Power equipment element aging monitoring system and method based on capacitive coupling technology

Abstract

The invention relates to the technical field of power equipment monitoring, in particular to a power equipment element aging monitoring system and method based on the capacitive coupling technology, and the system comprises an annular electrode sensor, a pre-amplifier, a data collection card and a system integration platform. The annular electrode sensor senses the change of an electric field in the power equipment in a non-contact manner and outputs a weak electric signal; the preamplifier receives the weak electric signal and converts the weak electric signal into an electric signal for output; the data acquisition card converts an electric signal into a digital signal and outputs the digital signal to the system integration platform; and processing the digital signal through the system integration platform, establishing an equipment aging model, and outputting an evaluation result through the equipment aging model to reflect the aging degree of the power equipment. According to the invention, a capacitive coupling technology is adopted, and dynamic monitoring and early warning are carried out on the health state of the power equipment without shutdown.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment monitoring, and in particular to a power equipment component aging monitoring system and method based on capacitive coupling technology. Background Art

[0002] With the continuous development of the power system, the safe and stable operation of power equipment has become one of the core issues of concern to the power industry. During the long-term operation of power equipment, its internal components will gradually age, which not only affects the normal operation of the equipment, but may also cause serious safety accidents. Therefore, effective monitoring of the aging status of power equipment components is of great significance to ensure the safety and reliability of the power system. However, traditional methods often rely on regular manual inspections and maintenance, which is not only inefficient, but also difficult to detect potential problems in a timely manner, and cannot meet the needs of modern power systems for real-time monitoring of the health status of equipment.

[0003] Traditional methods for monitoring the aging of power equipment components mainly include regular maintenance inspections, temperature monitoring, and vibration analysis. Although these methods can reflect the status of the equipment to a certain extent, they have obvious limitations. For example, regular maintenance inspections are time-consuming and labor-intensive, and it is difficult to detect subtle changes in the early stages; temperature monitoring is not accurate enough due to the influence of external environmental factors; although vibration analysis can detect some mechanical failures, it is powerless to detect changes in electrical performance. In addition, these methods usually require the equipment to be shut down, which is unacceptable for continuously operating power systems.

[0004] In recent years, with the development of sensing technology and data processing technology, many new online monitoring technologies have emerged, such as infrared thermal imaging monitoring, acoustic emission monitoring, etc. These technologies have improved the accuracy and real-time performance of monitoring to a certain extent, but there are still problems such as high cost and complex operation. Especially in the aging monitoring of internal components of power equipment, due to the complex internal environment of power equipment and serious electromagnetic interference, it is difficult for existing monitoring technologies to obtain satisfactory monitoring results. Therefore, it is particularly important to develop a new technology that can overcome the shortcomings of existing technologies and is suitable for aging monitoring of internal components of power equipment.

[0005] The present invention proposes an aging monitoring system for electric power equipment components based on capacitive coupling technology, aiming to solve the problems existing in the prior art. Summary of the invention

[0006] The present invention discloses a power equipment component aging monitoring system based on capacitive coupling technology, comprising: a ring electrode sensor, a preamplifier, a data acquisition card, and a system integration platform;

[0007] The annular electrode sensor is installed on the outer shell of the power equipment, senses the electric field changes inside the power equipment in a non-contact manner, and outputs a weak electric signal;

[0008] The preamplifier receives the weak electrical signal output by the annular electrode sensor and converts it into an electrical signal for output;

[0009] The data acquisition card receives the electrical signal output by the preamplifier and converts it into a digital signal for output;

[0010] The system integration platform includes a signal processing module, a data storage module, a machine learning module, a communication module, a visualization module and an alarm module;

[0011] The signal processing module filters and extracts features from the collected digital signal, and outputs feature information of the digital signal;

[0012] The data storage module stores the collected raw data and processed feature information;

[0013] The machine learning module combines the original data and feature information, uses a machine learning algorithm to establish an equipment aging model, and outputs an evaluation result through the equipment aging model;

[0014] The communication module is responsible for data transmission, and receives feature information from the signal processing module and evaluation results from the machine learning module, and transmits them to the data storage module and the visualization module;

[0015] The visualization module receives the evaluation results transmitted by the communication module and displays the health status of the device through the user interface;

[0016] The alarm module triggers an alarm through the evaluation result transmitted by the communication module.

