Insulation defect detection method and device for ultra-high voltage direct current GIS

Abstract

This application relates to the field of power transmission equipment monitoring technology, and particularly to a method and apparatus for detecting insulation defects in ultra-high voltage direct current (UHVDC) GIS. The method includes: acquiring photon counting signals and ultra-high frequency (UHF) signals from the gas-insulated metal-enclosed switchgear (GIS); determining whether the photon counting signal meets preset warning conditions; if the photon counting signal meets the preset warning conditions, then denoising and discretizing the UHF signal to obtain corresponding UHF data; generating feature maps of multiple parameter pairs based on the UHF data; and using the feature maps as an index to query a preset partial discharge feature fingerprint database to obtain the insulation defect detection results of the GIS. This solves the problem in related technologies where low-level electric field distortion caused by micro-defects or initial defects makes it difficult to detect insulation degradation in GIS in a timely manner, resulting in insufficient detection sensitivity and timeliness.

Description

Technical Field

[0001] This application relates to the field of power transmission equipment monitoring technology, and in particular to a method and device for detecting insulation defects in ultra-high voltage DC GIS. Background Technology

[0002] In my country, energy centers and load centers are spatially misaligned, leading to the widespread application of long-distance, high-capacity ultra-high-voltage direct current (UHVDC) transmission and distribution technology. UHVDC GIS (Gas Insulated Switchgear) has become a key piece of equipment in the power system due to its advantages of small footprint, superior insulation performance, and high operational reliability. Its insulation performance and operational reliability affect the overall safety level of the power grid.

[0003] In related technologies, insulation condition detection for GIS mainly relies on partial discharge detection technology. By collecting electrical signals, acoustic emission signals, and optical signals generated by defect discharge, insulation performance is evaluated. Among them, the ultra-high frequency method and ultrasonic method are suitable for the continuous operation of the power grid and can identify insulation defects that have undergone significant partial discharge, providing a basis for equipment operation and maintenance.

[0004] However, in related technologies, the low degree of electric field distortion caused by micro-defects or early defects makes it difficult to detect the insulation degradation of GIS in a timely manner, resulting in insufficient detection sensitivity and timeliness, which urgently needs to be addressed. Summary of the Invention

[0005] This application provides a method and apparatus for detecting insulation defects in ultra-high voltage direct current GIS, in order to solve the problem in related technologies that the degree of electric field distortion caused by micro-defects or early defects is low, making it difficult to detect the insulation degradation of GIS in a timely manner, and resulting in insufficient detection sensitivity and timeliness.

[0006] The first aspect of this application provides a method for detecting insulation defects in an ultra-high voltage direct current (UHVDC) GIS, comprising the following steps: acquiring photon counting signals and ultra-high frequency (UHF) signals of the gas-insulated metal-enclosed switchgear (GIS); determining whether the photon counting signals meet preset warning conditions; if the photon counting signals meet the preset warning conditions, then denoising and discretizing the UHF signals to obtain corresponding UHF data; generating feature maps of multiple parameter pairs based on the UHF data; and using the feature maps as indexes to query a preset partial discharge feature fingerprint database to obtain the insulation defect detection result of the GIS.

[0007] Based on the above technical means, this application embodiment effectively improves the ability to perceive early and weak insulation defects by collecting photon counting signals and ultra-high frequency signals. Then, by judging whether the photon counting signal meets certain early warning conditions, it can accurately locate potential insulation degradation events and avoid invalid detection. Furthermore, by denoising and discretizing the ultra-high frequency signals, noise interference is effectively reduced and the reliability of ultra-high frequency data is improved. Thus, feature maps of multiple parameter pairs are generated based on the ultra-high frequency data to obtain insulation defect detection results, realize the determination of insulation defect type, and improve the sensitivity and timeliness of GIS insulation defect detection.

[0008] Optionally, in one embodiment of this application, the acquisition of the photon counting signal of the GIS includes: acquiring the light signal generated by electroluminescence and gas ionization emission inside the GIS; performing avalanche multiplication processing on the light signal to form a current pulse; and obtaining the photon counting signal based on the current pulse.

[0009] Based on the above technical means, the embodiments of this application acquire optical signals and perform avalanche multiplication processing on the optical signals, thereby obtaining photon counting signals based on current pulses, effectively improving signal strength, improving the accuracy and stability of photon counting, achieving rapid response in the early stage of discharge, and providing a reliable triggering basis for subsequent ultra-high frequency signal acquisition and defect identification.

[0010] Optionally, in one embodiment of this application, determining whether the photon counting signal meets the preset warning condition includes: detecting whether the amplitude of the photon counting signal is greater than a preset amplitude; and determining that the photon counting signal meets the preset warning condition when the amplitude of the photon counting signal is greater than the preset amplitude and the duration is greater than a preset duration.

[0011] Based on the above technical means, the embodiments of this application can determine that the photon counting signal meets certain early warning conditions when the amplitude of the detected photon counting signal is greater than a certain amplitude and the duration is greater than a certain duration. This effectively improves the accuracy of signal recognition and anti-interference ability, avoids misjudgment caused by random noise, and effectively ensures the reliability of early warning.

[0012] Optionally, in one embodiment of this application, the expression of the plurality of parameter pairs may be, but is not limited to, as: , , in, f 1 is the first parameter pair. f 2 is the second parameter pair. f 3 is the third parameter pair. f 4 is the fourth parameter pair. f 5 is the fifth parameter pair.f 6 is the sixth parameter pair. A i For the first i The amplitude of each partial discharge pulse, t i For the first i The occurrence time of each partial discharge pulse, Δ t i For the first i The partial discharge pulse and the first i +1 partial discharge pulse time interval, Δ A i For the first i The partial discharge pulse and the first i +1 amplitude difference of partial discharge pulses, i This is the sequence number of the partial discharge pulse. N This represents the total number of partial discharge pulses.

