GIS partial discharge type identification method, system, device and medium

Abstract

The present application relates to the field of discharge detection, and more particularly to a GIS partial discharge type identification method, system, device and medium, comprising: collecting a partial discharge signal in a GIS to obtain a discharge pulse sequence; calculating the total number of power frequency cycles crossed by the discharge pulse sequence in a sliding time window; based on a timestamp, converting the time interval between adjacent discharge pulses into an interval cycle number in units of the power frequency cycle; in the sliding time window, calculating a discharge timing characteristic parameter according to the discharge pulse sequence and the interval cycle number; and determining a partial discharge mode in the GIS according to the discharge timing characteristic parameter. The present application can reliably distinguish occasional discharge modes from background noise and other regular discharges, significantly improving the detection rate and identification accuracy of early latent insulation defects, and providing a more timely and comprehensive basis for equipment condition assessment.

Description

Technical Field

[0001] This invention relates to the field of discharge detection, and specifically to a method, system, device, and medium for identifying partial discharge types in GIS. Background Technology

[0002] Gas-Insulated Switchgear (GIS) is a type of enclosed electrical equipment that uses high-pressure gas as the insulating medium. It is widely used in high-voltage and ultra-high-voltage power systems, especially in urban power grids, substations, and special environments. GIS has become an indispensable key device in modern power systems due to its advantages such as small footprint, high operational reliability, low maintenance workload, and minimal environmental impact. However, during long-term operation or manufacturing and installation, insulation defects may form inside the GIS due to factors such as aging of insulation materials, improper assembly processes, foreign object intrusion, or mechanical stress. These defects include free particles, floating potentials, air gaps in insulators, or interface cracks. These defects can lead to local electric field distortion, causing partial discharge. Partial discharge is a precursor to early degradation and eventual breakdown of the insulation system. The high-energy electrons, photons, and sound waves generated by partial discharge gradually erode the insulation material. If not detected and addressed in time, it may cause equipment failure and threaten the safe and stable operation of the power grid. Effective detection and identification of partial discharge in GIS is of crucial engineering significance for assessing equipment health, implementing predictive maintenance, and preventing sudden power outages.

[0003] Currently, partial discharge identification in GIS mainly employs analysis methods based on phase-resolved partial discharge (PRPD) and phase-resolved pulse sequence (PRPS) analysis. These methods construct statistical maps by statistically analyzing the phase distribution, amplitude, or frequency information of a large number of discharge pulses within the power frequency cycle, and then use pattern recognition algorithms or threshold warning mechanisms to diagnose the discharge type. This type of technical solution relies on the statistical significance of discharge events in long-term monitoring data, requiring a sufficient cumulative number of discharge pulses within the monitoring period to form distinguishable statistical characteristics. However, for sporadic and random discharges, due to their extremely low frequency and sparse sample size, it is difficult to accumulate sufficient statistical samples within a limited monitoring period. This results in statistical maps that fail to present significant characteristics, leading to misjudgments as random noise or inability to identify them, causing missed or misdiagnosed early-stage hidden defects. This deficiency severely restricts the application effectiveness of existing technologies in early insulation fault warning in GIS, preventing the timely detection of latent equipment risks. Summary of the Invention

[0004] To address the aforementioned problems, this invention provides a method, system, device, and medium for identifying partial discharge types in GIS.

[0005] The first aspect of this invention discloses a method for identifying partial discharge types in GIS, comprising: Partial discharge signals in GIS are collected by sensors and preprocessed to extract multiple discharge pulses representing discrete discharge events, forming a discharge pulse sequence composed of the timestamps of each discharge pulse occurrence. Using the power frequency cycle of the power grid where the GIS is located as the reference time unit, calculate the total number of power frequency cycles spanned by the discharge pulse sequence within a preset sliding time window; Based on the timestamps in the discharge pulse sequence, the time interval between adjacent discharge pulses is calculated, and the time interval is converted into the number of interval cycles in units of the power frequency cycle; Within the sliding time window, discharge timing characteristic parameters are calculated based on the discharge pulse sequence, the total number of power frequency cycles, and the number of interval cycles, including: large interval proportion, burst concentration, subsequent silence, active cycle rate, and interval variation coefficient. Based on the discharge timing characteristic parameters, the partial discharge in the GIS within the sliding time window is determined to be one of the following: occasional discharge mode, infrequent discharge mode, or frequent discharge mode.

