High-voltage switch cabinet partial discharge detection system and detection method thereof

Abstract

The invention discloses a high-voltage switch cabinet partial discharge detection system and a detection method thereof, and relates to the technical field of discharge detection, and the method comprises the steps: collecting environment data and partial discharge characteristic data through a sensor, and carrying out the preprocessing; constructing a threshold calculation model based on the environment data and analyzing the partial discharge characteristic data; calculating a self-adaptive partial discharge detection threshold value under the current condition according to an analysis result; comparing the partial discharge characteristic data collected in real time with a threshold value obtained by self-adaptive calculation; and a regular self-calibration time period is set, and the threshold calculation model is updated and optimized according to the new calibration parameters. According to the method, environmental factors and historical partial discharge characteristics are fully considered, whether partial discharge is abnormal or not is accurately judged, misjudgment caused by environmental changes is reduced, the precision and accuracy of partial discharge detection are greatly improved, further deterioration of faults is effectively prevented, and the fault prevention capability of the high-voltage switch cabinet is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of discharge detection, and particularly to a partial discharge detection system for a high-voltage switchgear and a detection method thereof. Background Art

[0002] In recent years, high-voltage switchgears play a crucial role in the power system. They are responsible for controlling, protecting, and distributing electric energy to ensure the safe and stable operation of the power system. During long-term operation, due to the aging of internal insulation materials, uneven electric field distribution, and environmental factors, partial discharge phenomena are likely to occur in high-voltage switchgears. Partial discharge refers to the electrical discharge phenomenon in a local area within an insulation system under the action of an electric field. Although its discharge energy is small, its long-term existence will gradually erode the insulation material, resulting in a decline in insulation performance and ultimately possibly triggering a serious insulation breakdown fault, causing a power outage accident in the power system and bringing huge economic losses to social production and life. Therefore, it is of great significance to accurately detect and monitor the partial discharge of high-voltage switchgears. Most of the existing detection methods use a fixed threshold to judge whether the partial discharge is abnormal. This fixed threshold cannot adapt to the changes in different operating environments and operating states of high-voltage switchgears. In some harsh environments or during the equipment aging process, the fixed threshold may lead to misjudgment or missed judgment, and it is impossible to timely and effectively discover potential partial discharge fault hidden dangers. With the continuous improvement of the power system's requirements for power supply reliability and the continuous development of sensing technology, data analysis technology, and intelligent algorithms, there is an urgent need for a system and method that can detect the partial discharge of high-voltage switchgears more accurately and intelligently, improve the accuracy and reliability of partial discharge detection, realize real-time monitoring and effective early warning of the operating state of high-voltage switchgears, and ensure the safe and stable operation of the power system.

[0003] However, the common solutions currently available have many drawbacks, including: The existing technologies generally use a fixed threshold to determine whether the partial discharge is abnormal. This fixed mode cannot consider the variable environmental factors during the operation of high-voltage switchgears, which will change the insulation characteristics and thus affect the partial discharge situation, and ignores the differences in the device's own historical operation data. Different historical partial discharge characteristics and frequency information are not effectively integrated and utilized. This makes it possible for the fixed threshold to misjudge under different working conditions, misjudging normal partial discharge fluctuations as faults, or missing judgment when the partial discharge intensifies due to equipment aging or harsh environment, and it is impossible to timely detect potential serious fault risks, seriously endangering the safe and stable operation of the power system. Summary of the Invention

[0004] The purpose of this section is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this section, the abstract, and the title. However, such simplifications or omissions shall not be used to limit the scope of the present invention.

[0005] In view of the problems existing in the above-mentioned existing partial discharge detection system and method for high-voltage switchgear, the present invention is proposed.

[0006] Therefore, the object of the present invention is to provide a partial discharge detection system and method for high-voltage switchgear, which are applicable to solving the problems that the existing technology generally uses a fixed threshold to determine whether partial discharge is abnormal, ignoring the differences in the historical operation data of the equipment itself, and the different historical partial discharge characteristics and frequency information are not effectively integrated and utilized. This results in the fact that under different working conditions, the fixed threshold may lead to misjudgment, misjudging normal partial discharge fluctuations as faults, or missing judgments when partial discharge intensifies due to equipment aging or harsh environment, and being unable to detect potential serious fault risks in time, seriously endangering the safe and stable operation of the power system.