[0013] Based on the above technical means, the embodiments of this application can comprehensively capture the characteristics of partial discharge signals from multiple perspectives such as amplitude, timing, and pulse correlation by constructing multiple parameter pairs, effectively improving the accuracy and robustness of insulation defect identification, avoiding misjudgment of defect types based on a single feature, and effectively enhancing the ability to distinguish different defect types.

[0014] Optionally, in one embodiment of this application, generating a feature map of multiple parameter pairs based on the UHF data includes: determining the amplitude and occurrence time of a partial discharge pulse based on the UHF data; determining the probability kernel density of the multiple parameter pairs based on the amplitude and occurrence time of the partial discharge pulse; and mapping the probability kernel density to a target space to generate the feature map.

[0015] Based on the above technical means, the embodiments of this application determine the probability kernel density of multiple parameter pairs, convert discrete data into density information that reflects statistical laws, and map the probability kernel density to the target space to generate a feature map, which can intuitively reflect the characteristics of insulation defects, improve the identification of features, and provide a reliable carrier for subsequent defect type identification.

[0016] A second aspect of this application provides an insulation defect detection device for an ultra-high voltage direct current GIS, comprising: a data acquisition module for acquiring photon counting signals and ultra-high frequency signals of the gas-insulated metal-enclosed switchgear GIS; a judgment module for judging whether the photon counting signals meet preset warning conditions; if the photon counting signals meet the preset warning conditions, the ultra-high frequency signals are denoised and discretized to obtain corresponding ultra-high frequency data; and a detection module for generating feature maps of multiple parameter pairs based on the ultra-high frequency data, and querying a preset partial discharge feature fingerprint database using the feature maps as indexes to obtain the insulation defect detection results of the GIS.

[0017] Based on the above technical means, this application embodiment effectively improves the ability to perceive early and weak insulation defects by collecting photon counting signals and ultra-high frequency signals. Then, by judging whether the photon counting signal meets certain early warning conditions, it can accurately locate potential insulation degradation events and avoid invalid detection. Furthermore, by denoising and discretizing the ultra-high frequency signals, noise interference is effectively reduced and the reliability of ultra-high frequency data is improved. Thus, feature maps of multiple parameter pairs are generated based on the ultra-high frequency data to obtain insulation defect detection results, realize the determination of insulation defect type, and improve the sensitivity and timeliness of GIS insulation defect detection.

[0018] Optionally, in one embodiment of this application, the acquisition module includes: an acquisition unit for acquiring light signals generated by electroluminescence and gas ionization emission inside the GIS; a first generation unit for performing avalanche multiplication processing on the light signals to form current pulses; and a second generation unit for obtaining the photon counting signal based on the current pulses.

[0019] Based on the above technical means, the embodiments of this application acquire optical signals and perform avalanche multiplication processing on the optical signals, thereby obtaining photon counting signals based on current pulses, effectively improving signal strength, improving the accuracy and stability of photon counting, achieving rapid response in the early stage of discharge, and providing a reliable triggering basis for subsequent ultra-high frequency signal acquisition and defect identification.

[0020] Optionally, in one embodiment of this application, the judgment module includes: a detection unit, configured to detect whether the amplitude of the photon counting signal is greater than a preset amplitude; and a determination unit, configured to determine that the photon counting signal meets the preset warning condition when the amplitude of the photon counting signal is detected to be greater than the preset amplitude and the duration is greater than the preset duration.

[0021] Based on the above technical means, the embodiments of this application can determine that the photon counting signal meets certain early warning conditions when the amplitude of the detected photon counting signal is greater than a certain amplitude and the duration is greater than a certain duration. This effectively improves the accuracy of signal recognition and anti-interference ability, avoids misjudgment caused by random noise, and effectively ensures the reliability of early warning.

[0022] Optionally, in one embodiment of this application, the expression of the plurality of parameter pairs may be, but is not limited to, as: , , in, f 1 is the first parameter pair. f 2 is the second parameter pair. f 3 is the third parameter pair. f 4 is the fourth parameter pair. f 5 is the fifth parameter pair. f 6 is the sixth parameter pair. A i For the first i The amplitude of each partial discharge pulse, t i For the first i The occurrence time of each partial discharge pulse, Δ t i For the first i The partial discharge pulse and the first i +1 partial discharge pulse time interval, Δ A i For the first i The partial discharge pulse and the first i +1 amplitude difference of partial discharge pulses, i This is the sequence number of the partial discharge pulse. N This represents the total number of partial discharge pulses.

[0023] Based on the above technical means, the embodiments of this application can comprehensively capture the characteristics of partial discharge signals from multiple perspectives such as amplitude, timing, and pulse correlation by constructing multiple parameter pairs, effectively improving the accuracy and robustness of insulation defect identification, avoiding misjudgment of defect types based on a single feature, and effectively enhancing the ability to distinguish different defect types.

[0024] Optionally, in one embodiment of this application, the detection module includes: a first determining unit, configured to determine the amplitude and occurrence time of a partial discharge pulse based on the UHF data; a second determining unit, configured to determine the probability kernel density of the plurality of parameter pairs according to the amplitude and occurrence time of the partial discharge pulse; and a third generating unit, configured to map the probability kernel density to a target space to generate the feature map.

