Partial discharge optical signal noise reduction method, system and device based on weighted decomposition and entropy calculation and medium
Through a multi-stage noise reduction processing method combining weighted decomposition and entropy value calculation, the problem of difficulty in removing noise interference of local discharge optical signal in complex environments is solved, which significantly improves detection accuracy and signal-to-noise ratio, and enhances the robustness and stability of the signal.
Patent Information
- Application Number
- CN202411639528.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-18
- Publication Date
- 2025-02-28
AI Technical Summary
The prior art is difficult to effectively remove multi-source noise in local discharge optical signals in complex environments, resulting in insufficient detection accuracy and reliability.
The noise reduction method based on weighted decomposition and entropy value calculation is adopted, and the optical signal is captured through a photodetector, and a multi-stage noise reduction process combined with preprocessing, local weighted decomposition and entropy value calculation is performed to remove noise and extract the local discharge signal.
It significantly improves the detection accuracy and signal-to-noise ratio of local discharge optical signals, enhances the robustness and stability of the signal, improves the fault monitoring capabilities, and broadens the application scenarios.
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Figure CN119537809A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to a partial discharge optical signal denoising method, system, device and medium based on weighted decomposition and entropy value calculation, belonging to the technical field of partial discharge optical signal detection. Background Art
[0002] During the operation of power equipment, partial discharge (PD) is a common precursor to failure, especially in the insulation system of high-voltage equipment. The occurrence of PD often indicates the degradation or local damage of the insulation performance of the equipment, which may lead to insulation failure and even cause serious equipment failure and power outage accidents. Therefore, early detection and monitoring of PD can effectively prevent the occurrence of major failures and improve the safety and reliability of power equipment operation. Traditional PD detection methods mainly rely on electrical signals, such as ultra-high frequency (UHF) detection, current pulse method and acoustic emission technology. These methods determine the discharge location and intensity by capturing electromagnetic radiation, sound waves or current pulse signals during the PD process. However, these electrical signal detection methods have a common problem, that is, in the complex operating environment of power equipment, electromagnetic interference is relatively serious. The strong electromagnetic noise generated by the power equipment itself or other electrical equipment around it will seriously affect the recognition of PD signals and reduce the accuracy and reliability of detection.
[0003] With the rapid development of optical technology, partial discharge detection technology based on optical signals has gradually become a research hotspot. This method uses optical radiation signals such as ultraviolet light, visible light or infrared light generated during partial discharge to monitor partial discharge. Compared with electrical signal detection methods, optical signal detection has obvious advantages, especially in terms of anti-electromagnetic interference. Optical signals are not affected by electromagnetic noise and can achieve more accurate partial discharge signal detection in the complex electromagnetic environment of power equipment. However, optical signal detection also faces a series of technical challenges. First, the optical signal generated by partial discharge is often very weak, especially in outdoor or industrial environments, where the signal intensity is easily overwhelmed by external factors such as strong light sources, environmental noise and background light. Secondly, the interference of external noise makes it very difficult to accurately extract the optical signal of partial discharge in a complex environment. Although the existing signal processing and noise suppression methods can achieve certain results under laboratory conditions, in practical applications, especially in outdoor or high-noise environments, it is often difficult to effectively handle the interference of multi-source noise. Traditional filtering methods and denoising algorithms show obvious limitations when facing complex noise conditions and cannot effectively extract the optical signal of partial discharge. Therefore, developing a new noise reduction method that can suppress multi-source noise in complex environments and effectively extract partial discharge optical signals has important research value and practical significance. This not only helps to improve the detection accuracy of partial discharge optical signals, but also enhances the application capability of optical detection technology in actual power equipment fault diagnosis, thereby further improving the reliability and safety of power system operation. Summary of the invention
[0004] The purpose of the present invention is to provide a method, system, device and medium for reducing noise of partial discharge optical signals based on weighted decomposition and entropy value calculation, so as to solve the problem that noise interference is difficult to effectively remove in the process of partial discharge optical signal detection in the prior art, thereby improving the accuracy and reliability of detection.