[0007] To solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, an embodiment of the present invention provides a partial discharge detection method for high-voltage switchgear, which includes collecting environmental data and partial discharge characteristic data by using sensors and performing preprocessing; constructing a threshold calculation model based on the environmental data and analyzing the partial discharge characteristic data; calculating an adaptive partial discharge detection threshold under the current situation according to the analysis results; comparing the real-time collected partial discharge characteristic data with the adaptively calculated threshold; setting a time period for regular self-calibration, and updating and optimizing the threshold calculation model according to the new calibration parameters.

[0009] As a preferred solution of the partial discharge detection method for high-voltage switchgear according to the present invention, wherein: the sensors include a temperature sensor, a humidity sensor, a pressure sensor, a gas composition sensor, and a partial discharge sensor; the environmental data includes temperature data, humidity data, pressure data, and gas composition data; the partial discharge characteristic data includes time-domain characteristic data, frequency-domain characteristic data, and data related to spatial position information.

[0010] As a preferred solution of the partial discharge detection method for high-voltage switchgear according to the present invention, the specific steps for constructing the threshold calculation model are as follows: divide the partial discharge characteristic data; construct a threshold calculation model based on a neural network and perform feature extraction on the environmental data and partial discharge characteristic data; use an adaptive algorithm to further extract features from the partial discharge characteristics; evaluate the threshold calculation model and adjust the model structure or parameters according to the evaluation results.

[0011] As a preferred solution of the partial discharge detection method for high-voltage switchgear according to the present invention, the specific formula for extracting features from the environmental data is as follows:

[0012]

[0013] Wherein, E(t) is the environmental data collected and preprocessed at time t; e1(t) is the temperature data at time t; e2(t) is the humidity data at time t; e3(t) is the air pressure data at time t; e4(t) is the gas composition data at time t;

[0014] The specific formula for extracting features from the partial discharge characteristic data is as follows:

[0015]

[0016] Wherein, F(i) is the partial discharge characteristic data collected and preprocessed for the i-th time; f i1 is the time-domain characteristic data for the i-th time; f i2 is the frequency-domain characteristic data for the i-th time; f i3 is the information data related to the spatial position information for the i-th time; m is the total number of times of collecting partial discharge characteristic data.

[0017] As a preferred solution of the partial discharge detection method for high-voltage switchgear according to the present invention, the specific formula for the adaptive partial discharge detection threshold is as follows:

[0018] Ti = E(t) + F(i)·T0;

[0019] Wherein, T is the adaptive partial discharge detection threshold; E(t) is the environmental data collected and preprocessed at time t; F(i) is the partial discharge characteristic data collected and preprocessed for the i-th time; T0 is the basic threshold constant.

[0020] As a preferred solution of the partial discharge detection method for high-voltage switchgear according to the present invention, specifically: the specific situation of the adaptive partial discharge detection threshold is as follows: when the real-time partial discharge characteristic data is less than the adaptive partial discharge detection threshold, it indicates that the partial discharge situation in the current high-voltage switchgear is within the normal range, indicating that there is no abnormal partial discharge in the equipment for the time being, and the power system can operate normally; when the real-time partial discharge characteristic data is equal to the adaptive partial discharge detection threshold, it indicates that the partial discharge situation in the current high-voltage switchgear is in a critical state and needs to be closely monitored; when the real-time partial discharge characteristic data is greater than the adaptive partial discharge detection threshold, it indicates that there is a partial discharge situation in the current high-voltage switchgear, and measures need to be taken in a timely manner. At the same time, record this abnormal data for subsequent update and optimization of the threshold calculation model.

[0021] As a preferred solution of the partial discharge detection method for high-voltage switchgear according to the present invention, specifically: the specific steps of the regular self-calibration are as follows: set the time interval of the regular self-calibration; when the self-calibration time is reached, the system suspends the normal detection work and enters the self-calibration mode to update the calibration parameters; after the calibration is completed, use the new calibration parameters to optimize and adjust the previously established threshold calculation model.