[0025] Based on the above technical means, the embodiments of this application determine the probability kernel density of multiple parameter pairs, convert discrete data into density information that reflects statistical laws, and map the probability kernel density to the target space to generate a feature map, which can intuitively reflect the characteristics of insulation defects, improve the identification of features, and provide a reliable carrier for subsequent defect type identification.

[0026] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to implement the insulation defect detection method for ultra-high voltage direct current GIS as described in the above embodiments.

[0027] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for detecting insulation defects in ultra-high voltage direct current GIS.

[0028] The fifth aspect of this application provides a computer program product, including a computer program that, when executed, implements the above-described method for detecting insulation defects in ultra-high voltage direct current GIS.

[0029] This application's embodiments effectively enhance the detection capability of early, weak insulation defects by acquiring photon counting signals and ultra-high frequency (UHF) signals. Furthermore, by determining whether the photon counting signal meets certain early warning conditions, potential insulation degradation events can be accurately located, avoiding invalid detections. Subsequently, by denoising and discretizing the UHF signal, noise interference is effectively reduced, improving the reliability of the UHF data. Based on the UHF data, feature maps of multiple parameter pairs are generated to obtain insulation defect detection results, enabling the determination of insulation defect types and improving the sensitivity and timeliness of GIS insulation defect detection. Therefore, this solves the problem in related technologies where the low degree of electric field distortion caused by micro-defects or early-stage defects makes it difficult to detect GIS insulation degradation in a timely manner, resulting in insufficient detection sensitivity and timeliness.

[0030] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0031] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This application provides a GIS busbar single-section cavity detection system according to one embodiment. Figure 2This is a flowchart of an insulation defect detection method for an ultra-high voltage direct current GIS provided according to an embodiment of this application; Figure 3(a) is a schematic diagram of a feature map based on a first parameter pair according to an embodiment of this application; Figure 3(b) is a schematic diagram of a feature map based on a second parameter pair according to an embodiment of this application; Figure 3(c) is a schematic diagram of a feature map based on a third parameter pair according to an embodiment of this application; Figure 3(d) is a schematic diagram of a feature map based on a fourth parameter pair according to an embodiment of this application; Figure 3(e) is a schematic diagram of a feature map based on a fifth parameter pair according to an embodiment of this application; Figure 3(f) is a schematic diagram of a feature map based on a sixth parameter pair according to an embodiment of this application; Figure 4 A flowchart illustrating the principle of an insulation defect detection method for ultra-high voltage direct current GIS provided according to an embodiment of this application; Figure 5 This is a block diagram of an insulation defect detection device for ultra-high voltage direct current GIS provided according to an embodiment of this application; Figure 6 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application.

[0032] Figure label: 50 - Insulation defect detection device for ultra-high voltage DC GIS; 100 - Acquisition module, 200 - Judgment module, 300 - Detection module; 601 - Memory, 602 - Processor, 603 - Communication interface. Detailed Implementation

[0033] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0034] The following describes a method and apparatus for detecting insulation defects in ultra-high voltage direct current GIS (UHVDC) based on embodiments of this application, with reference to the accompanying drawings. Addressing the problem mentioned in the background art where the low degree of electric field distortion caused by micro-defects or early-stage defects makes it difficult to detect insulation degradation in GIS in a timely manner, resulting in insufficient detection sensitivity and timeliness, this application provides a method for detecting insulation defects in ultra-high voltage direct current GIS. In this method, by acquiring photon counting signals and ultra-high frequency (UHF) signals, the ability to detect early, weak insulation defects is effectively improved. Then, by determining whether the photon counting signal meets certain early warning conditions, potential insulation degradation events can be accurately located, avoiding invalid detection. Furthermore, by denoising and discretizing the UHF signal, noise interference is effectively reduced, improving the reliability of the UHF data. Based on the UHF data, feature maps of multiple parameter pairs are generated to obtain insulation defect detection results, enabling the determination of the insulation defect type and improving the sensitivity and timeliness of GIS insulation defect detection. Thus, the problem of low degree of electric field distortion caused by micro-defects or early-stage defects in related technologies, leading to difficulty in timely detection of GIS insulation degradation and insufficient detection sensitivity and timeliness, is solved.

[0035] Before introducing the insulation defect detection method of ultra-high voltage DC GIS provided in the embodiments of this application, we will first briefly introduce the GIS bus single-section cavity detection system adapted to this method.

[0036] like Figure 1 As shown, the GIS busbar single-section cavity detection system may include, but is not limited to, a photon counting probe 101, a counting gate circuit 102, an ultra-high frequency sensor 103, a sensor observation window and matching placement slot 104, a data acquisition card 105, and a host computer 106.

[0037] The photon counting probe 101 is located inside the sensor observation window and is used to collect the optical signals generated by the internal discharge of the GIS and output current pulses (analog signals).

[0038] The counting gate circuit 102 is located outside the cavity and is used to shape and count the current pulses and output a photon counting signal (digital signal).

[0039] The ultra-high frequency sensor 103 is used to collect electromagnetic signals in the 300 MHz to 3 GHz frequency band generated by internal discharge of GIS and output electromagnetic pulse signals (analog signals).

[0040] The sensor observation window and its matching placement slot 104 are located on the side of the cavity and feature a light-shielding design to isolate ambient light and electromagnetic interference, ensuring reliable signal acquisition. The matching placement slot is integrated into the sensor observation window and is used to position and install the photon counting probe 101 and the ultra-high frequency sensor 103, thereby improving the stability of signal acquisition.