[0005] In order to achieve the above object, the present invention is implemented through the following technical solutions: A method for reducing noise of partial discharge optical signals based on weighted decomposition and entropy value calculation, comprising: The optical signal during the partial discharge process is accurately captured by the photodetector, and the environmental noise and interference are recorded synchronously, and the collected optical signal is converted into a time series data form; Preprocess the collected signals, including normalization and preliminary noise suppression. By subtracting the mean of the signal and dividing it by the standard deviation, DC offset and high-frequency sharp noise can be effectively removed. Use local weighted decomposition to decompose the preprocessed signal into different frequency bands, and assign weighted coefficients to signal points for denoising according to the distribution of noise in the signal; The entropy value calculation method is used to perform secondary noise reduction on the signal to obtain the noise-reduced partial discharge optical signal.
[0006] Preferably, the preprocessing formula is as follows: , , , in, represents the preprocessed signal, represents the collected optical signal, represents the mean value of the signal, Indicates the standard deviation of the signal; is the number of sampling points.
[0007] Preferably, a weighted coefficient is assigned to the signal point for denoising according to the distribution of noise in the signal. The specific formula is as follows: in, Represents the signal after weighting coefficient denoising.
[0008] Preferably, the weighted coefficient calculation formula is as follows: , in, Represents the weighting coefficient.
[0009] Preferably, the signal is subjected to secondary noise reduction using an entropy value calculation method to obtain a noise-reduced partial discharge optical signal, and the specific method is as follows: The entropy of the signal is calculated and the local threshold is obtained by the sliding window method; the signal is processed by the local threshold to minimize the residual noise component; and the local discharge optical signal after multi-layer noise reduction processing is output.
[0010] Preferably, the signal is subjected to secondary noise reduction using an entropy value calculation method to obtain a noise-reduced partial discharge optical signal. The specific formula is as follows: , in, represents the partial discharge optical signal after noise reduction, Represents the signal entropy value.
[0011] Preferably, the signal entropy value calculation formula is as follows: , , in, Indicates signal The number of occurrences, window The total number of signal points in Indicates the window size.
[0012] A partial discharge optical signal noise reduction system based on weighted decomposition and entropy value calculation, comprising: A data acquisition module is configured to collect optical signals in the partial discharge process through a high-sensitivity photodetector, and simultaneously record environmental noise and interference, and convert them into a time series data form; A preprocessing module is configured to preprocess the collected optical signal, including normalization processing and preliminary noise suppression, and remove the influence of DC offset and high-frequency sharp noise by subtracting the mean of the signal and dividing it by the standard deviation; A weighted denoising module is configured to decompose the preprocessed signal into different frequency bands using local weighted decomposition, and assign weighted coefficients to signal points for denoising according to the distribution of noise in the signal; The entropy denoising module is configured to perform secondary denoising on the signal using an entropy calculation method to obtain a denoised partial discharge optical signal.
[0013] A local discharge optical signal denoising device based on weighted decomposition and entropy value calculation comprises a processor and a memory storing program instructions, wherein the processor is configured to execute the local discharge optical signal denoising method based on weighted decomposition and entropy value calculation when running the program instructions.
[0014] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the local discharge optical signal denoising method based on weighted decomposition and entropy value calculation.
[0015] The advantages of the present invention are: Significantly improve detection accuracy and signal-to-noise ratio: The present invention realizes multi-level noise reduction processing of partial discharge optical signals by combining local weighted decomposition technology and entropy value calculation method. This method can suppress noise of different types and frequencies in a targeted manner, thereby significantly improving the detection accuracy and signal-to-noise ratio of the signal.
[0016] Enhance signal robustness and stability: In complex industrial environments, partial discharge optical signals are often interfered by various noises. The noise reduction method proposed in the present invention can effectively deal with non-stationary noise by setting weighting coefficients in real time to adapt to changes in signal characteristics, thereby enhancing the robustness and stability of the signal.
[0017] Improve fault monitoring capabilities: Partial discharge is a precursor to insulation failure in power equipment. The present invention can detect potential problems in advance through high-precision partial discharge signal detection, thereby preventing equipment failure or accidents. This is of great significance for fault monitoring of high-voltage equipment such as transformers, switchgear, cables, etc., and helps to ensure the safety and reliability of power equipment operation.
