Failure self-detection method of partial discharge detection system and partial discharge detection system

Abstract

The present disclosure provides a failure self-check method for a partial discharge detection system and a partial discharge detection system for failure self-check, the method comprising: obtaining electromagnetic environment simulation information in a substation to be monitored; obtaining information of a partial discharge sensor, a vibration sensor and a temperature sensor in the substation to be monitored, and constructing a sensor simulation model; iterating the following steps to perform multi-physical field coupling simulation to obtain a multi-physical field coupling simulation result: performing a heat conduction finite element simulation based on a temperature sensor simulation model; performing an electric field finite element simulation based on a partial discharge sensor simulation model and electromagnetic environment simulation information; performing a thermal stress finite element simulation based on a vibration sensor simulation model; performing a failure self-check based on the multi-physical field coupling simulation result to obtain a self-check result. Failure self-check is performed on the basis of comprehensively monitoring the partial discharge situation of the substation under a multi-physical field coupling environment, thereby improving the operational stability and safety of the substation.

Description

Technical Field

[0001] The present disclosure relates to the technical field of substation equipment status monitoring, and in particular to a failure self-detection method for a partial discharge detection system and a partial discharge detection system. Background Art

[0002] This section is intended to provide a background or context to the embodiments of the disclosure that are recited in the claims. No description herein is admitted to be prior art by inclusion in this section.

[0003] With the development of smart grid, substations, as key nodes for power transmission and distribution, the operating status of their equipment directly affects the stability and reliability of the power grid.

[0004] In a complex operating environment, substation equipment is often affected by the coupling of multiple physical fields such as electromagnetic interference, temperature changes, and mechanical stress. These factors may cause partial discharge, thereby accelerating equipment aging, damage, and even causing failures.

[0005] However, the partial discharge detection schemes in related technologies are often limited to a single physical field environment, and it is difficult to fully reflect the actual status of the equipment under complex working conditions. Summary of the invention

[0006] In view of this, the purpose of the present disclosure is to provide a failure self-detection method for a partial discharge detection system and a partial discharge detection system, so as to at least solve one of the technical problems in the related art to a certain extent.

[0007] Based on the above purpose, the first aspect of the exemplary embodiment of the present disclosure provides a failure self-detection method of a partial discharge detection system, wherein the partial discharge detection system is used to detect partial discharge information in a substation to be monitored, and the partial discharge detection system includes a partial discharge sensor, and the method includes:

[0008] Acquiring electromagnetic environment simulation information in the substation to be monitored;

[0009] Acquire the size information of the partial discharge sensor, vibration sensor and temperature sensor in the substation to be monitored and their location information in the substation to be monitored, and construct a partial discharge sensor simulation model, a vibration sensor simulation model and a temperature sensor simulation model;

[0010] Based on the electromagnetic environment simulation information, the partial discharge sensor simulation model, the vibration sensor simulation model and the temperature sensor simulation model, iterate the following steps to perform multi-physical field coupling simulation to obtain a multi-physical field coupling simulation result: perform heat conduction finite element simulation based on the temperature sensor simulation model; perform electric field finite element simulation based on the partial discharge sensor simulation model and the electromagnetic environment simulation information; perform thermal stress finite element simulation based on the vibration sensor simulation model;

[0011] Based on the multi-physics field coupling simulation result, a failure self-test is performed on the partial discharge detection system to obtain a self-test result of the partial discharge detection system.

[0012] Based on the same inventive concept, the second aspect of the exemplary embodiment of the present disclosure provides a partial discharge detection system, the partial discharge detection system is used to detect partial discharge information in a substation to be monitored, the system comprising:

[0013] The multi-physics field coupled partial discharge detection module is configured to obtain electromagnetic environment simulation information in the substation to be monitored; obtain size information of the partial discharge sensor, vibration sensor and temperature sensor in the substation to be monitored and position information in the substation to be monitored, and construct a partial discharge sensor simulation model, a vibration sensor simulation model and a temperature sensor simulation model;

[0014] The partial discharge detection signal analysis module is configured to iterate the following steps to perform multi-physical field coupling simulation based on the electromagnetic environment simulation information, the partial discharge sensor simulation model, the vibration sensor simulation model and the temperature sensor simulation model to obtain a multi-physical field coupling simulation result: perform a heat conduction finite element simulation based on the temperature sensor simulation model; perform an electric field finite element simulation based on the partial discharge sensor simulation model and the electromagnetic environment simulation information; and perform a thermal stress finite element simulation based on the vibration sensor simulation model.

[0015] A signal transmission module, configured to transmit signals in the partial discharge detection system;

[0016] The host display and analysis unit is configured to perform a failure self-test on the partial discharge detection system based on the multi-physics field coupling simulation result to obtain a self-test result of the partial discharge detection system.