[0006] Furthermore, the large interval ratio refers to a parameter that measures the sparsity and randomness of the discharge pulse in time distribution; And, the method of obtaining it is as follows: Within the sliding time window, the number of interval cycles between all adjacent discharge pulses is counted to obtain multiple adjacent interval cycle numbers; Calculate the number of adjacent intervals that are greater than a preset interval threshold among all adjacent intervals to obtain the first value; Divide the first value by the number of all adjacent interval periods to obtain the proportion of large intervals.

[0007] Furthermore, the burst concentration refers to a parameter that measures the degree of burst concentration of the discharge pulse; And, the method of obtaining it is as follows: The sliding time window is divided into multiple sub-windows according to a preset duration; Count the number of discharge pulses within each sub-window; The sub-window where the number of discharge pulses exceeds a preset burst threshold is defined as the burst sub-window; The burst concentration is obtained by calculating the ratio of the number of discharge pulses in the burst sub-window to the number of discharge pulses in the sliding time window.

[0008] Furthermore, the subsequent silence level refers to the continuous silence following the burst sub-window. The ratio of the total number of pulses in each sub-window to the total number of pulses in the sliding time window; And, the method of obtaining it is as follows: Statistics start from the burst sub-window and continue... The number of discharge pulses within each sub-window is used to obtain the number of continuous window pulses. The subsequent silence level is obtained by calculating the ratio of the number of continuous window pulses to the number of discharge pulses within the sliding time window.

[0009] Furthermore, the active cycle rate refers to a parameter that measures the distribution density of the discharge pulse at the power frequency cycle level; And, the method of obtaining it is as follows: Each discharge pulse is mapped to its corresponding power frequency cycle based on its timestamp. The number of active cycles is obtained by counting the number of all power frequency cycles containing at least one discharge pulse within the sliding time window. The ratio of the number of active cycles to the total number of power frequency cycles is calculated to obtain the active cycle rate.

[0010] Furthermore, the interval variation coefficient refers to a parameter that measures the regularity and stability of the discharge pulse time interval; And, the method of obtaining it is as follows: Calculate the time interval between all adjacent discharge pulses within the sliding time window; Calculate the arithmetic mean and standard deviation of the time intervals between all adjacent discharge pulses; The standard deviation is divided by the arithmetic mean to obtain the interval variation coefficient.

[0011] Furthermore, the step of determining the partial discharge in the GIS within the sliding time window as one of the following modes—intermittent discharge mode, infrequent discharge mode, or frequent discharge mode—based on the discharge timing characteristic parameters includes: Based on the large interval ratio, and / or the outbreak concentration, and / or the subsequent silence, and / or the active cycle rate, determine whether the partial discharge in the GIS within the sliding time window is an intermittent discharge mode; If not, then based on the active periodicity and the interval variation coefficient, it is determined whether the partial discharge in the GIS within the sliding time window is a low-frequency discharge mode or a high-frequency discharge mode.

[0012] A second aspect of this invention discloses a GIS partial discharge type identification system, comprising: The extraction module is used to collect partial discharge signals in GIS through sensors, preprocess them, extract multiple discharge pulses representing discrete discharge events, and form a discharge pulse sequence composed of the timestamps of each discharge pulse occurrence. The first calculation module is used to calculate the total number of power frequency cycles spanned by the discharge pulse sequence within a preset sliding time window, using the power frequency cycle of the power grid where the GIS is located as the reference time unit. The conversion module is used to calculate the time interval between adjacent discharge pulses based on the timestamps in the discharge pulse sequence, and convert the time interval into the number of interval cycles in units of the power frequency cycle; The second calculation module is used to calculate discharge timing characteristic parameters, including: large interval proportion, burst concentration, subsequent silence, active cycle rate, and interval variation coefficient, within the sliding time window based on the discharge pulse sequence, the total number of power frequency cycles, and the number of interval cycles. The determination module is used to determine the partial discharge in the GIS within the sliding time window as one of the following: occasional discharge mode, infrequent discharge mode, or frequent discharge mode, based on the discharge timing characteristic parameters.

[0013] A third aspect of the present invention discloses an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, implements the steps of any of the GIS partial discharge type identification methods disclosed in the first aspect of the present invention.

[0014] The fourth aspect of the present invention discloses a storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, it implements the steps of any of the GIS partial discharge type identification methods disclosed in the first aspect of the present invention.