[0022] In a second aspect, to further solve the above technical problems, an embodiment of the present invention provides a partial discharge detection system for high-voltage switchgear, which includes: a data acquisition module for collecting environmental data and partial discharge characteristic data and performing preprocessing; a model construction module for constructing a threshold calculation model and analyzing the partial discharge characteristic data; a threshold calculation module for calculating the adaptive partial discharge detection threshold in the current situation; a real-time judgment module for judging the partial discharge situation in the current high-voltage switchgear; a model update module for updating and optimizing the threshold calculation model.

[0023] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, where: the memory stores a computer program, and specifically: when the computer program is executed by the processor, it implements any step of the partial discharge detection method for high-voltage switchgear as described in the first aspect of the present invention.

[0024] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and specifically: when the computer program is executed by the processor, it implements any step of the partial discharge detection method for high-voltage switchgear as described in the first aspect of the present invention.

[0025] The beneficial effects of the present invention are as follows: The adaptive threshold calculation mechanism of the present invention fully considers environmental factors and historical partial discharge characteristics, enabling the detection threshold to be flexibly adjusted according to the actual situation. In a high-temperature and high-humidity environment, environmental data will prompt the threshold to rise reasonably, more accurately judge whether partial discharge is abnormal, reduce misjudgment caused by environmental changes, greatly improve the accuracy and precision of partial discharge detection. By collecting and comparing partial discharge characteristic data with the adaptive threshold in real time, once the real-time data exceeds the threshold, abnormal partial discharge conditions can be detected in a timely manner, which helps maintenance personnel take targeted measures, effectively prevent the further deterioration of faults, and enhance the early warning and prevention capabilities for high-voltage switchgear faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings. Among them:

[0027] Figure 1 It is the implementation flowchart of the present invention in Embodiment 1.

[0028] Figure 2 It is the dynamic adjustment diagram of the threshold calculation model update and optimization in Embodiment 1. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following will describe the specific embodiments of the present invention in detail with reference to the drawings of the specification.

[0030] Many specific details are set forth in the following description in order to fully understand the present invention, but the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0031] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that mutually excludes other embodiments.

[0032] Embodiment 1

[0033] Refer to Figure 1 and Figure 2, which is the first embodiment of the present invention. This embodiment provides a method for detecting partial discharge in a high-voltage switchgear, including the following steps:

[0034] S1: Use sensors to collect environmental data and partial discharge characteristic data and perform preprocessing.

[0035] Preferably, as Figure 1 shown in the implementation process of the present invention, first use sensors to collect environmental data and partial discharge characteristic data and perform preprocessing, then build a threshold calculation model based on the environmental data and analyze the partial discharge characteristic data, calculate the adaptive partial discharge detection threshold in the current situation according to the analysis results, then compare the real-time collected partial discharge characteristic data with the threshold obtained by adaptive calculation, and finally set a time period for regular self-calibration, and update and optimize the threshold calculation model according to the new calibration parameters.

[0036] Furthermore, the sensors include a temperature sensor, a humidity sensor, a pressure sensor, a gas composition sensor, and a partial discharge sensor.

[0037] Furthermore, the environmental data includes temperature data, humidity data, pressure data, and gas composition data.

[0038] Furthermore, the partial discharge characteristic data includes time-domain characteristic data, frequency-domain characteristic data, and data related to spatial position information.

[0039] Specifically, the preprocessing performs real-time preprocessing of the partial discharge characteristic data and environmental data on the data node, including data cleaning, noise reduction, and standardization, to ensure the quality and consistency of the data.

[0040] S2: Build a threshold calculation model based on the environmental data and analyze the partial discharge characteristic data.

[0041] Preferably, the specific steps for building the threshold calculation model are as follows: Divide the partial discharge characteristic data.

[0042] Build a threshold calculation model based on a neural network and perform feature extraction on the environmental data and partial discharge characteristic data.

[0043] Use an adaptive algorithm to further extract features of the partial discharge characteristics.