[0018] Broaden application scenarios: The noise reduction method of the present invention has wide applicability, and is not only applicable to indoor environments, but can also work effectively outdoors or in high-noise environments, which enables the method to be applied to more types of partial discharge detection scenarios, broadening its application scope.
[0019] Intelligent and adaptable: Traditional noise reduction methods often rely on fixed parameter settings and are difficult to adapt to complex and changing environments. The present invention realizes intelligent noise reduction processing by combining local weighted decomposition and entropy value calculation. The method can dynamically adjust the noise reduction strategy according to the characteristics of the signal, thereby improving the adaptability of the noise reduction effect.
[0020] In summary, the partial discharge optical signal denoising method based on weighted decomposition and entropy value calculation proposed in the present invention has shown significant beneficial effects in improving detection accuracy, enhancing signal robustness, improving fault monitoring capabilities, broadening application scenarios, and realizing intelligent and adaptive denoising. BRIEF DESCRIPTION OF THE DRAWINGS
[0021] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.
[0022] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION
[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0024] Example 1 like Figure 1 As shown, a method for reducing noise of partial discharge optical signals based on weighted decomposition and entropy value calculation includes: S1: The optical signals during the partial discharge process are accurately captured by photodetectors, and the environmental noise and interference are simultaneously recorded, and the collected optical signals are converted into time series data.
[0025] The optical signals during the partial discharge process are collected by a high-sensitivity photodetector.
[0026] S2: Preprocess the collected signal, including normalization and preliminary noise suppression. By subtracting the mean of the signal and dividing it by the standard deviation, DC offset and high-frequency sharp noise can be effectively removed. As a refinement of the above embodiment, the collected optical signal is preprocessed, usually to improve the signal quality and prepare for the subsequent analysis steps. The goal of the preprocessing is to remove the DC component (i.e., the average value of the signal) and suppress high-frequency noise (such as sharp noise), thereby enhancing the usability of the signal. Specifically, the preprocessing process includes signal normalization and noise suppression, and the preprocessing formula is as follows: , , , in, Represents the preprocessed signal; represents the collected optical signal; is the number of sampling points; Represents the mean value of the signal, also known as the DC component, which is the average value of the signal over a period of time. The purpose of removing the mean is to eliminate the DC offset of the signal and center the signal to zero. It represents the standard deviation of the signal and the degree of discreteness of the signal strength. The standard deviation is used to normalize the amplitude of the signal and eliminate the influence of the scales between different signals. In this way, different signals can be converted to the same amplitude range for easy comparison and analysis.
[0027] Through the above-mentioned normalization preprocessing, the DC offset and the influence of different scales of the optical signal are effectively removed, ensuring that the subsequent processing process is more accurate.
[0028] S3: Use local weighted decomposition to decompose the preprocessed signal into different frequency bands, and assign weighted coefficients to signal points for denoising according to the distribution of noise in the signal.
[0029] As a refinement of the above embodiment, when performing local weighted decomposition, the weighting coefficient of each signal point is calculated according to the local variance value in the above step 1. It is inversely proportional to the local variance, that is, the larger the local variance, the smaller the signal weight; the weight coefficient calculation formula is as follows: , in, Represents the weighting coefficient.
[0030] According to the distribution of noise in the signal, the signal points are assigned weighted coefficients for denoising. The specific formula is as follows: in, Represents the signal after weighting coefficient denoising.
[0031] S4: Perform secondary noise reduction on the signal using an entropy calculation method to obtain a noise-reduced partial discharge optical signal.
[0032] As a refinement of the above embodiment, the entropy of the signal is calculated, and a local threshold is obtained by a sliding window method; the signal is processed by the local threshold to minimize the residual noise component; and a local discharge optical signal after multi-layer noise reduction processing is output.
[0033] Optional, the specific processing method is as follows: First of all, entropy is an indicator to measure the complexity of a signal, and is usually used to describe the amount of information. In signal denoising, the part with a lower entropy value usually represents the noise in the signal, while the part with a higher entropy value is the part of the signal with a higher amount of information. The entropy-based denoising method calculates the local entropy of the signal to determine which parts are noise, thereby removing the noise part. Reading the signal is the first step in denoising, and it is necessary to read the signal to be processed.