[0015] This invention analyzes the temporal distribution patterns of pulse sequences rather than their phase statistics, effectively addressing the shortcomings of existing methods that rely on large statistical samples and cannot identify sporadic discharges. The core characteristics of sporadic discharges are their extremely low frequency of occurrence, long silence intervals, and randomness of eruptions; these characteristics are highly distinguishable over time. This invention extracts and quantifies temporal indicators that characterize these temporal distribution patterns, enabling the sensitive capture of the unique long silences followed by brief eruptions characteristic of sporadic discharges, even within monitoring windows with sparse pulse samples. Therefore, this invention reliably identifies sporadic discharge patterns from background noise and other regular discharges, significantly improving the detection rate and accuracy of early latent insulation defects, and providing a more timely and comprehensive basis for equipment condition assessment. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating a method for identifying partial discharge types in GIS, as disclosed in an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of a GIS partial discharge type identification system disclosed in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of the electronic device disclosed in the embodiments of the present invention. Detailed Implementation

[0018] To enable those skilled in the art to better understand the present invention, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, or product comprising a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, apparatus, or products.

[0020] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0021] Please see Figure 1 As shown, Figure 1 This is a flowchart illustrating a GIS partial discharge type identification method disclosed in an embodiment of the present invention. Figure 1As shown, the GIS partial discharge type identification method may include the following operations: S101. Collect partial discharge signals in GIS through sensors, preprocess them, extract multiple discharge pulses representing discrete discharge events, and form a discharge pulse sequence composed of the timestamps of each discharge pulse occurrence. In one optional embodiment, an ultra-high frequency sensor is used to acquire electromagnetic wave signals within the GIS cavity. To ensure the integrity of the captured partial discharge pulse waveform, high-bandwidth sampling technology is used to continuously acquire and record the sensor output signal, and the timestamp of each sampling point is recorded synchronously. For example, the high-bandwidth sampling used can have a bandwidth of no less than 2 GHz, and the timestamp is accurate to milliseconds.

[0022] The acquired partial discharge signals undergo preprocessing, including filtering and amplitude normalization. The filtering process employs a bandpass filter to effectively preserve the ultra-high frequency signal components of the partial discharge while suppressing low-frequency power frequency interference and out-of-band noise. In this embodiment, the passband range of the bandpass filter can be set to 300MHz to 3000MHz. Amplitude normalization standardizes the signal amplitude to eliminate amplitude deviations caused by differences in sensor sensitivity or signal propagation attenuation, ensuring comparability of pulse signals from different monitoring locations and laying the foundation for subsequent cross-site comprehensive analysis.

[0023] After the above preprocessing, discharge pulses representing individual discharge events are detected and extracted from the continuous signal stream using a set amplitude threshold or pulse waveform discrimination algorithm. Each extracted discharge pulse is associated with two key parameters: pulse arrival time and normalized pulse amplitude. Finally, all identified discharge pulses are arranged in chronological order of their occurrence, forming the discharge pulse sequence. This sequence accurately records the occurrence time of discharge events with high temporal resolution, providing accurate and consistent basic data for subsequent timestamp-based time-series feature calculations.

[0024] S102. Using the power frequency cycle of the power grid where the GIS is located as the reference time unit, calculate the total number of power frequency cycles spanned by the discharge pulse sequence within the preset sliding time window. In this optional embodiment, the power frequency period is determined based on the standard frequency of the power grid to which the GIS equipment is connected. For example, for a power grid with a frequency of 50Hz, the power frequency period is... For a power grid with a frequency of 60Hz, the power frequency period is approximately equal to... .

[0025] The length of the sliding time window can be set according to monitoring needs and analysis granularity. In this optional embodiment, the sliding time window is set to 1 hour.

[0026] S103. Based on the timestamps in the discharge pulse sequence, calculate the time interval between adjacent discharge pulses, and convert the time interval into the number of interval cycles in units of the power frequency cycle; In this optional embodiment, to analyze the time intervals of discharge pulses within a standardized framework synchronized with the power system's power frequency, the difference in timestamps between adjacent discharge pulses is converted into a relative interval number in units of the power grid's power frequency cycle. Through this conversion, the time intervals between all discharge events are normalized to a metric system scaled by the electrical cycle, linking the random temporal characteristics of discharges to the basic operating rhythm of the power grid. This provides a core data foundation for quantifying the sporadic and irregular nature of discharges in a periodic dimension.

[0027] S104. Within the sliding time window, discharge timing characteristic parameters are calculated based on the discharge pulse sequence, the total number of power frequency cycles, and the number of interval cycles, including: large interval ratio, burst concentration, subsequent silence, active cycle rate, and interval variation coefficient. In an optional embodiment, the large interval ratio refers to a parameter that measures the sparsity and randomness of the discharge pulse in time distribution; And, the method of obtaining it is as follows: Within the sliding time window, the number of interval cycles between all adjacent discharge pulses is counted to obtain multiple adjacent interval cycle numbers; Calculate the number of adjacent intervals that are greater than a preset interval threshold among all adjacent intervals to obtain the first value; Divide the first value by the number of all adjacent interval periods to obtain the proportion of large intervals.