[0044] Evaluate the threshold calculation model and adjust the model structure or parameters according to the evaluation results.

[0045] Furthermore, the specific formula for feature extraction of the environmental data is as follows:

[0046]

[0047] Among them, E(t) is the environmental data collected and preprocessed at time t; e1(t) is the temperature data at time t; e2(t) is the humidity data at time t; e3(t) is the air pressure data at time t; e4(t) is the gas component data at time t.

[0048] The specific formula for feature extraction of partial discharge characteristic data is as follows:

[0049]

[0050] Among them, F(i) is the partial discharge characteristic data collected and preprocessed for the i-th time; f i1 is the time-domain characteristic data for the i-th time; f i2 is the frequency-domain characteristic data for the i-th time; f i3 is the data related to the spatial position information for the i-th time; m is the total number of times of collecting partial discharge characteristic data.

[0051] Preferably, by incorporating environmental data into the consideration scope of threshold calculation, it can dynamically adapt to the variation law of partial discharge under different operating environments. The model can adjust the threshold according to environmental data to avoid misjudgment caused by environmental factors.

[0052] S3: Calculate the adaptive partial discharge detection threshold under the current situation according to the analysis result.

[0053] Preferably, the specific formula for the adaptive partial discharge detection threshold is as follows:

[0054] T = E(t) + F(i)·T0;

[0055] Among them, T is the adaptive partial discharge detection threshold; E(t) is the environmental data collected and preprocessed at time t; F(i) is the partial discharge characteristic data collected and preprocessed for the i-th time; T0 is the basic threshold constant.

[0056] Preferably, the calculated adaptive partial discharge detection threshold can reflect the actual operating state of the high-voltage switchgear in real time. It comprehensively considers the current environmental factors and partial discharge characteristics, and flexibly adjusts under different working conditions, ensuring the rationality and effectiveness of the threshold.

[0057] S4: Compare the partial discharge characteristic data collected in real time with the threshold obtained by adaptive calculation.

[0058] Preferably, the specific situation of the adaptive partial discharge detection threshold is as follows: When the real-time partial discharge characteristic data is less than the adaptive partial discharge detection threshold, it indicates that the partial discharge situation in the current high-voltage switchgear is within the normal range, indicating that there is no abnormal partial discharge in the equipment for the time being, and the power system can operate normally.

[0059] When the real-time partial discharge characteristic data is equal to the adaptive partial discharge detection threshold, it indicates that the partial discharge situation in the current high-voltage switchgear is in a critical state and needs to be closely monitored.

[0060] When the real-time partial discharge characteristic data is greater than the adaptive partial discharge detection threshold, it indicates that there is a partial discharge situation in the current high-voltage switchgear. Measures need to be taken in a timely manner, and at the same time, record this abnormal data for subsequent update and optimization of the threshold calculation model.

[0061] Preferably, compared with the fixed threshold, this adaptive threshold can more accurately judge whether the partial discharge is abnormal, reduce false alarms and missed alarms. Comparing the real-time collected partial discharge characteristic data with the adaptive threshold can timely detect abnormal partial discharge situations, providing a key time window for preventive maintenance of equipment. Once an abnormality is detected, measures can be taken immediately.

[0062] S5: Set the time period for regular self-calibration, and update and optimize the threshold calculation model according to the new calibration parameters.

[0063] Specifically, the specific steps of regular self-calibration are as follows: Set the time interval for regular self-calibration.

[0064] When the self-calibration time is reached, the system pauses normal detection work and enters the self-calibration mode to update the calibration parameters.

[0065] After calibration is completed, optimize and adjust the previously established threshold calculation model using the new calibration parameters.

[0066] Preferably, setting the time period for regular self-calibration and updating and optimizing the threshold calculation model according to the new calibration parameters can enable the detection system to adapt to the performance changes during the long-term operation of the high-voltage switchgear. As the equipment ages, the long-term evolution of environmental conditions, and the gradual change of partial discharge characteristics, the original threshold calculation model may deviate. Through regular self-calibration, using the newly accumulated data to re-adjust the model parameters can ensure that the model always maintains high accuracy and adaptability, and ensure the effectiveness of the detection system.