[0034] The local entropy value reflects the complexity of the signal in a certain area. In the process of denoising, the sliding window method can be used to calculate the local entropy of the signal. The size of the window is, and the entropy value is calculated in each window. The definition of entropy is as follows: , in, It's a signal In the window The probability distribution within is the probability distribution of the signal in the window; is the window size.
[0035] In general, the probability distribution It can be obtained through frequency estimation or maximum likelihood estimation.
[0036] , In the formula, Indicates signal Number of occurrences; window Total number of signal points within; Indicates the window size.
[0037] Entropy The larger the entropy value is, the more complex the signal is in that area, and it may contain more useful information. Conversely, the lower the entropy value is, the more likely the signal in that area is noise. In these areas, the signal features in the local window are used to calculate the weighted entropy adjustment: , in, is the weighting coefficient, that is, the weight of the local area.
[0038] Signal Processing is performed to optimize the global denoising effect of the signal: , in, represents the partial discharge optical signal after noise reduction; Represents the signal entropy value.
[0039] It should be noted that in partial discharge detection, optical signals are often interfered by various environmental noises and instrument noises, which can seriously affect the quality of the signal and lead to inaccurate detection results. The present invention retains useful partial discharge information and improves the signal-to-noise ratio of the signal by effectively performing noise reduction processing on the signal. The local weighted decomposition technology proposed in the present invention calculates the local variance for the signal point, decomposes the signal into different frequency levels, and effectively distinguishes the noise and useful information in the signal. The entropy value calculation method analyzes the soft threshold and hard threshold in the entropy of the signal to obtain the local threshold, identify and remove the noise component. Combining these two technologies can remove noise interference to the greatest extent while maintaining the useful components of the signal.
[0040] The noise reduction method of the present invention has wide applicability and is applicable to a variety of partial discharge signal detection scenarios. It can not only effectively improve the clarity and accuracy of the signal, but also improve the robustness and stability of the detection system. In particular, in complex industrial environments, the present invention can significantly improve the detection performance of partial discharge optical signals and ensure the safety and reliability of equipment operation.
[0041] Therefore, the present invention can effectively improve the accuracy and reliability of local discharge signal detection by combining local weighted decomposition with entropy value calculation, overcome the limitations of the prior art, and provide a more intelligent and adaptive noise reduction solution.
[0042] Example 2 A partial discharge optical signal noise reduction system based on weighted decomposition and entropy value calculation, comprising: The data acquisition module is configured to collect optical signals in the partial discharge process through a high-sensitivity photodetector, and simultaneously record environmental noise and interference, and convert them into a time series data form.
[0043] The preprocessing module is configured to preprocess the collected optical signal, including normalization processing and preliminary noise suppression, and removes the influence of DC offset and high-frequency sharp noise by subtracting the mean value of the signal and dividing it by the standard deviation.
[0044] The weighted denoising module is configured to decompose the preprocessed signal into different frequency bands using local weighted decomposition, and assign weighted coefficients to signal points for denoising according to the distribution of noise in the signal.
[0045] The entropy denoising module is configured to perform secondary denoising on the signal using an entropy calculation method to obtain a denoised partial discharge optical signal.
[0046] As a refinement of the above embodiment, the preprocessing module preprocesses the collected optical signal, including normalization processing and preliminary noise suppression, and removes the influence of DC offset and high-frequency sharp noise by subtracting the mean of the signal and dividing it by the standard deviation. Specifically, the preprocessing formula for the signal is as follows: , , , in, Represents the preprocessed signal; represents the collected optical signal; represents the mean value of the signal; Indicates the standard deviation of the signal; is the number of sampling points.
[0047] As a refinement of the above embodiment, the weighted denoising module uses local weighted decomposition to decompose the preprocessed signal into different frequency bands, and assigns weighted coefficients to signal points for denoising according to the distribution of noise in the signal.
[0048] According to the distribution of noise in the signal, the signal points are assigned weighted coefficients for denoising. The specific formula is as follows: in, Represents the signal after weighting coefficient denoising.
[0049] Preferably, the weighted coefficient calculation formula is as follows: , in, Represents the weighting coefficient.