[0041] The acquisition card 105 is used to receive the electromagnetic pulse signal output by the ultra-high frequency sensor 103 and perform analog-to-digital conversion, and to receive the photon counting signal output by the counting gate circuit 102, so as to transmit the two signals to the host computer 106.

[0042] The host computer 106 is used to receive the photon counting signal and ultra-high frequency signal transmitted by the acquisition card 105 in order to complete the discharge characteristic analysis and defect type identification.

[0043] The following is a detailed description of an insulation defect detection method for ultra-high voltage direct current GIS provided in the embodiments of this application.

[0044] Specifically, Figure 2 This is a flowchart of an insulation defect detection method for an ultra-high voltage direct current GIS provided according to an embodiment of this application.

[0045] like Figure 2 As shown, the insulation defect detection method of this ultra-high voltage DC GIS includes the following steps: In step S201, the photon counting signal and ultra-high frequency signal of the gas-insulated metal-enclosed switchgear (GIS) are acquired.

[0046] In the embodiments of this application, the photon counting signal can be understood as a digital signal obtained by detecting, amplifying and digitizing the weak light signal generated by the internal discharge of GIS, and used to quantify the effective number of photons.

[0047] Ultra-high frequency (UHF) signals can be understood as electromagnetic signals excited by internal discharges in GIS (Gas Insulators and GIS), which are collected and converted into electromagnetic pulse signals by UHF sensors, and then collected and converted by a data acquisition card to form digital signals that reflect the characteristics of partial discharges.

[0048] In actual implementation, due to the fact that the insulation defects inside GIS will generate optical radiation and electromagnetic radiation simultaneously before and after partial discharge, and optical signals have the characteristics of anti-electromagnetic interference, early response and high sensitivity, and ultra-high frequency signals have the characteristics of good stability, the embodiments of this application use photon counting to measure the optical signals generated by the insulation defects to collect photon counting signals, and measure the electromagnetic signals generated by the insulation defects to collect ultra-high frequency signals for subsequent feature map generation.

[0049] The following details how the embodiments of this application acquire photon counting signals from a GIS.

[0050] Specifically, in one embodiment of this application, acquiring the photon counting signal of a GIS includes: acquiring the light signal generated by electroluminescence and gas ionization emission inside the GIS; performing avalanche multiplication processing on the light signal to form a current pulse; and obtaining the photon counting signal based on the current pulse.

[0051] In some cases, embodiments of this application may include an optical sensor at the signal acquisition end. This optical sensor may include, but is not limited to, a photon counting probe and a counting gate circuit. The photon counting probe integrates a PMT (Photomultiplier Tube) with a spectral measurement range of 185 nm-850 nm. It is used to capture the light signals generated by electroluminescence and gas ionization emission inside the GIS. After electron avalanche multiplication by a high-voltage electrode, the weak light signal is converted into a current pulse. The counting gate circuit is used to convert the current pulse into a digital pulse and filter out noise, count the effective photons, and output a photon counting signal.

[0052] This application embodiment acquires an optical signal and performs avalanche multiplication processing on the optical signal to obtain a photon counting signal based on the current pulse, effectively improving the signal strength and the accuracy and stability of photon counting. It can achieve a rapid response in the early stage of discharge and provide a reliable triggering basis for subsequent UHF signal acquisition and defect identification.

[0053] In step S202, it is determined whether the photon counting signal meets the preset warning conditions. If the photon counting signal meets the preset warning conditions, the UHF signal is denoised and discretized to obtain the corresponding UHF data.

[0054] In the embodiments of this application, certain warning conditions can be understood as standards set by those skilled in the art to determine whether a warning is triggered. For example, a certain warning condition is that the amplitude of the photon counting signal is greater than a certain amplitude and the duration is greater than a certain duration. This application does not impose specific limitations.

[0055] Ultra-high frequency (UHF) data can be understood as a set of data obtained after preprocessing such as denoising and discretization of UHF signals, which is used to determine the amplitude and occurrence time of partial discharge pulses.

[0056] In actual implementation, the embodiments of this application can use the photon counting signal as the basis for judgment to quickly determine whether the photon counting signal meets certain early warning conditions. If the photon counting signal meets certain early warning conditions, the UHF signal is denoised and discretized to obtain UHF data.

[0057] The following details how embodiments of this application determine whether a photon counting signal meets certain early warning conditions.

[0058] Specifically, in one embodiment of this application, determining whether a photon counting signal meets a preset warning condition includes: detecting whether the amplitude of the photon counting signal is greater than a preset amplitude; and determining that the photon counting signal meets the preset warning condition if the amplitude of the photon counting signal is greater than the preset amplitude and the duration is greater than a preset duration.

[0059] In the embodiments of this application, a certain amplitude can be understood as an amplitude set by those skilled in the art to distinguish noise from real signals. For example, a certain amplitude is twice the absolute value of the coded signal. This application does not impose any specific limitations.

[0060] A certain duration can be understood as the duration during which the amplitude of the photon counting signal is greater than a certain value, as set by those skilled in the art. For example, a certain duration is 10 seconds. This application does not impose any specific limitation.

[0061] In actual execution, since random noise and transient interference in photon counting signals often manifest as weak-amplitude, short-duration spike pulses, while real signals have a certain signal strength and a certain duration, this embodiment first detects whether the amplitude of the photon counting signal is greater than a certain amplitude, and if the amplitude of the photon counting signal is greater than a certain amplitude, it detects whether the duration is greater than a certain duration. Thus, if both conditions are met, it is determined that the photon counting signal meets certain warning conditions.

