functions; A mixed white noise dataset is constructed, and the mixed white noise in the dataset is decomposed into signals using an improved adaptive noise complete set empirical mode decomposition algorithm to obtain multiple reference white noise eigenmode functions. The energy density is statistically calculated based on the intrinsic mode functions of all reference white noise, and the upper and lower boundaries of the white noise energy density are set according to the preset information level. All intrinsic mode functions are compared with the upper and lower boundaries of the white noise energy density. After removing the intrinsic mode functions that are within the upper and lower boundaries of the white noise energy density, the remaining intrinsic mode functions are reconstructed to obtain the denoised partial discharge signal.

2. The method for processing partial discharge signals in high-voltage equipment according to claim 1, characterized in that, The partial discharge signal of the high-voltage equipment is collected from the grounding point or neutral system of the high-voltage equipment through a high-frequency current transformer sensor.

3. The method for processing partial discharge signals in high-voltage equipment according to claim 1, characterized in that, The specific steps for decomposing the acquired partial discharge signal using the improved adaptive noise complete set empirical mode decomposition algorithm are as follows: Define empirical mode decomposition operators for different modes; Define the local mean operator; Construct multiple sets of white noise and set the initial noise amplitude coefficients and the noise amplitude coefficients for multiple modes; The specific steps for calculating the first-order intrinsic modulus function are as follows: A noisy signal is constructed for each group of white noise based on the partial discharge signal, as shown in the following formula: in: express Time based The constructed noisy signal; Indicates a partial discharge signal; Indicates the initial noise amplitude coefficient; Indicates the first Group white noise; The local mean of each noisy signal is calculated using the local mean operator, as shown in the following formula: in: express The local mean; Represents the local mean operator; The arithmetic mean of the local means of all noisy signals is taken as follows: in: This represents the arithmetic mean of the local means of all noisy signals; Indicates the total number of white noise groups; Define the first-order eigenmode function The specific formula is as follows: Update the first-order residual The specific formula is as follows: Determine if the residuals meet the stopping condition; if not, continue the decomposition process. Calculate the first The specific steps for determining the order intrinsic modulus function are as follows: Through the first The first-order empirical mode decomposition operator decomposes each group of white noise, obtaining the first-order corresponding white noise for each group. The order intrinsic modulus function is shown in the following equation: in: Indicates the first The first group of white noise corresponding to the eigenmode functions of order; Indicates the first Order-order empirical mode decomposition operator; pass The first-order residual and the first-order residual corresponding to each group of white noise Construction of eigenmodular functions of order The noise-added residual for each group of white noise is shown in the following formula: in: express Rank The noise-added residuals corresponding to the white noise group; express Order residual; express Noise amplitude coefficient; right The local mean of the noise-added residuals for each group of white noise is calculated as follows: in: express Rank The local mean of the noise-added residuals corresponding to the white noise group; Yes, yes The local mean of the noise-added residuals corresponding to each group of white noise is taken as the arithmetic mean. The specific formula is as follows: Definition of the first eigenmode functions The specific formula is as follows: Update # The order residual is shown in the following formula: Determine if the residuals meet the stopping condition; if not, continue the decomposition process.

4. The method for processing partial discharge signals in high-voltage equipment according to claim 3, characterized in that, The stopping condition also includes an evaluation function threshold. and the remaining time threshold ; The evaluation function is specifically shown in the following formula: in: Indicates the evaluation function value; This represents the average envelope of all intrinsic moduli; This represents the modal amplitude of the eigenmode function obtained from the latest decomposition; This represents the lower envelope of the eigenmode functions obtained from the latest decomposition; The upper envelope of the eigenmode functions obtained from the latest decomposition is represented; If the evaluation function value of the eigenmode function obtained from the latest decomposition is less than The duration of the signal lasts for a period of time. And the remaining time is less than When the time comes, stop the decomposition.