[0017] Preferably, the annular electrode sensor interacts with the electric field generated inside the power equipment through capacitive coupling technology. The annular electrode sensor and the internal electric field of the power equipment form a capacitor. When the internal electric field of the power equipment changes, the charge distribution of the capacitor changes, generating a tiny current. The annular electrode sensor outputs a weak electrical signal to the preamplifier by receiving the tiny current.

[0018] Preferably, a high-gain operational amplifier chip and a low-noise operational amplifier chip are arranged inside the preamplifier, and the weak electrical signal realizes high gain of the weak electrical signal through the multi-stage amplification circuit and the high-gain operational amplifier chip inside the preamplifier, and realizes low noise of the weak electrical signal through the low-noise operational amplifier chip and the decoupling capacitor at the input end.

[0019] Preferably, the data acquisition card filters the electrical signal through an internal signal conditioning circuit, converts the electrical signal into a digital signal through an analog converter, and transmits the digital signal to the signal processing module and the data storage module through the communication module of the system integration platform.

[0020] Preferably, the signal processing module receives the digital signal from the digital acquisition card for filtering and feature extraction; the digital signal is filtered by an adaptive filtering technology, and the adaptive filtering technology removes erroneous digital signals and repeated digital signals in the digital signal.

[0021] Preferably, the feature extraction obtains feature information by extracting digital signals; the feature extraction includes Fourier transform technology and wavelet transform technology to extract spectral features and multi-scale features from digital signals; the spectral features extract the amplitude and phase information of different frequency components through Fourier transform to reflect the changes in the electric field inside the power equipment; the multi-scale features extract the features of the signal at different time scales through wavelet transform technology to capture transient and local changes in the signal.

[0022] Preferably, the machine learning module uses the original data and feature information of the label to perform training through a random forest algorithm to establish an equipment aging model; the equipment aging model outputs an evaluation result through an input real-time digital signal, which is transmitted to the visualization module and the alarm module through the communication module.

[0023] Preferably, the visualization module receives evaluation results and feature information, displays the health status, historical trends and fault warning information of the equipment through a user interface, and provides intuitive visualization reports; the visualization module adopts dynamic data update technology and realizes real-time data transmission through WebSocket, ensuring that the user interface can display the latest monitoring results and evaluation reports in real time.

[0024] Preferably, the early warning module monitors the health status of the equipment in real time, and triggers an alarm when the health status of the equipment reaches a preset threshold; the early warning module also adopts a multi-level early warning mechanism, setting different early warning level thresholds according to different stages of equipment aging, and accurately predicting the degree of equipment aging.

[0025] The method for monitoring aging of power equipment components based on capacitive coupling technology includes the following steps:

[0026] S1. Install the ring electrode sensor on the outer shell of the power equipment, sense the change of the internal electric field by non-contact method, and output weak electric signal;

[0027] S2, receiving the weak electrical signal of the ring electrode sensor through the preamplifier, and outputting the amplified electrical signal through the preamplifier;

[0028] S3, using a data acquisition card to receive the electrical signal from the preamplifier, filtering it, converting the electrical signal into a digital signal through an analog converter, and outputting the digital signal;

[0029] S4, the signal processing module receives the digital signal from the data acquisition card, filters it through adaptive filtering technology, and uses Fourier transform and wavelet transform to extract feature information;

[0030] S5. The machine learning module combines the original data and feature information, uses the random forest algorithm to establish an equipment aging model, outputs the evaluation results of the equipment health status, and transmits the evaluation results to the visualization module and early warning module to monitor the degree of equipment aging.

[0031] Compared with the prior art, the technical solution of this application has the following technical effects:

[0032] The present invention adopts a non-contact annular electrode sensor to form a capacitor with the internal electric field of the power equipment, thereby solving the operational inconvenience and safety hazard problems caused by the traditional monitoring method that requires direct contact with the internal components of the power equipment. The annular electrode sensor can monitor the electric field changes inside the power equipment in real time without stopping the equipment, output weak electrical signals, and thus realize continuous monitoring of the aging status of the power equipment components, greatly improving the efficiency and safety of monitoring and ensuring the stable operation of the power system.

[0033] The present invention solves the problems of low signal-to-noise ratio and signal distortion in the process of amplifying weak electrical signals by using high-gain operational amplifier chips and low-noise operational amplifier chips set inside the preamplifier, effectively amplifies the weak electrical signals from the annular electrode sensor, and reduces noise interference to ensure signal purity. After being processed by the preamplifier, the quality of the electrical signal is significantly improved, providing a reliable basis for subsequent data processing, thereby improving the accuracy and reliability of the entire monitoring system.