[0062] The embodiments of this application can determine that the photon counting signal meets certain early warning conditions when the amplitude of the detected photon counting signal is greater than a certain value and the duration is greater than a certain duration. This effectively improves the accuracy of signal recognition and anti-interference ability, avoids misjudgment caused by random noise, and effectively ensures the reliability of the early warning.

[0063] Based on the description of other embodiments, by way of example, in this embodiment of the application, when the host computer detects a sudden change in the photon counting signal and the signal value is more than twice the absolute value of the dark mark, it starts recording the UHF signal. When the duration of the sudden change in the photon counting signal and the signal value being more than twice the absolute value of the dark mark is greater than 10 s, it determines that certain warning conditions are met and starts preprocessing of the UHF signal by denoising and discretization to obtain UHF data.

[0064] In step S203, feature maps of multiple parameter pairs are generated based on UHF data. Using the feature maps as indexes, a preset partial discharge feature fingerprint database is queried to obtain the insulation defect detection results of GIS.

[0065] In the embodiments of this application, parameter pairs can be understood as discriminative combinations of parameters selected by those skilled in the art from the parameters of partial discharge pulses. For example, a parameter pair is the first... i The amplitude of the first partial discharge pulse and the first i+ The amplitude of a partial discharge pulse is not specifically limited in this application.

[0066] Optionally, in one embodiment of this application, the expression of the plurality of parameter pairs may be, but is not limited to, as: , , in, f 1 is the first parameter pair. f 2 is the second parameter pair. f 3 is the third parameter pair. f 4 is the fourth parameter pair. f 5 is the fifth parameter pair. f 6 is the sixth parameter pair. A i For the first i The amplitude of each partial discharge pulse, t i For the first i The occurrence time of each partial discharge pulse, Δ t i For the first i The partial discharge pulse and the first i +1 partial discharge pulse time interval, Δ A i For the first i The partial discharge pulse and the first i +1 amplitude difference of partial discharge pulses, i This is the sequence number of the partial discharge pulse. N This represents the total number of partial discharge pulses.

[0067] In actual implementation, the embodiments of this application can use the amplitude of the partial discharge pulse, the time of occurrence, the time interval between adjacent pulses, and the difference in amplitude between adjacent pulses as the basis to construct 6 parameter pairs, namely the first parameter pair, the second parameter pair, the third parameter pair, the fourth parameter pair, the fifth parameter pair, and the sixth parameter pair.

[0068] It is understandable that, since the amplitude of a partial discharge pulse can be understood as the discharge intensity, the first parameter pair represents the current discharge intensity and the next discharge intensity, the second parameter pair represents the next intensity change and the current intensity change, the third parameter pair represents the current discharge interval and the next discharge interval, the fourth parameter pair represents the current discharge interval and the current intensity change, the fifth parameter pair represents the current discharge interval and the current discharge intensity, and the sixth parameter pair represents the next intensity change and the current discharge intensity.

[0069] This application embodiment constructs multiple parameter pairs, which can comprehensively capture the characteristics of partial discharge signals from multiple perspectives such as amplitude, timing, and pulse correlation, effectively improving the accuracy and robustness of insulation defect identification, avoiding misjudgment of defect type based on a single feature, and effectively enhancing the ability to distinguish different defect types.

[0070] In addition, the feature map can be understood as a visual distribution map formed by using parameter pairs as coordinate axes and mapping the probability kernel density of parameter pairs to the target space, which is used to reflect the overall distribution law of partial discharge pulses. For example, the feature map is a thermogram, and this application does not impose specific limitations.

[0071] Furthermore, a certain partial discharge feature fingerprint database can be understood as a set of standard feature maps corresponding to various typical GIS insulation defects established by those skilled in the art, used as a reference standard for identifying defect types from feature maps. The defect types may include, but are not limited to, metal particle defects, electrode protrusion defects, and surface scratch defects. This application does not impose any specific limitations.

[0072] The following section uses metal particle defects, electrode protrusion defects, and surface scratch defects in GIS as examples to introduce the construction process of a fingerprint database with certain partial discharge characteristics.

[0073] In this embodiment, a simulated cavity identical to the ±550 kV GIS busbar cavity structure in the laboratory was constructed. Using machining and assembly methods, three defect models were artificially constructed. The specifications of each defect model are as follows (simulating actual defect dimensions in the field): For metal particle defects, copper particles with a diameter of 0.5 mm and a length of 5 mm were selected and evenly placed on the surface of the simulated cavity busbar, with 3 particles in total, simulating the defect of residual metal foreign matter inside the GIS; for electrode protrusion defects, a hemispherical protrusion with a height of 1 mm and a diameter of 2 mm was machined on the surface of the electrode inside the simulated cavity, simulating the electric field distortion defect caused by insufficient electrode machining accuracy; for surface scratch defects, linear scratches with a length of 10 mm, a width of 0.2 mm, and a depth of 0.1 mm were sanded on the inner wall of the simulated cavity insulation, simulating mechanical damage defects during the transportation / installation of the insulation components. In addition, this embodiment sets up a defect-free blank control group to simulate the normal operation of the GIS.

[0074] In this embodiment, the same detection system as the actual measurement is used when deploying the experimental equipment. This system may include, but is not limited to, an optical sensor (spectral range 185 nm-850 nm, integrating PMT and counting gate circuit), an ultra-high frequency sensor (frequency band 300 MHz to 3 GHz), a data acquisition card (sampling rate 1 GS / s), and a host computer (equipped with host computer detection and analysis software). The optical sensor and the ultra-high frequency sensor are integrated and installed in the matching slot of the light-shielding observation window on the side of the simulation cavity to ensure effective docking between the sensor acquisition end and the inside of the simulation cavity. The acquisition card is connected to the sensor and the computer through optical fiber and data cable, respectively. The deployment method is completely consistent with the actual measurement.