[0067] This embodiment also provides a partial discharge detection system for high-voltage switchgear, including: a data acquisition module for collecting environmental data and partial discharge characteristic data and performing preprocessing; a model construction module for constructing a threshold calculation model and analyzing the partial discharge characteristic data; a threshold calculation module for calculating the adaptive partial discharge detection threshold in the current situation; a real-time judgment module for judging the partial discharge situation in the current high-voltage switchgear; a model update module for updating and optimizing the threshold calculation model.

[0068] This embodiment also provides a computer device, which is applicable to a method for detecting partial discharge in a high-voltage switchgear cabinet, and includes: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement a method for detecting partial discharge in a high-voltage switchgear cabinet as proposed in the above embodiment.

[0069] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0070] This embodiment also provides a storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements a method for detecting partial discharge in a high-voltage switchgear cabinet as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM for short), Electrically Erasable Programmable Read-Only Memory (EEPROM for short), Erasable Programmable Read-Only Memory (EPROM for short), Programmable Read-Only Memory (PROM for short), Read-Only Memory (ROM for short), magnetic memory, flash memory, a magnetic disk, or an optical disc.

[0071] In summary, the adaptive threshold calculation mechanism of the present invention fully considers environmental factors and historical partial discharge characteristics, enabling the detection threshold to be flexibly adjusted according to the actual situation. In a high-temperature and high-humid environment, environmental data will prompt the threshold to rise reasonably, more accurately judge whether partial discharge is abnormal, reduce misjudgment caused by environmental changes, greatly improve the accuracy and precision of partial discharge detection. By real-time collecting and comparing partial discharge characteristic data with the adaptive threshold, once the real-time data exceeds the threshold, abnormal partial discharge conditions can be promptly detected, which helps maintenance personnel take targeted measures, effectively prevent the further deterioration of faults, and enhance the early warning and prevention capabilities for high-voltage switchgear faults.

[0072] Embodiment 2

[0073] Referring to Tables 1 to 3, this is the second embodiment of the present invention. The difference between this embodiment and the first embodiment is that, in order to verify its beneficial effects, the operation data and related descriptions of the present invention in the actual environment are provided.

[0074] As shown in Tables 1 and 2, the environmental data and partial discharge characteristic data collected in this example include temperature, humidity, air pressure, gas composition, time domain, frequency domain, and spatial position data, providing a data basis for subsequent judgment of the adaptive threshold and partial discharge conditions.

[0075] Table 1 Comparison Table of Partial Discharge Detection Data of High-Voltage Switchgear

[0076]

[0077] Table 2 Statistical Table of Threshold Misjudgment Conditions in Different Environments

[0078]

[0079] As shown in Table 3, this is a comparison table of the changes in the threshold accuracy rate of the present invention and the traditional method after long-term operation.

[0080] Table 3 Trend Table of Threshold Accuracy Rate Changes after Long-Term Operation

[0081] Switchgear group Accuracy rate change of traditional fixed threshold after long-term operation Accuracy rate change of adaptive threshold after long-term operation A Drop from 90% to 70% Drop from 95% to 92% B Drop from 85% to 65% Drop from 93% to 90% C Drop from 92% to 75% Drop from 94% to 91% D Drop from 88% to 68% Drop from 91% to 88% E Drop from 93% to 78% Drop from 96% to 93%

[0082] As can be seen from the above table, the present invention accurately judges whether partial discharge is abnormal, reduces misjudgment caused by environmental changes, greatly improves the accuracy and precision of partial discharge detection, effectively prevents the further deterioration of faults, and enhances the early warning and prevention capabilities for high-voltage switchgear faults.

[0083] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A method for detecting partial discharge of a high-voltage switch cabinet, characterized in that: include: Use sensors to collect environmental data and partial discharge characteristic data and perform preprocessing; Construct a threshold calculation model based on environmental data and analyze partial discharge characteristic data; Calculate the adaptive partial discharge detection threshold value in the current situation according to the analysis results; Compare the local discharge characteristic data collected in real time with the threshold value obtained by adaptive calculation; A time period for regular self-calibration is set to update and optimize the threshold calculation model according to new calibration parameters.