[0034] The present invention uses adaptive filtering technology, Fourier transform and wavelet transform technology to extract features through the signal processing module, solves the interference problem of erroneous signals and repeated signals in the digital signal processing process, and the problem of how to extract useful features from complex signals, removes the erroneous and repeated parts in the digital signal, and can also extract the spectral features and multi-scale features reflecting the changes in the electric field inside the power equipment from the signal. This enables the system to more accurately analyze the health status of the power equipment, provides high-quality data support for the subsequent establishment of the equipment aging model, and enhances the intelligent analysis capability of the monitoring system.

[0035] The present invention uses a random forest algorithm to establish an equipment aging model through a machine learning module, solves the problem of how to use raw data and real-time monitoring data to evaluate the degree of equipment aging, and trains a model that can accurately evaluate the aging status of equipment by combining raw data and feature information monitored in real time. The model can output the evaluation results in real time, and transmit the results to the visualization module and the early warning module through the communication module, thereby realizing dynamic monitoring and early warning of the health status of power equipment. This not only improves the predictability and initiative of equipment maintenance, but also reduces the risk of accidents caused by equipment aging, providing a strong guarantee for the safe operation of the power system.

[0036] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application so that it can be implemented in accordance with the contents of the specification, and to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following is a detailed description of the preferred embodiments of the present application in conjunction with the accompanying drawings as follows.

[0037] Based on the detailed description of the specific embodiments of the present application in combination with the accompanying drawings below, those skilled in the art will become more aware of the above and other objects, advantages and features of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can also be obtained based on these drawings without creative work. In all drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, each element or part is not necessarily drawn according to the actual scale.

[0039] Figure 1 It is a module diagram of the power equipment component aging monitoring system based on capacitive coupling technology of the present invention;

[0040] Figure 2 The present invention is a flow chart of the method for monitoring aging of power equipment components based on capacitive coupling technology. DETAILED DESCRIPTION

[0041] To make the purpose, technical scheme and advantages of the embodiment of the present application clearer, the technical scheme in the embodiment of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiment of the present application. Obviously, the described embodiment is a part of the embodiment of the present application, rather than all of the embodiments. In the following description, specific details such as specific configuration and components are provided only to help fully understand the embodiments of the present application. Therefore, it should be clear to those skilled in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. In addition, for clarity and brevity, the description of known functions and structures is omitted in the embodiment.

[0042] It should be understood that the references to "one embodiment" or "this embodiment" throughout the specification mean that the specific features, structures, or characteristics associated with the embodiment are included in at least one embodiment of the present application. Therefore, the references to "one embodiment" or "this embodiment" appearing throughout the specification do not necessarily refer to the same embodiment. In addition, these specific features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0043] In addition, the present application may repeat reference numerals and / or letters in different examples. This repetition is for the purpose of simplicity and clarity, and does not in itself indicate the relationship between the various embodiments and / or settings discussed.

[0044] The term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, B exists alone, and A and B exist at the same time. The term " / and" in this article describes another type of association object relationship, indicating that there can be two relationships. For example, A / and B can mean: A exists alone, and A and B exist alone. In addition, the character " / " in this article generally indicates that the previous and next associated objects are in an "or" relationship.

[0045] The term "at least one" in this article is merely a description of the association relationship of associated objects, indicating that there may be three relationships. For example, at least one of A and B can mean: A exists alone, A and B exist at the same time, and B exists alone.

[0046] It should also be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusions.

[0047] Example 1

[0048] This embodiment mainly describes an aging monitoring system for power equipment components based on capacitive coupling technology. Figure 1 As shown, it includes: a ring electrode sensor, a preamplifier, a data acquisition card, and a system integration platform;

[0049] The annular electrode sensor is installed on the outer shell of the power equipment, and senses the electric field changes inside the power equipment in a non-contact manner. The annular electrode sensor can be installed on the transformer and circuit breaker of the power equipment to ensure that the main electric field change area of ​​the power equipment is covered to output weak electric signals;

[0050] Furthermore, the annular electrode sensor interacts with the electric field generated inside the power equipment through capacitive coupling technology. The annular electrode sensor and the internal electric field of the power equipment form a capacitor. When the internal electric field of the power equipment changes, the charge distribution of the capacitor changes, generating a tiny current. The annular electrode sensor outputs a weak electrical signal to the preamplifier by receiving the tiny current.