[0075] In this embodiment, the laboratory ambient temperature is controlled at 25±2 ℃ and the humidity is controlled at 50±5% RH. External electromagnetic interference is shielded (electromagnetic shielding effectiveness ≥80 dB) to avoid the influence of environmental factors on signal acquisition and ensure the accuracy and repeatability of experimental data.

[0076] In this embodiment, 50 parallel experiments were conducted for each defect model to generate 50 sets (6 images per set) of feature maps. The probability kernel density distribution corresponding to each parameter pair in each set was statistically analyzed using the arithmetic mean method to eliminate the random error of a single experiment, thereby obtaining a standard feature map for each defect type (6 thermal maps corresponding to each defect set). The thermal maps of the blank control group were also statistically averaged in 10 experiments to obtain a standard feature map for the normal state. Thus, a certain partial discharge feature fingerprint database was constructed based on the standard feature maps of the defect types and the standard feature maps of the normal state.

[0077] In addition, the insulation defect detection results can be understood as the GIS defect type determination conclusion generated by matching the feature spectrum with a certain partial discharge feature fingerprint database, which is used to provide a basis for GIS fault early warning and maintenance decision-making.

[0078] In actual implementation, the embodiments of this application can perform statistical and mapping processing on UHF data to generate a feature spectrum composed of certain parameter pairs, transforming abstract data into intuitive and comparable distribution features. Then, the feature spectrum is used as an index to query a certain partial discharge feature fingerprint database. By comparing the feature spectrum with the standard feature spectrum of known defect types in the database, the insulation defect detection results of GIS can be obtained.

[0079] The following details how embodiments of this application generate feature maps with certain parameter pairs based on UHF data.

[0080] Specifically, in one embodiment of this application, generating a feature map of multiple parameter pairs based on UHF data includes: determining the amplitude and occurrence time of a partial discharge pulse based on the UHF data; determining the probability kernel density of multiple parameter pairs based on the amplitude and occurrence time of the partial discharge pulse; and mapping the probability kernel density to a target space to generate a feature map.

[0081] In the embodiments of this application, the probability kernel density can be understood as the probability density of the occurrence of parameter pairs of partial discharge pulses, which is used to reflect the statistical distribution law of parameter pairs.

[0082] The target space can be understood as a two-dimensional space defined by those skilled in the art to visualize the probability kernel density distribution; for example, the target space is a color space.

[0083] In actual implementation, as shown in Figure 3(a), the feature map of the first parameter pair is displayed; as shown in Figure 3(b), the feature map of the second parameter pair is displayed; as shown in Figure 3(c), the feature map of the third parameter pair is displayed; as shown in Figure 3(d), the feature map of the fourth parameter pair is displayed; as shown in Figure 3(e), the feature map of the fifth parameter pair is displayed; and as shown in Figure 3(f), the feature map of the sixth parameter pair is displayed. Specifically, in this embodiment, the amplitude and occurrence time of the local pulse sequence are determined based on the UHF data, and the amplitude difference and time interval are calculated based on the amplitude and occurrence time to generate 6 parameter pairs based on the amplitude, occurrence time, amplitude difference, and time interval. Then, the probability kernel density of each parameter pair is statistically calculated, and the probability kernel density is mapped to the color space (i.e., the target space) to draw the thermal map of the parameter pair as the feature map.

[0084] Based on the descriptions of other embodiments, exemplarily, embodiments of this application can use a host computer to perform visualization processing on UHF signals (for identifying effective components and interference components in the signal without changing the essence of the digital signal), then set a first amplitude threshold to filter out noise in the UHF signal with amplitudes lower than the first amplitude threshold, retaining effective signals with amplitudes higher than the first amplitude threshold, then discretizing the effective information to obtain UHF data, thereby setting a second amplitude threshold to identify peak points with amplitudes higher than the second amplitude threshold, determining the peak points as partial discharge pulses, and extracting the amplitude and occurrence time of the partial discharge pulses, thereby arranging all partial discharge pulses in chronological order of their occurrence times to obtain a partial discharge pulse sequence (e.g., pulse 1: A 1 = 5 t 1 = 0.1s, Pulse 2: A 2=7, t 2 = 0.3s, pulse 3: A 3=6, t 3 = 0.5s, etc.

[0085] This application embodiment determines the probability kernel density of multiple parameter pairs, converts discrete data into density information that reflects statistical laws, and maps the probability kernel density to the target space to generate a feature map. This can intuitively reflect the characteristics of insulation defects, improve the identification of features, and provide a reliable carrier for subsequent defect type identification.

[0086] like Figure 4 As shown below, the principle of the insulation defect detection method for ultra-high voltage DC GIS proposed in this application will be illustrated with a specific embodiment.

[0087] Step S401: Acquire the photon counting signal from the GIS.

[0088] In this embodiment, the light signal generated by electroluminescence and gas ionization emission inside the GIS can be captured by a photon counting probe. After electron avalanche multiplication by a high-voltage electrode, the weak light signal is converted into a current pulse. Then, the current pulse is converted into a digital pulse and noise is filtered out by a counting gate circuit to count the effective photons and output a photon counting signal.

[0089] Step S402: Collect UHF signals from the GIS.