[0028] In this optional embodiment, the period threshold can be set to 50 power frequency cycles, and the formula for obtaining the proportion of large intervals can be described as follows: ; in, Indicates the proportion of large intervals. Indicates the number of adjacent intervals. This indicates the function of taking the quantity. This represents the total number of discharge pulses in the discharge pulse sequence within the sliding time window.

[0029] One of the core characteristics of sporadic discharge patterns is that discharge events are extremely sparse in time and the intervals are highly uneven, with a large number of silent periods much longer than normal discharge intervals. By calculating the proportion of intervals with a number of cycles greater than a cycle threshold, this optional embodiment transforms the abstract characteristic of the sparsity and randomness of discharge in the time dimension into a concrete statistic that can be calculated in real time. This indicator can sensitively capture discharge activities that may be overlooked by traditional methods based on pulse counting or phase statistics, which are difficult to form effective statistical patterns due to long silences. This provides a stable and direct criterion for accurately identifying sporadic discharge patterns characterized by long silences in subsequent steps, thereby improving the system's early detection capability for latent and intermittent insulation defects.

[0030] In an optional embodiment, the burst concentration refers to a parameter that measures the degree of burst concentration of the discharge pulse; And, the method of obtaining it is as follows: The sliding time window is divided into multiple sub-windows according to a preset duration; Count the number of discharge pulses within each sub-window; The sub-window where the number of discharge pulses exceeds a preset burst threshold is defined as the burst sub-window; The burst concentration is obtained by calculating the ratio of the number of discharge pulses in the burst sub-window to the number of discharge pulses in the sliding time window.

[0031] In this optional embodiment, the sub-window can be set to 60 seconds, the burst intensity threshold can be set to 0.5, and the burst concentration can be described by the following formula: ; in, This indicates the concentration of the outbreak. This indicates the number of discharge pulses in the burst sub-window.

[0032] As can be seen, this optional embodiment characterizes discharge activity from the perspective of temporal clustering. For some sporadic or transient defects, the discharge is often not uniformly distributed, but rather erupts in a concentrated burst within a very short time, forming significant pulse clusters. The burst concentration index can accurately measure the intensity of this burst. Compared to simply looking at the total number of pulses or the average density, this index can effectively distinguish between two distinct discharge modes: short-duration high-intensity bursts and long-duration low-density bursts. This provides a key characteristic for identifying discharge types with typical burst characteristics, avoiding misjudgments of modes due to similar pulse counts.

[0033] In an optional embodiment, the subsequent silence refers to the continuous silence following the burst sub-window. The ratio of the total number of pulses in each sub-window to the total number of pulses in the sliding time window; And, the method of obtaining it is as follows: Statistics start from the burst sub-window and continue... The number of discharge pulses within each sub-window is used to obtain the number of continuous window pulses. The subsequent silence level is obtained by calculating the ratio of the number of continuous window pulses to the number of discharge pulses within the sliding time window.

[0034] In this optional embodiment, The value is 5. The subsequent method for obtaining the silence level can be described by the following formula: ; in, Indicates the degree of subsequent silence. This indicates the number of discharge pulses within five consecutive sub-windows following the burst sub-window.

[0035] As can be seen, this optional embodiment achieves an accurate characterization of the typical sporadic discharge mode of burst-silence. Many sporadic defects, after triggering a brief high-intensity discharge, quickly become dormant and remain inactive for a considerable period afterward. This is achieved by statistically analyzing the continuous burst sub-window. The pulse percentage of each sub-window and the low subsequent silence precisely characterize this rapid aftereffect decay. The subsequent silence parameter, combined with the burst concentration, constitutes a two-dimensional judgment condition, which can effectively distinguish between occasional events that subside after a brief burst and other types of periodically recurring bursts, greatly improving the specificity and reliability of identifying insulation defects with burst-silence timing characteristics.

[0036] In an optional embodiment, the active cycle rate refers to a parameter that measures the distribution density of the discharge pulses at the power frequency cycle level; And, the method of obtaining it is as follows: Each discharge pulse is mapped to its corresponding power frequency cycle based on its timestamp. The number of active cycles is obtained by counting the number of all power frequency cycles containing at least one discharge pulse within the sliding time window. The ratio of the number of active cycles to the total number of power frequency cycles is calculated to obtain the active cycle rate.

[0037] In this optional embodiment, the active cycle rate It can be described by the following formula: ; in, This indicates the number of power frequency cycles containing at least one discharge pulse. This represents the total number of power frequency cycles.