[0051] The preamplifier receives the weak electrical signal output by the annular electrode sensor and converts it into a processable electrical signal output;

[0052] Furthermore, a high-gain operational amplifier chip and a low-noise operational amplifier chip are arranged inside the preamplifier, and the weak electrical signal realizes high gain of the weak electrical signal through the multi-stage amplification circuit and the high-gain operational amplifier chip inside the preamplifier, and realizes low noise of the weak electrical signal through the low-noise operational amplifier chip and the decoupling capacitor at the input end.

[0053] The data acquisition card receives the electrical signal output by the preamplifier and converts it into a digital signal for output;

[0054] Furthermore, the data acquisition card filters the electrical signal through an internal signal conditioning circuit, converts the electrical signal into a digital signal through an analog converter, and transmits the digital signal to the signal processing module and the data storage module through the communication module of the system integration platform.

[0055] The system integration platform includes a signal processing module, a data storage module, a machine learning module, a communication module, a visualization module and an alarm module;

[0056] The signal processing module filters and extracts features from the collected digital signal, and outputs feature information of the digital signal;

[0057] Furthermore, the signal processing module receives the digital signal from the digital acquisition card for filtering and feature extraction; the digital signal is filtered by adaptive filtering technology, and the adaptive filtering technology removes erroneous digital signals and repeated digital signals from the digital signal; the adaptive filtering technology can remove random noise from the digital signal, which comes from the sensor itself, the preamplifier, power supply interference or other external electromagnetic interference; at the same time, the adaptive filtering technology can remove frequency components in the signal that are not related to device aging. For example, if the aging of the device is mainly manifested as changes in the electric field within a specific frequency range, the filter can remove signals in other frequency ranges to reduce interference; and the adaptive filtering technology can smooth transient spikes or mutations in the signal, which will affect subsequent feature extraction and analysis.

[0058] Furthermore, the feature extraction obtains feature information by extracting digital signals; the feature extraction includes Fourier transform technology and wavelet transform technology to extract spectrum features and multi-scale features from digital signals; the spectrum features extract the amplitude and phase information of different frequency components through Fourier transform to reflect the changes in the electric field inside the power equipment; the multi-scale features extract the features of the signal at different time scales through wavelet transform technology to capture transient and local changes in the signal.

[0059] Furthermore, spectral features refer to the amplitude and phase information of different frequency components extracted from digital signals. Through Fourier transform, time domain signals can be converted into frequency domain signals, so as to analyze the distribution of signals at different frequencies and reflect the changes in the electric field inside the power equipment. For example, the degradation of the insulating material inside the equipment may cause the electric field strength within a specific frequency range to increase or decrease. By analyzing the amplitude and phase changes of these frequency components, the health status and aging trend of the equipment can be effectively identified.

[0060] Furthermore, multi-scale features refer to characteristic information extracted from digital signals at different time scales. Through wavelet transform technology, the signal can be decomposed into sub-signals at different time scales, thereby capturing transient and local changes in the signal; for example, mechanical wear or local discharge inside the equipment may cause transient changes in the signal, which may be difficult to capture in traditional time domain or frequency domain analysis. By extracting multi-scale features through wavelet transform, the health status of the equipment can be more comprehensively evaluated, improving the accuracy and reliability of aging monitoring.

[0061] The data storage module stores the collected raw data and processed feature information;

[0062] The machine learning module combines the original data and feature information, uses a machine learning algorithm to establish an equipment aging model, and outputs an evaluation result through the equipment aging model;

[0063] Furthermore, the original data is the equipment operation data in the past period of time, including temperature, humidity, voltage, current, and electric field change data obtained through capacitive coupling technology; by annotating the original data, a training data set is generated, and a model is trained using a random forest algorithm through the training data set to establish an equipment aging model;

[0064] Furthermore, the machine learning module uses the original data and feature information of the label to perform training through a random forest algorithm to establish an equipment aging model; the equipment aging model outputs an evaluation result through an input real-time digital signal, and transmits it to the visualization module and the alarm module through the communication module.