[0090] In this embodiment, the UHF signal of the GIS can be collected by an UHF sensor when the photon counting signal undergoes a sudden change and the signal value is more than twice the absolute value of the dark mark value.

[0091] Step S403: Determine whether the photon counting signal meets certain early warning conditions.

[0092] In this embodiment, when the photon counting signal undergoes a sudden change, and the signal value (amplitude of the photon counting signal) is more than twice the absolute value of the dark mark value, and the duration is greater than 10 seconds, it is determined that the photon counting signal meets certain early warning conditions. At this time, preprocessing such as denoising and discretization of the UHF signal can be started to generate and save UHF data.

[0093] Step S404: Generate feature maps of multiple parameter pairs.

[0094] In this embodiment, the amplitude and occurrence time of the partial discharge pulse can be determined based on UHF data to obtain 6 parameter pairs. Then, the probability kernel density of the 6 parameter pairs is determined, and the probability kernel density is mapped to the target space to generate a feature map.

[0095] Step S405: Generate insulation defect detection results.

[0096] In this embodiment, the feature maps of multiple parameter pairs can be used as an index to query a certain partial discharge feature fingerprint database and compare it with the standard feature maps in the database to determine the insulation type, which is then used as the insulation defect detection result.

[0097] The insulation defect detection method for ultra-high voltage direct current GIS proposed in this application effectively enhances the ability to detect early, weak insulation defects by acquiring photon counting signals and ultra-high frequency signals. Furthermore, by determining whether the photon counting signal meets certain early warning conditions, potential insulation degradation events can be accurately identified, avoiding invalid detections. Subsequently, by denoising and discretizing the ultra-high frequency signals, noise interference is effectively reduced, improving the reliability of the ultra-high frequency data. Based on the ultra-high frequency data, feature maps of multiple parameter pairs are generated to obtain insulation defect detection results, enabling the determination of insulation defect types and improving the sensitivity and timeliness of GIS insulation defect detection. This solves the problem in related technologies where low electric field distortion caused by micro-defects or early-stage defects makes it difficult to detect GIS insulation degradation in a timely manner, resulting in insufficient detection sensitivity and timeliness.

[0098] Next, referring to the accompanying drawings, an insulation defect detection device for ultra-high voltage DC GIS proposed according to an embodiment of this application is described.

[0099] Figure 5 This is a block diagram of an insulation defect detection device for ultra-high voltage direct current GIS provided according to an embodiment of this application.

[0100] like Figure 5 As shown, the insulation defect detection device 50 of the ultra-high voltage DC GIS includes: a data acquisition module 100, a judgment module 200, and a detection module 300.

[0101] The acquisition module 100 is used to acquire photon counting signals and ultra-high frequency signals of gas-insulated metal-enclosed switchgear (GIS).

[0102] The judgment module 200 is used to determine whether the photon counting signal meets the preset warning conditions. If the photon counting signal meets the preset warning conditions, the UHF signal is denoised and discretized to obtain the corresponding UHF data.

[0103] The detection module 300 is used to generate feature maps of multiple parameter pairs based on UHF data, and to query a preset partial discharge feature fingerprint database using the feature maps as an index to obtain the insulation defect detection results of GIS.

[0104] Optionally, in one embodiment of this application, the acquisition module 100 includes: an acquisition unit, a first generation unit, and a second generation unit.

[0105] The acquisition unit is used to acquire the light signals generated by electroluminescence and gas ionization emission inside the GIS.

[0106] The first generation unit is used to perform avalanche multiplication on the optical signal to form a current pulse.

[0107] The second generation unit is used to obtain the photon counting signal based on the current pulse.

[0108] Optionally, in one embodiment of this application, the judgment module 200 includes a detection unit and a determination unit.

[0109] The detection unit is used to detect whether the amplitude of the photon counting signal is greater than a preset amplitude.

[0110] The determination unit is used to determine that the photon counting signal meets the preset warning conditions when the amplitude of the detected photon counting signal is greater than the preset amplitude and the duration is greater than the preset duration.

[0111] Optionally, in one embodiment of this application, the expression of the plurality of parameter pairs may be, but is not limited to, as: , , in, f 1 is the first parameter pair. f 2 is the second parameter pair. f 3 is the third parameter pair. f 4 is the fourth parameter pair. f 5 is the fifth parameter pair. f 6 is the sixth parameter pair. A i For the first i The amplitude of each partial discharge pulse, t i For the first i The occurrence time of each partial discharge pulse, Δ t i For the first i The partial discharge pulse and the first i +1 partial discharge pulse time interval, Δ A i For the first i The partial discharge pulse and the first i +1 amplitude difference of partial discharge pulses, i This is the sequence number of the partial discharge pulse. N This represents the total number of partial discharge pulses.

[0112] Optionally, in one embodiment of this application, the detection module 300 includes: a first determining unit, a second determining unit, and a third generating unit.

[0113] The first determining unit is used to determine the amplitude and occurrence time of the partial discharge pulse based on ultra-high frequency data.

[0114] The second determining unit is used to determine the probability kernel density of multiple parameter pairs based on the amplitude and occurrence time of the partial discharge pulse.

[0115] The third generation unit is used to map the probability kernel density to the target space to generate a feature map.

[0116] It should be noted that the explanation of the above-described embodiment of the insulation defect detection method for ultra-high voltage DC GIS also applies to the insulation defect detection device for ultra-high voltage DC GIS in this embodiment, and will not be repeated here.