[0038] As can be seen, this optional embodiment characterizes discharge from the unique perspective of the breadth of power frequency phase activity. Different types of discharges have different degrees of correlation with the power frequency voltage phase: frequent discharges may be active in every cycle, while occasional discharges may only be triggered in a very small number of cycles. The active cycle rate directly reflects the distribution density or duty cycle of discharge activity at the power frequency cycle level. The active cycle rate is a macroscopic and stable indicator, less affected by fluctuations in the number of pulses within a single cycle. Through this indicator, discharges that may have a small total number of pulses but are scattered across multiple cycles can be effectively distinguished from discharges with a similar total number of pulses but concentrated in a very small number of cycles. This provides a key basis for judging whether a discharge has broad periodicity and enhances the classification system's ability to distinguish at the power frequency dimension.

[0039] In an optional embodiment, the interval variation coefficient refers to a parameter that measures the regularity and stability of the discharge pulse time interval; And, the method of obtaining it is as follows: Calculate the time interval between all adjacent discharge pulses within the sliding time window; Calculate the arithmetic mean and standard deviation of the time intervals between all adjacent discharge pulses; The standard deviation is divided by the arithmetic mean to obtain the interval variation coefficient.

[0040] In this optional embodiment, the interval variation coefficient It can be described by the following formula: ; in, This represents the standard deviation of the time interval between all adjacent discharge pulses. This represents the arithmetic mean of the time intervals between all adjacent discharge pulses.

[0041] As can be seen, this optional embodiment quantitatively characterizes the regularity or stability of the discharge pulse time series. For frequent discharges and other discharges strongly correlated with power frequency voltage, the pulse interval is often very stable with a small coefficient of variation; while for sporadic discharges with strong randomness, the pulse interval fluctuates greatly, and the coefficient of variation is also large. The interval variation coefficient eliminates the influence of the absolute time scale, allowing discharge sequences with different average frequencies to be compared in terms of regularity. This index provides a core and robust criterion for distinguishing between regular and random discharges. It condenses the fluctuation characteristics of the discharge time series into a single value, greatly facilitating the design and implementation of subsequent pattern determination rules. It is a key technical feature that ensures the entire classification method can accurately distinguish between regular patterns such as infrequent and frequent discharges and sporadic random patterns.

[0042] S105. Based on the discharge timing characteristic parameters, the partial discharge in the GIS within the sliding time window is determined to be one of the following: occasional discharge mode, infrequent discharge mode, or frequent discharge mode.

[0043] In an optional embodiment, the step of determining the partial discharge in the GIS within the sliding time window as one of an occasional discharge mode, a few-times discharge mode, or a frequent discharge mode based on the discharge timing characteristic parameters includes: Based on the large interval ratio, and / or the outbreak concentration, and / or the subsequent silence, and / or the active cycle rate, determine whether the partial discharge in the GIS within the sliding time window is an intermittent discharge mode; If not, then based on the active periodicity and the interval variation coefficient, it is determined whether the partial discharge in the GIS within the sliding time window is a low-frequency discharge mode or a high-frequency discharge mode.

[0044] In this optional embodiment, if any of the following conditions are met, the partial discharge in the GIS within the sliding time window is determined to be an intermittent discharge mode: Condition 1: The proportion of large intervals is greater than or equal to the preset occasional interval threshold; Condition 2: The burst concentration is greater than or equal to the burst threshold, and the subsequent silence is less than or equal to the preset silence threshold; Condition 3: The active cycle rate is less than or equal to the preset threshold value at the left end of the active cycle; If the partial discharge determination in the GIS within the sliding time window is not an intermittent discharge mode, then: Determine whether the active cycle rate is greater than the left threshold of the active cycle and less than or equal to the preset right threshold of the active cycle, and whether the interval variation coefficient is less than or equal to the preset first variation threshold. If so, determine that the partial discharge in the GIS within the sliding time window is a low-discharge mode. If the active cycle rate is greater than the threshold at the right end of the active cycle and the interval variation coefficient is less than or equal to the preset second variation threshold, then the partial discharge in the GIS within the sliding time window is determined to be a frequent discharge mode.

[0045] Wherein, the threshold at the left end of the active cycle is less than the threshold at the right end of the active cycle, and the first mutation threshold is greater than the second mutation threshold.

[0046] For example, the threshold for occasional interval is 0.8, the threshold for outbreak is 0.5, the threshold for silence is 0.05, the threshold for the left end of the active cycle is 0.1, the threshold for the right end of the active cycle is 0.5, the threshold for the first mutation is 0.8, and the threshold for the second mutation is 0.3.