[0065] The communication module is responsible for data transmission, and receives feature information from the signal processing module and evaluation results from the machine learning module, and transmits them to the data storage module and the visualization module;

[0066] The visualization module receives the evaluation results transmitted by the communication module and displays the health status of the device through the user interface;

[0067] Furthermore, the visualization module includes a front-end development module, a back-end development module and a user interface; the front-end development module receives the characteristic information and evaluation results transmitted by the communication module and transmits them to the back-end development module, and the back-end development module generates the health status, historical trend and fault warning information of the equipment through integration and transmits them to the user interface;

[0068] Furthermore, the visualization module receives evaluation results and feature information, displays the health status, historical trends and fault warning information of the equipment through the user interface, and provides intuitive visualization reports; the visualization module adopts dynamic data update technology and realizes real-time data transmission through WebSocket, ensuring that the user interface can display the latest monitoring results and evaluation reports in real time.

[0069] The alarm module triggers an alarm based on the evaluation results transmitted by the communication module. The early warning module monitors the health status of the equipment in real time and triggers an alarm when the health status of the equipment reaches a preset threshold. The early warning module also adopts a multi-level early warning mechanism, setting different early warning level thresholds according to different stages of equipment aging, so as to accurately predict the degree of equipment aging.

[0070] This embodiment describes in detail that the system uses a non-contact annular electrode sensor to sense the changes in the electric field inside the power equipment. Through components such as the preamplifier, data acquisition card, and system integration platform, real-time monitoring of the aging status of the power equipment components is achieved. This can not only improve the accuracy and efficiency of monitoring and reduce labor costs, but also achieve long-term tracking of the health status of the equipment and timely warn of potential faults, thereby effectively improving the safety and stability of the power system. In addition, the present invention also combines advanced machine learning algorithms to further improve the intelligence level and predictive ability of the monitoring system by constructing an equipment aging model.

[0071] Example 2

[0072] This embodiment is based on Embodiment 1 and describes in detail a method for monitoring aging of power equipment components based on capacitive coupling technology. Figure 2 As shown, the following steps are included;

[0073] S1. Install the ring electrode sensor on the outer shell of the power equipment, sense the change of the internal electric field by non-contact method, and output weak electric signal;

[0074] S2, receiving the weak electrical signal of the ring electrode sensor through the preamplifier, and outputting the amplified electrical signal through the preamplifier;

[0075] S3, using a data acquisition card to receive the electrical signal from the preamplifier, filtering it, converting the electrical signal into a digital signal through an analog converter, and outputting the digital signal;

[0076] S4, the signal processing module receives the digital signal from the data acquisition card, filters it through adaptive filtering technology, and uses Fourier transform and wavelet transform to extract feature information;

[0077] S5. The machine learning module combines the original data and feature information, uses the random forest algorithm to establish an equipment aging model, outputs the evaluation results of the equipment health status, and transmits the evaluation results to the visualization module and early warning module to monitor the degree of equipment aging.

[0078] This embodiment implements the power equipment component aging monitoring method based on capacitive coupling technology, which can realize real-time monitoring of the aging of the internal insulation material of the high-voltage transformer, detect small electric field changes at an early stage, and discover signs of aging in time.

[0079] The above are only preferred embodiments of the present invention, which do not limit the scope of protection of the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any changes, modifications, replacements, integrations and parameter changes to these embodiments within the spirit and principles of the present invention through conventional substitutions or without departing from the principles and spirit of the present invention fall within the scope of protection of the present invention.

Claims

1. The power equipment component aging monitoring system based on capacitive coupling technology is characterized by: include: Ring electrode sensor, preamplifier, data acquisition card, system integration platform; The annular electrode sensor is installed on the outer shell of the power equipment, senses the electric field changes inside the power equipment in a non-contact manner, and outputs a weak electric signal; The preamplifier receives the weak electrical signal output by the annular electrode sensor and converts it into an electrical signal for output; The data acquisition card receives the electrical signal output by the preamplifier and converts it into a digital signal for output; The system integration platform includes a signal processing module, a data storage module, a machine learning module, a communication module, a visualization module and an alarm module; The signal processing module filters and extracts features from the collected digital signal, and outputs feature information of the digital signal; The data storage module stores the collected raw data and processed feature information; The machine learning module combines the original data and feature information, uses a machine learning algorithm to establish an equipment aging model, and outputs an evaluation result through the equipment aging model; The communication module is responsible for data transmission, and receives feature information from the signal processing module and evaluation results from the machine learning module, and transmits them to the data storage module and the visualization module; The visualization module receives the evaluation results transmitted by the communication module and displays the health status of the device through the user interface; The alarm module triggers an alarm through the evaluation result transmitted by the communication module.