[0050] As a refinement of the above embodiment, the entropy denoising module performs secondary denoising on the signal using an entropy value calculation method to obtain a denoised partial discharge optical signal. The specific method is as follows: The entropy of the signal is calculated and the local threshold is obtained by the sliding window method; the signal is processed by the local threshold to minimize the residual noise component; and the local discharge optical signal after multi-layer noise reduction processing is output.
[0051] Optionally, the signal is subjected to secondary noise reduction using an entropy value calculation method to obtain a noise-reduced partial discharge optical signal, which also includes the following specific formula: , in, represents the partial discharge optical signal after noise reduction; Represents the signal entropy value.
[0052] Preferably, the signal entropy value calculation formula is as follows: , , in, Indicates signal Number of occurrences; window Total number of signal points within; Indicates the window size.
[0053] The present invention realizes high-precision noise reduction processing of partial discharge optical signals by combining the advantages of weighted decomposition and entropy processing. Compared with the traditional single noise reduction method, this scheme has the following significant advantages: first, through multi-level noise reduction processing, noise of different types and frequencies can be suppressed in a targeted manner. Secondly, the use of weighted decomposition enables the method to set weighting coefficients in real time, adapt to changes in signal characteristics, and effectively deal with non-stationary noise. Finally, after two noise reductions, the output signal retains more effective information, and the noise is significantly reduced, which significantly improves the overall detection accuracy of the signal. This technology is particularly suitable for the detection and monitoring of partial discharge. In electrical equipment, partial discharge is often a precursor to electrical insulation failure. Through high-precision partial discharge signal detection, potential problems can be discovered in advance, thereby preventing equipment failure or accidents. With the improvement of detection accuracy, partial discharge detection technology can be widely used in fault monitoring of high-voltage equipment such as transformers, switchgear, cables, etc., and has broad application prospects. The noise reduction method described in the present invention can effectively reduce the noise of partial discharge signals in each embodiment.
[0054] The embodiment of the present disclosure provides a local discharge optical signal denoising device based on weighted decomposition and entropy value calculation, including a processor and a memory. Optionally, the device may also include a communication interface and a bus. The processor, the communication interface, and the memory may communicate with each other through the bus. The communication interface may be used for information transmission. The processor may call the logic instructions in the memory to execute the local discharge optical signal denoising method based on weighted decomposition and entropy value calculation of the above embodiment.
[0055] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product.
[0056] The memory, as a computer-readable storage medium, can be used to store software programs and computer executable programs, such as program instructions / modules corresponding to the method in the embodiment of the present disclosure. The processor 304 executes the function application and data processing by running the program instructions / modules stored in the memory, that is, the partial discharge optical signal noise reduction method based on weighted decomposition and entropy value calculation in the above embodiment is implemented.
[0057] The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store data created according to the use of the terminal device, etc. In addition, the memory may include a high-speed random access memory and may also include a non-volatile memory.
[0058] An embodiment of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured to execute the above-mentioned partial discharge optical signal denoising method based on weighted decomposition and entropy value calculation.
[0059] The computer-readable storage medium mentioned above may be a transient computer-readable storage medium or a non-transitory computer-readable storage medium.
[0060] The technical solution of the embodiment of the present disclosure can be embodied in the form of a software product, which is stored in a storage medium and includes one or more instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiment of the present disclosure. The aforementioned storage medium may be a non-transient storage medium, including: a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a disk or an optical disk, and other media that can store program codes, or a transient storage medium.
[0061] The above description and the accompanying drawings fully illustrate the embodiments of the present disclosure so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent only possible changes. Unless explicitly required, separate components and functions are optional, and the order of operations may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the words used in this application are only used to describe the embodiments and are not used to limit the claims. As used in the description of the embodiments and the claims, unless the context clearly indicates, the singular forms of "a", "an" and "the" are intended to include plural forms as well. Similarly, the term "and / or" as used in this application refers to any and all possible combinations of one or more associated listings. In addition, when used in this application, the term "comprise" and its variants "comprises" and / or comprising refer to the presence of stated features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof. In the absence of further restrictions, the elements defined by the sentence "including one..." do not exclude the presence of other identical elements in the process, method or device including the elements. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the embodiments may refer to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts can refer to the description of the method part.
[0062] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. The technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of the present disclosure. The technicians may clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.