[0047] In this example, an intermittent interval threshold of 0.8 indicates that more than 80% of adjacent discharge pulses within the sliding time window have intervals greater than 50 power frequency cycles. The intermittent interval threshold reflects the extreme temporal sparsity of discharge events. Physically, intermittent discharges are often triggered by occasional activation due to localized insulation defects, fluctuations in environmental factors, or external interference, resulting in highly uneven discharge intervals. This highly random and statistically unstable characteristic can be directly identified through the long interval ratio index.

[0048] A burst threshold of 0.5 and a silence threshold of 0.05 indicate that if more than 50% of the discharge pulses are concentrated in a short period of time, and almost no pulses appear for a long period afterward, it can be judged as a "burst-silence" type discharge. This mode typically corresponds to sporadic phenomena such as particle motion breakdown and transient air gap discharge in GIS. This criterion can effectively distinguish sporadic discharges from periodic discharges and reduce the probability of misjudgment.

[0049] A threshold of 0.1 at the left end of the active cycle indicates that discharge occurs in less than 10% of the power frequency cycles within the sliding time window, meaning that the equipment is in a silent state for the vast majority of cycles. The active cycle rate reflects the temporal sparsity of discharge at the cycle level, and can reliably detect extremely low-frequency events, especially sensitive to sporadic discharges with few pulses but high energy.

[0050] The smaller the coefficient of variation of the interval, the more stable the interval and the stronger the regularity. For the low-frequency discharge mode, although the number of discharge pulses is not large, the interval distribution tends to be stable and shows a certain periodicity; while the high-frequency discharge mode means that discharge occurs in most power frequency cycles, and its interval distribution is concentrated and stable, with a small coefficient of variation of the interval and a large active period rate.

[0051] The threshold values ​​in this optional embodiment are not subjective rules set by humans, but rather objective differences in the physical mechanisms and temporal statistical laws of discharges caused by different types of GIS insulation defects. For example, the sporadic interval threshold corresponds to the extreme temporal sparsity caused by random insulation breakdown; the combination of burst and silence thresholds accurately describes the burst-silence physical characteristics of transient events such as particle collisions; and the active periodicity threshold and interval variation coefficient threshold objectively distinguish the differences in the distribution density and periodic stability of discharge activity at the power frequency level. These thresholds are key quantitative boundaries that can most effectively distinguish different physical processes, derived from statistical analysis of a large amount of measured and experimental data, and are an important component of the objective law-based technical means in this embodiment.

[0052] As can be seen, this optional embodiment prioritizes the processing of the most distinctive and sensitively captured sporadic events, and then further subdivides the remaining events. This process has a clear structure and rigorous logic, ensuring that all patterns, especially sporadic patterns that are easily overlooked, receive priority and thorough review of judgment conditions. Simultaneously, it organically integrates discharge timing characteristic parameters into an efficient decision-making framework, allowing the value of various indicators to synergistically play a role in the judgment process, jointly forming a complete, self-consistent, and efficient automatic discharge pattern classification mechanism, ultimately achieving intelligent matching and optimization between algorithm calls and signal features.

[0053] Please see Figure 2 As shown, Figure 2 This is a schematic diagram of the structure of a GIS partial discharge type identification system disclosed in an embodiment of the present invention, including: The extraction module 201 is used to collect partial discharge signals in GIS through sensors, preprocess them, extract multiple discharge pulses representing discrete discharge events, and form a discharge pulse sequence composed of the timestamps of each discharge pulse occurrence. The first calculation module 202 is used to calculate the total number of power frequency cycles spanned by the discharge pulse sequence within a preset sliding time window, using the power frequency cycle of the power grid where the GIS is located as the reference time unit. The conversion module 203 is used to calculate the time interval between adjacent discharge pulses based on the timestamps in the discharge pulse sequence, and convert the time interval into the number of interval cycles in units of the power frequency cycle; The second calculation module 204 is used to calculate discharge timing characteristic parameters, including: large interval proportion, burst concentration, subsequent silence, active cycle rate, and interval variation coefficient, within the sliding time window based on the discharge pulse sequence, the total number of power frequency cycles, and the number of interval cycles. The determination module 205 is used to determine the partial discharge in the GIS within the sliding time window as one of the occasional discharge mode, the infrequent discharge mode, or the frequent discharge mode based on the discharge timing characteristic parameters.

[0054] Specific limitations regarding the GIS partial discharge type identification system can be found in the limitations of the GIS partial discharge type identification method described above, and will not be repeated here. Each module in the aforementioned GIS partial discharge type identification system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of the electronic device in hardware format or independently of it, or stored in the memory of the electronic device in software format, so that the processor can call the corresponding operations of each module.