5. A partial discharge signal processing system for high-voltage equipment, characterized in that, It includes a signal acquisition module, a signal decomposition module, a reference intrinsic mode function construction module, an energy density boundary construction module, and a noise reduction module; The signal acquisition module is used to acquire partial discharge signals from high-voltage equipment; The signal decomposition module is used to decompose the acquired partial discharge signal using an improved adaptive noise complete set empirical mode decomposition algorithm to obtain multiple intrinsic mode functions. The reference intrinsic mode function construction module is used to construct a mixed white noise dataset. The mixed white noise in the mixed white noise dataset is decomposed into signals by an improved adaptive noise complete set empirical mode decomposition algorithm to obtain multiple reference white noise intrinsic mode functions. The energy density boundary construction module is used to statistically analyze the energy density based on the intrinsic mode functions of all reference white noise, and to set the upper and lower boundaries of the white noise energy density according to a preset information level. The denoising module is used to compare all intrinsic mode functions with the upper and lower boundaries of the white noise energy density, remove the intrinsic mode functions that are within the upper and lower boundaries of the white noise energy density, and then reconstruct the remaining intrinsic mode functions to obtain the denoised partial discharge signal.

6. The high-voltage equipment partial discharge signal processing system according to claim 5, characterized in that, The partial discharge signal of the high-voltage equipment is collected from the grounding point or neutral system of the high-voltage equipment through a high-frequency current transformer sensor.

7. A partial discharge signal processing system for high-voltage equipment according to claim 5, characterized in that, The specific steps for decomposing the acquired partial discharge signal using the improved adaptive noise complete set empirical mode decomposition algorithm are as follows: Define empirical mode decomposition operators for different modes; Define the local mean operator; Construct multiple sets of white noise and set the initial noise amplitude coefficients and the noise amplitude coefficients for multiple modes; The specific steps for calculating the first-order intrinsic modulus function are as follows: A noisy signal is constructed for each group of white noise based on the partial discharge signal, as shown in the following formula: in: express Time based The constructed noisy signal; Indicates a partial discharge signal; Indicates the initial noise amplitude coefficient; Indicates the first Group white noise; The local mean of each noisy signal is calculated using the local mean operator, as shown in the following formula: in: express The local mean; Represents the local mean operator; The arithmetic mean of the local means of all noisy signals is taken as follows: in: This represents the arithmetic mean of the local means of all noisy signals; Indicates the total number of white noise groups; Define the first-order eigenmode function The specific formula is as follows: Update the first-order residual The specific formula is as follows: Determine if the residuals meet the stopping condition; if not, continue the decomposition process. Calculate the first The specific steps for determining the order intrinsic modulus function are as follows: Through the first The first-order empirical mode decomposition operator decomposes each group of white noise, obtaining the first-order corresponding white noise for each group. The order intrinsic modulus function is shown in the following equation: in: Indicates the first The first group of white noise corresponding to the eigenmode functions of order; Indicates the first Order-order empirical mode decomposition operator; pass The first-order residual and the first-order residual corresponding to each group of white noise Construction of eigenmodular functions of order The noise-added residual for each group of white noise is shown in the following formula: in: express Rank The noise-added residuals corresponding to the white noise group; express Order residual; express Noise amplitude coefficient; right The local mean of the noise-added residuals for each group of white noise is calculated as follows: in: express Rank The local mean of the noise-added residuals corresponding to the white noise group; Yes, yes The local mean of the noise-added residuals corresponding to each group of white noise is taken as the arithmetic mean. The specific formula is as follows: Definition of the first eigenmode functions The specific formula is as follows: Update # The order residual is shown in the following formula: Determine if the residuals meet the stopping condition; if not, continue the decomposition process.

8. A partial discharge signal processing system for high-voltage equipment according to claim 7, characterized in that, The stopping condition also includes an evaluation function threshold. and the remaining time threshold ; The evaluation function is specifically shown in the following formula: in: Indicates the evaluation function value; This represents the average envelope of all intrinsic moduli; This represents the modal amplitude of the eigenmode function obtained from the latest decomposition; This represents the lower envelope of the eigenmode functions obtained from the latest decomposition; The upper envelope of the eigenmode functions obtained from the latest decomposition is represented; If the evaluation function value of the eigenmode function obtained from the latest decomposition is less than The duration of the signal lasts for a period of time. And the remaining time is less than When the time comes, stop the decomposition.

9. An electronic 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 method as described in any one of claims 1 to 4.

10. 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 method as described in any one of claims 1 to 4.

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