[0063] In the embodiments disclosed herein, the disclosed methods and products (including but not limited to devices, equipment, etc.) can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units can be only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to implement this embodiment. In addition, each functional unit in the embodiment of the present disclosure may be integrated in a processing unit, or each unit may exist physically separately, or two or more units may be integrated in one unit.
[0064] The flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.
Claims
1. A method for reducing noise of partial discharge optical signals based on weighted decomposition and entropy value calculation, characterized in that: include: The optical signal during the partial discharge process is accurately captured by the photodetector, and the environmental noise and interference are recorded synchronously, and the collected optical signal is converted into a time series data form; Preprocess the collected signals, including normalization and preliminary noise suppression, by subtracting the mean of the signal and dividing by the standard deviation to remove DC offset and high-frequency sharp noise; Use local weighted decomposition to decompose the preprocessed signal into different frequency bands, and assign weighted coefficients to signal points for denoising according to the distribution of noise in the signal; The entropy value calculation method is used to perform secondary noise reduction on the signal to obtain the noise-reduced partial discharge optical signal.
2. The method for reducing noise of partial discharge optical signals based on weighted decomposition and entropy value calculation according to claim 1, characterized in that: The preprocessing formula is as follows: ， ， ， in, represents the preprocessed signal, represents the collected optical signal, represents the mean value of the signal, Indicates the standard deviation of the signal; is the number of sampling points.
3. The method for reducing noise of partial discharge optical signals based on weighted decomposition and entropy value calculation according to claim 2, characterized in that: According to the distribution of noise in the signal, weighted coefficients are assigned to the signal points for denoising. The specific formula is as follows: in, Represents the signal after weighting coefficient denoising.
4. The method for reducing noise of partial discharge optical signals based on weighted decomposition and entropy value calculation according to claim 3, characterized in that: The weighted coefficient calculation formula is as follows: ， in, Represents the weighting coefficient.
5. The method for reducing noise of partial discharge optical signals based on weighted decomposition and entropy value calculation according to claim 1, characterized in that: The entropy value calculation method is used to perform secondary noise reduction on the signal to obtain the noise-reduced partial discharge optical signal. The specific method is as follows: The entropy of the signal is calculated and the local threshold is obtained by the sliding window method; the signal is processed by the local threshold to minimize the residual noise component; and the local discharge optical signal after multi-layer noise reduction processing is output.
6. The method for reducing noise of partial discharge optical signals based on weighted decomposition and entropy value calculation according to claim 4, characterized in that: The entropy value calculation method is used to perform secondary noise reduction on the signal to obtain the noise-reduced partial discharge optical signal. The specific formula is as follows: ， in, represents the partial discharge optical signal after noise reduction, Represents the signal entropy value.
7. The method for reducing noise of partial discharge optical signals based on weighted decomposition and entropy value calculation according to claim 6, characterized in that: The signal entropy value calculation formula is as follows: ， ， in, Indicates signal The number of occurrences, window The total number of signal points in Indicates the window size.
8. A partial discharge optical signal noise reduction system based on weighted decomposition and entropy value calculation using the method described in any one of claims 1 to 7, characterized in that: include: A data acquisition module is configured to collect optical signals in the partial discharge process through a high-sensitivity photodetector, and simultaneously record environmental noise and interference, and convert them into a time series data form; A preprocessing module is configured to preprocess the collected optical signal, including normalization processing and preliminary noise suppression, and remove the influence of DC offset and high-frequency sharp noise by subtracting the mean of the signal and dividing it by the standard deviation; A weighted denoising module is configured to decompose the preprocessed signal into different frequency bands using local weighted decomposition, and assign weighted coefficients to signal points for denoising according to the distribution of noise in the signal; The entropy denoising module is configured to perform secondary denoising on the signal using an entropy calculation method to obtain a denoised partial discharge optical signal.
9. A partial discharge optical signal noise reduction device based on weighted decomposition and entropy value calculation, comprising a processor and a memory storing program instructions, characterized in that: The processor is configured to execute the partial discharge optical signal denoising method based on weighted decomposition and entropy value calculation as described in any one of claims 1 to 7 when running the program instructions.
10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the program is executed by a processor, the method for reducing noise of partial discharge optical signals based on weighted decomposition and entropy value calculation as described in any one of claims 1 to 7 above is implemented.