[0055] It should be noted that, in order to highlight the innovative aspects of this invention, this embodiment does not include modules that are not closely related to solving the technical problems proposed by this invention, but this does not mean that there are no other modules in this embodiment.

[0056] like Figure 3 As shown, the electronic device 1 provided by the present invention may include a memory 12, a processor 13 and a bus, and may also include a computer program stored in the memory 12 and executable on the processor 13, such as a GIS partial discharge type identification program.

[0057] The memory 12 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 12 can be an internal storage unit of the electronic device 1, such as a portable hard drive. In other embodiments, the memory 12 can be an external storage device of the electronic device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the electronic device 1. Furthermore, the memory 12 can include both internal and external storage units of the electronic device 1. The memory 12 can be used not only to store application software and various types of data installed on the electronic device 1, such as codes for GIS partial discharge type identification, but also to temporarily store data that has been output or will be output.

[0058] In some embodiments, the processor 13 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits packaged with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 13 is the control core of the electronic device 1, connecting various components of the electronic device 1 via various interfaces and lines. It executes programs or modules (such as GIS partial discharge type identification programs) stored in the memory 12, and calls data stored in the memory 12 to perform various functions of the electronic device 1 and process data.

[0059] The processor 13 executes the operating system of the electronic device 1 and various installed applications. The processor 13 executes the applications to implement the steps in the above-described GIS partial discharge type identification method.

[0060] For example, the computer program may be divided into one or more modules, which are stored in the memory 12 and executed by the processor 13 to complete this application. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the electronic device 1. For example, the computer program may be divided into an extraction module 201, a first calculation module 202, a conversion module 203, a second calculation module 204, and a determination module 205.

[0061] The integrated unit implemented as a software functional module can be stored in a computer-readable storage medium, which can be non-volatile or volatile. The software functional module, stored in the storage medium, includes several instructions to cause a computer device (which may be a personal computer, computer equipment, or network device, etc.) or processor to execute some functions of the GIS partial discharge type identification method described in the various embodiments of this application.

[0062] In summary, the GIS partial discharge type identification method, system, device, and medium disclosed in this invention effectively solves the shortcomings of existing methods that rely on large statistical samples and cannot identify sporadic discharges by analyzing the temporal distribution patterns of pulse sequences rather than phase statistical characteristics. Since the core characteristics of sporadic discharges are their extremely low occurrence frequency, long silence intervals, and randomness of outbreaks, these characteristics are significantly distinguishable in the time dimension. This invention extracts and quantifies temporal indicators that characterize these temporal distribution patterns, enabling the sensitive capture of the long silence intervals followed by brief outbreaks characteristic of sporadic discharges even within a monitoring window with sparse pulse samples. Therefore, this invention can reliably identify sporadic discharge patterns from background noise and other regular discharges, significantly improving the detection rate and accuracy of early latent insulation defects, and providing a more timely and comprehensive basis for equipment condition assessment. Therefore, this invention effectively overcomes the various shortcomings of existing technologies and has high industrial application value.

[0063] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for identifying partial discharge types in GIS, characterized in that, The method includes: The partial discharge signal in the GIS is collected by the sensor and preprocessed to extract multiple discharge pulses representing discrete discharge events, forming a discharge pulse sequence composed of the timestamps of each discharge pulse occurrence. Using the power frequency cycle of the power grid where the GIS is located as the reference time unit, calculate the total number of power frequency cycles spanned by the discharge pulse sequence within a preset sliding time window; Based on the timestamps in the discharge pulse sequence, the time interval between adjacent discharge pulses is calculated, and the time interval is converted into the number of interval cycles in units of the power frequency cycle; Within the sliding time window, discharge timing characteristic parameters are calculated based on the discharge pulse sequence and the number of interval cycles, including: large interval proportion, burst concentration, subsequent silence, active cycle rate, and interval variation coefficient. Based on the discharge timing characteristic parameters, the partial discharge in the GIS within the sliding time window is determined to be one of the following: occasional discharge mode, infrequent discharge mode, or frequent discharge mode. Wherein, the large interval ratio refers to a parameter measuring the sparsity and randomness of the discharge pulse in time distribution; the burst concentration refers to a parameter measuring the degree of burst concentration of the discharge pulse; and the subsequent silence refers to the continuous silence after the burst sub-window. The sum of the pulses in each sub-window is the proportion of the total number of pulses in the sliding time window; the active cycle rate is a parameter that measures the distribution density of the discharge pulses at the power frequency cycle level; the interval variation coefficient is a parameter that measures the regularity and stability of the time interval of the discharge pulses.