2. The method for detecting partial discharge of a high-voltage switch cabinet according to claim 1, characterized in that: The sensors include a temperature sensor, a humidity sensor, an air pressure sensor, a gas composition sensor and a partial discharge sensor; The environmental data includes temperature data, humidity data, air pressure data and gas composition data; The local discharge characteristic data includes time domain characteristic data, frequency domain characteristic data and spatial position information related data.

3. The method for detecting partial discharge of a high-voltage switch cabinet according to claim 2, characterized in that: The specific steps of constructing the threshold calculation model are as follows: dividing the partial discharge characteristic data; Construct a threshold calculation model based on a neural network and extract features from environmental data and partial discharge feature data; Further feature extraction of partial discharge features using adaptive algorithms; The threshold calculation model is evaluated and the model structure or parameters are adjusted according to the evaluation results.

4. The method for detecting partial discharge of a high-voltage switch cabinet according to claim 3, characterized in that: The specific formula for extracting features from environmental data is as follows: Among them, E(t) is the environmental data collected and preprocessed at time t; e1(t) is the temperature data at time t; e2(t) is the humidity data at time t; e3(t) is the air pressure data at time t; e4(t) is the gas composition data at time t; The specific formula for extracting the features of the partial discharge feature data is as follows: Wherein, F(i) is the local discharge characteristic data collected and preprocessed for the i-th time; f i1 is the time domain feature data of the ith time; f i2 is the frequency domain feature data of the ith time; f i3 is the spatial position information related information data of the ith time; m is the total number of times the partial discharge characteristic data is collected.

5. The method for detecting partial discharge of a high-voltage switch cabinet according to claim 4, characterized in that: The specific formula of the adaptive partial discharge detection threshold is as follows: T=E(t)+F(i)·T0; Wherein, T is the adaptive partial discharge detection threshold; E(t) is the environmental data collected and preprocessed at time t; F(i) is the partial discharge characteristic data collected and preprocessed for the i-th time; and T0 is the basic threshold constant.

6. The method for detecting partial discharge of a high-voltage switch cabinet according to claim 5, characterized in that: The specific situation of the adaptive partial discharge detection threshold is as follows: When the real-time partial discharge characteristic data is less than the adaptive partial discharge detection threshold, it indicates that the partial discharge situation in the current high-voltage switch cabinet is within the normal range, indicating that the equipment has no partial discharge abnormality for the time being and the power system can operate normally; When the real-time partial discharge characteristic data is equal to the adaptive partial discharge detection threshold, it indicates that the partial discharge situation in the current high-voltage switchgear is in a critical state and needs to be closely monitored; When the real-time partial discharge characteristic data is greater than the adaptive partial discharge detection threshold, it indicates that partial discharge has occurred in the current high-voltage switchgear, and timely measures need to be taken. At the same time, the abnormal data is recorded for subsequent updating and optimization of the threshold calculation model.

7. The method for detecting partial discharge of a high-voltage switch cabinet according to claim 1, characterized in that: The specific steps of the periodic self-calibration are as follows: Set the time interval for regular self-calibration; When the self-calibration time is reached, the system suspends normal detection work, enters the self-calibration mode, and updates the calibration parameters; After the calibration is completed, the threshold calculation model established previously is optimized and adjusted using the new calibration parameters.

8. A high-voltage switch cabinet partial discharge detection system, based on a high-voltage switch cabinet partial discharge detection method according to any one of claims 1 to 7, characterized in that: include, Data acquisition module, used to collect environmental data and partial discharge characteristic data and perform pre-processing; A model building module, used to build a threshold calculation model and analyze partial discharge characteristic data; A threshold calculation module, used to calculate an adaptive partial discharge detection threshold under current circumstances; Real-time judgment module, used to judge the current partial discharge situation in the high-voltage switch cabinet; The model updating module is used to update and optimize the threshold calculation model.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a high-voltage switch cabinet partial discharge detection method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for detecting partial discharge of a high-voltage switch cabinet as claimed in any one of claims 1 to 7 are implemented.

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