[0117] The insulation defect detection device for ultra-high voltage direct current GIS proposed in this application effectively enhances the ability to detect early, weak insulation defects by acquiring photon counting signals and ultra-high frequency signals. Furthermore, by determining whether the photon counting signal meets certain early warning conditions, it can accurately pinpoint potential insulation degradation events, avoiding invalid detections. Subsequently, by denoising and discretizing the ultra-high frequency signals, noise interference is effectively reduced, improving the reliability of the ultra-high frequency data. Based on the ultra-high frequency data, feature maps of multiple parameter pairs are generated to obtain insulation defect detection results, enabling the determination of insulation defect types and improving the sensitivity and timeliness of GIS insulation defect detection. This solves the problem in related technologies where low electric field distortion caused by micro-defects or early-stage defects makes it difficult to detect GIS insulation degradation in a timely manner, resulting in insufficient detection sensitivity and timeliness.

[0118] Figure 6 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. The electronic device may include: The memory 601, the processor 602, and the computer program stored on the memory 601 and capable of running on the processor 602.

[0119] When the processor 602 executes the program, it implements the insulation defect detection method for ultra-high voltage DC GIS provided in the above embodiments.

[0120] Furthermore, electronic devices also include: Communication interface 603 is used for communication between memory 601 and processor 602.

[0121] The memory 601 is used to store computer programs that can run on the processor 602.

[0122] The memory 601 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0123] If the memory 601, processor 602, and communication interface 603 are implemented independently, then the communication interface 603, memory 601, and processor 602 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0124] Optionally, in a specific implementation, if the memory 601, processor 602, and communication interface 603 are integrated on a single chip, then the memory 601, processor 602, and communication interface 603 can communicate with each other through an internal interface.

[0125] The processor 602 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0126] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for detecting insulation defects in ultra-high voltage DC GIS.

[0127] This application also provides a computer program product, including a computer program that, when executed, implements the above-described method for detecting insulation defects in ultra-high voltage DC GIS.

[0128] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0129] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0130] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0131] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0132] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0133] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0134] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0135] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for detecting insulation defects in ultra-high voltage direct current GIS, characterized in that, Includes the following steps: Collect photon counting signals and ultra-high frequency signals from gas-insulated, metal-enclosed switchgear (GIS). Determine whether the photon counting signal meets the preset warning conditions. If the photon counting signal meets the preset warning conditions, then the UHF signal is denoised and discretized to obtain the corresponding UHF data. Based on the UHF data, feature maps of multiple parameter pairs are generated. Using the feature maps as indexes, a preset partial discharge feature fingerprint database is queried to obtain the insulation defect detection results of the GIS.

2. The method according to claim 1, characterized in that, The acquisition of photon counting signals from the GIS includes: Acquire the light signals generated by electroluminescence and gas ionization luminescence inside the GIS; The optical signal is subjected to avalanche multiplication to form a current pulse; The photon counting signal is obtained based on the current pulse.

3. The method according to claim 1, characterized in that, The step of determining whether the photon counting signal meets the preset warning conditions includes: Detect whether the amplitude of the photon counting signal is greater than a preset amplitude; If the amplitude of the photon counting signal is greater than the preset amplitude and the duration is greater than the preset duration, the photon counting signal is determined to meet the preset warning condition.

4. The method according to claim 1, characterized in that, The expressions for the plurality of parameter pairs are as follows: ， ， in, f 1 is the first parameter pair. f 2 is the second parameter pair. f 3 is the third parameter pair. f 4 is the fourth parameter pair. f 5 is the fifth parameter pair. f 6 is the sixth parameter pair. A i For the first i The amplitude of each partial discharge pulse, t i For the first i The occurrence time of each partial discharge pulse, Δ t i For the first i The partial discharge pulse and the first i +1 partial discharge pulse time interval, Δ A i For the first i The partial discharge pulse and the first i +1 amplitude difference of partial discharge pulses, i This is the sequence number of the partial discharge pulse. N This represents the total number of partial discharge pulses.

5. The method according to claim 1, characterized in that, The step of generating feature maps of multiple parameter pairs based on the UHF data includes: Based on the ultra-high frequency data, the amplitude and occurrence time of the partial discharge pulse are determined; The probability kernel density of the plurality of parameter pairs is determined based on the amplitude and occurrence time of the partial discharge pulse; The probability kernel density is mapped to the target space to generate the feature map.

6. An insulation defect detection device for ultra-high voltage direct current GIS, characterized in that, include: The acquisition module is used to acquire photon counting signals and ultra-high frequency signals from gas-insulated metal-enclosed switchgear (GIS). The judgment module is used to determine whether the photon counting signal meets the preset warning conditions. If the photon counting signal meets the preset warning conditions, the UHF signal is denoised and discretized to obtain the corresponding UHF data. The detection module is used to generate feature maps of multiple parameter pairs based on the UHF data, and to query a preset partial discharge feature fingerprint database using the feature maps as indexes to obtain the insulation defect detection results of the GIS.

7. The apparatus according to claim 6, characterized in that, The acquisition module includes: The acquisition unit is used to acquire the light signals generated by electroluminescence and gas ionization luminescence inside the GIS; The first generation unit is used to perform avalanche multiplication processing on the optical signal to form a current pulse; The second generation unit is used to obtain the photon counting signal based on the current pulse.

8. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and capable of running on the processor, the processor executing the program to implement the insulation defect detection method for ultra-high voltage direct current GIS as described in any one of claims 1-5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the insulation defect detection method for ultra-high voltage DC GIS as described in any one of claims 1-5.

10. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the insulation defect detection method for ultra-high voltage DC GIS as described in any one of claims 1-5.

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