2. The method for identifying partial discharge type in GIS according to claim 1, characterized in that, The method for obtaining the proportion of large intervals is as follows: Within the sliding time window, the number of interval cycles between all adjacent discharge pulses is counted to obtain multiple adjacent interval cycle numbers; Calculate the number of adjacent intervals that are greater than a preset interval threshold among all adjacent intervals to obtain the first value; Divide the first value by the total number of adjacent intervals to obtain the proportion of large intervals.

3. The method for identifying partial discharge types in GIS according to claim 1, characterized in that, The method for obtaining the burst concentration is as follows: The sliding time window is divided into multiple sub-windows according to a preset duration; Count the number of discharge pulses within each sub-window; The sub-window where the number of discharge pulses exceeds a preset burst threshold is defined as the burst sub-window; The burst concentration is obtained by calculating the ratio of the number of discharge pulses in the burst sub-window to the number of discharge pulses in the sliding time window.

4. The method for identifying partial discharge type in GIS according to claim 3, characterized in that, The method for obtaining the subsequent silence level is as follows: Statistics start from the burst sub-window and continue... The number of discharge pulses within each sub-window is used to obtain the number of continuous window pulses. The subsequent silence level is obtained by calculating the ratio of the number of continuous window pulses to the number of discharge pulses within the sliding time window.

5. The method for identifying partial discharge type in GIS according to claim 1, characterized in that, The active cycle rate is obtained as follows: Each discharge pulse is mapped to its corresponding power frequency cycle based on its timestamp. The number of active cycles is obtained by counting the number of all power frequency cycles containing at least one discharge pulse within the sliding time window. The ratio of the number of active cycles to the total number of power frequency cycles is calculated to obtain the active cycle rate.

6. The method for identifying partial discharge type in GIS according to claim 1, characterized in that, The interval variation coefficient is obtained as follows: Calculate the time interval between all adjacent discharge pulses within the sliding time window; Calculate the arithmetic mean and standard deviation of the time intervals between all adjacent discharge pulses; The standard deviation is divided by the arithmetic mean to obtain the interval variation coefficient.

7. The method for identifying partial discharge type in GIS according to claim 1, characterized in that, The step of determining the partial discharge in the GIS within the sliding time window as one of the following modes based on the discharge timing characteristic parameters includes: Based on the large interval ratio, and / or the outbreak concentration, and / or the subsequent silence, and / or the active cycle rate, determine whether the partial discharge in the GIS within the sliding time window is an intermittent discharge mode; If not, then based on the active periodicity and the interval variation coefficient, it is determined whether the partial discharge in the GIS within the sliding time window is a low-frequency discharge mode or a high-frequency discharge mode.

8. A GIS partial discharge type identification system, characterized in that, include: The extraction module is used to collect partial discharge signals in GIS through sensors, preprocess them, extract multiple discharge pulses representing discrete discharge events, and form a discharge pulse sequence composed of the timestamps of each discharge pulse occurrence. The first calculation module is used to calculate the total number of power frequency cycles spanned by the discharge pulse sequence within a preset sliding time window, with the power frequency cycle of the power grid where the GIS is located as the reference time unit. The conversion module is used to calculate the time interval between adjacent discharge pulses based on the timestamps in the discharge pulse sequence, and convert the time interval into the number of interval cycles in units of the power frequency cycle; The second calculation module is used to calculate discharge timing characteristic parameters based on the discharge pulse sequence and the number of interval cycles within the sliding time window, including: large interval ratio, burst concentration, subsequent silence, active cycle rate, and interval variation coefficient. The determination module is used to determine the partial discharge in the GIS within the sliding time window as one of the occasional discharge mode, the infrequent discharge mode, or the frequent discharge mode based on the discharge timing characteristic parameters. Wherein, the large interval ratio refers to a parameter measuring the sparsity and randomness of the discharge pulse in time distribution; the burst concentration refers to a parameter measuring the degree of burst concentration of the discharge pulse; and the subsequent silence refers to the continuous silence after the burst sub-window. The sum of the pulses in each sub-window is the proportion of the total number of pulses in the sliding time window; the active cycle rate is a parameter that measures the distribution density of the discharge pulses at the power frequency cycle level; the interval variation coefficient is a parameter that measures the regularity and stability of the time interval of the discharge pulses.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the GIS partial discharge type identification method as described in any one of claims 1 to 7.

10. A storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the GIS partial discharge type identification method as described in any one of claims 1 to 7.

Images