2. The power equipment component aging monitoring system based on capacitive coupling technology according to claim 1 is characterized in that: The annular electrode sensor interacts with the electric field generated inside the power equipment through capacitive coupling technology. The annular electrode sensor and the internal electric field of the power equipment form a capacitor. When the internal electric field of the power equipment changes, the charge distribution of the capacitor changes, generating a tiny current. The annular electrode sensor outputs a weak electrical signal to the preamplifier by receiving the tiny current.

3. The power equipment component aging monitoring system based on capacitive coupling technology according to claim 2 is characterized in that: A high-gain operational amplifier chip and a low-noise operational amplifier chip are arranged inside the preamplifier. The weak electrical signal realizes high gain of the weak electrical signal through the multi-stage amplification circuit and the high-gain operational amplifier chip inside the preamplifier, and realizes low noise of the weak electrical signal through the low-noise operational amplifier chip and the decoupling capacitor at the input end.

4. The power equipment component aging monitoring system based on capacitive coupling technology according to claim 1 or 3, characterized in that: The data acquisition card filters the electrical signal through an internal signal conditioning circuit, converts the electrical signal into a digital signal through an analog converter, and transmits the digital signal to the signal processing module and the data storage module through the communication module of the system integration platform.

5. The power equipment component aging monitoring system based on capacitive coupling technology according to claim 4 is characterized in that: The signal processing module receives the digital signal from the digital acquisition card for filtering and feature extraction; the digital signal is filtered by an adaptive filtering technology, and the adaptive filtering technology removes erroneous digital signals and repeated digital signals from the digital signal.

6. The power equipment component aging monitoring system based on capacitive coupling technology according to claim 5 is characterized in that: The feature extraction obtains feature information by extracting digital signals; the feature extraction includes Fourier transform technology and wavelet transform technology to extract spectrum features and multi-scale features from digital signals; the spectrum features extract the amplitude and phase information of different frequency components through Fourier transform to reflect the changes in the electric field inside the power equipment; the multi-scale features extract the features of the signal on different time scales through wavelet transform technology to capture transient and local changes in the signal.

7. The power equipment component aging monitoring system based on capacitive coupling technology according to claim 1 is characterized in that: The machine learning module uses the original data and feature information of the label to perform training through the random forest algorithm to establish an equipment aging model; the equipment aging model outputs an evaluation result through the input real-time digital signal, and transmits it to the visualization module and the alarm module through the communication module.

8. The power equipment component aging monitoring system based on capacitive coupling technology according to claim 7 is characterized in that: The visualization module receives the evaluation results and feature information, displays the health status, historical trends and fault warning information of the equipment through the user interface, and provides intuitive visualization reports; the visualization module adopts dynamic data update technology and realizes real-time data transmission through WebSocket, ensuring that the user interface can display the latest monitoring results and evaluation reports in real time.

9. The power equipment component aging monitoring system based on capacitive coupling technology according to claim 7 is characterized in that: The early warning module monitors the health status of the equipment in real time and triggers an alarm when the health status of the equipment reaches a preset threshold. The early warning module also adopts a multi-level early warning mechanism, setting different early warning level thresholds according to different stages of equipment aging, and accurately predicting the degree of equipment aging.

10. A method for monitoring aging of power equipment components based on capacitive coupling technology, characterized in that: The following steps are involved: S1. Install the ring electrode sensor on the outer shell of the power equipment, sense the change of the internal electric field by non-contact method, and output weak electric signal; S2, receiving the weak electrical signal of the ring electrode sensor through the preamplifier, and outputting the amplified electrical signal through the preamplifier; S3, using a data acquisition card to receive the electrical signal from the preamplifier, filtering it, converting the electrical signal into a digital signal through an analog converter, and outputting the digital signal; S4, the signal processing module receives the digital signal from the data acquisition card, filters it through adaptive filtering technology, and uses Fourier transform and wavelet transform to extract feature information; S5. The machine learning module combines the original data and feature information, uses the random forest algorithm to establish an equipment aging model, outputs the evaluation results of the equipment health status, and transmits the evaluation results to the visualization module and early warning module to monitor the degree of equipment aging.

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