[0017] It can be seen from the above that the failure self-check method and the partial discharge detection system of the partial discharge detection system provided by the embodiment of the present disclosure, the partial discharge detection system is used to detect partial discharge information in the substation to be monitored, the partial discharge detection system includes a partial discharge sensor, and the method includes: obtaining electromagnetic environment simulation information in the substation to be monitored; obtaining size information of the partial discharge sensor, vibration sensor and temperature sensor in the substation to be monitored and position information in the substation to be monitored, and constructing a partial discharge sensor simulation model, a vibration sensor simulation model and a temperature sensor simulation model; based on the electromagnetic environment simulation information, the partial discharge sensor simulation model, the vibration sensor simulation model and the temperature sensor simulation model, iterating the following steps to perform multi-physical field coupling simulation to obtain a multi-physical field coupling simulation result: performing heat conduction finite element simulation based on the temperature sensor simulation model; performing electric field finite element simulation based on the partial discharge sensor simulation model and the electromagnetic environment simulation information; performing thermal stress finite element simulation based on the vibration sensor simulation model; performing failure self-check on the partial discharge detection system based on the multi-physical field coupling simulation result to obtain a partial discharge detection system self-check result. Through the present disclosure, failure self-inspection can be performed on the basis of comprehensive monitoring of the partial discharge situation of the substation in a multi-physical field coupling environment, thereby improving the operational stability and safety of the substation. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present disclosure or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1a A schematic diagram of a current pulse waveform provided for an exemplary embodiment of the present disclosure;

[0020] Figure 1b A schematic diagram of ultra-high frequency partial discharge detection provided for an exemplary embodiment of the present disclosure;

[0021] Figure 2 A schematic diagram of partial discharge detection by an inductive sensor provided for an exemplary embodiment of the present disclosure;

[0022] Figure 3 A schematic diagram of ultrasonic partial discharge detection provided for an exemplary embodiment of the present disclosure;

[0023] Figure 4 A schematic diagram of partial discharge detection using a transient ground voltage method provided for an exemplary embodiment of the present disclosure;

[0024] Figure 5 A schematic diagram of the classification of failure mechanisms provided for exemplary embodiments of the present disclosure;

[0025] Figure 6 A schematic diagram of the structure of a partial discharge detection system provided for an exemplary embodiment of the present disclosure;

[0026] Figure 7 Another structural schematic diagram of a partial discharge detection system provided for an exemplary embodiment of the present disclosure;

[0027] Figure 8 A schematic flow chart of a failure self-check method for a partial discharge detection system provided by an exemplary embodiment of the present disclosure;

[0028] Fig. 9 A schematic diagram of a multi-physics coupling simulation provided for an exemplary embodiment of the present disclosure;

[0029] Fig.10 A schematic diagram of a temperature simulation provided for an exemplary embodiment of the present disclosure;

[0030] Fig.11 A schematic diagram of partial discharge signal conversion provided for an exemplary embodiment of the present disclosure;

[0031] Fig.12 A schematic diagram of anti-interference and noise reduction of partial discharge signals provided for an exemplary embodiment of the present disclosure;

[0032] Fig.13 A schematic diagram of the structure of a partial discharge detection signal analysis module provided by an exemplary embodiment of the present disclosure;

[0033] Fig.14 A schematic diagram of a radio frequency link provided for an exemplary embodiment of the present disclosure;

[0034] Fig.15 A schematic diagram of a BP neural network training method provided for an exemplary embodiment of the present disclosure;

[0035] Fig.16 A schematic diagram of partial discharge signal type identification provided for an exemplary embodiment of the present disclosure;

[0036] Fig.17 A schematic diagram of a failure mode framework provided for an exemplary embodiment of the present disclosure;

[0037] Fig.18 A schematic diagram of a fault tree of system-level failure modes provided for an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, the principles and spirit of the present disclosure will be described with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present disclosure, and are not intended to limit the scope of the present disclosure in any way. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0039] It should be understood herein that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction rather than having any limiting meaning.

[0040] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure should be understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the embodiments of the present disclosure do not represent any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing in front of the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connecting" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly. The article "one" or "a" before an element does not exclude the existence of multiple such elements.

[0041] The principle and spirit of the present disclosure are explained in detail below with reference to several representative embodiments of the present disclosure.

[0042] With the development of smart grids, substations are key nodes for power transmission and distribution, and the operating status of their equipment directly affects the stability and reliability of the power grid. In a complex operating environment, substation equipment is often affected by the coupling of multiple physical fields such as electromagnetic interference, temperature changes, and mechanical stress. These factors may cause partial discharge, which in turn accelerates equipment aging, damage, and even causes failures. The partial discharge detection methods in related technologies are often limited to a single physical field environment, and it is difficult to fully reflect the true state of the equipment under complex working conditions. Therefore, developing a partial discharge detection solution that can be applied to a multi-physical field coupling environment and has a failure self-detection function is of great significance to improving the operating safety and maintenance efficiency of substation equipment.

[0043] In the common equipment in the field of power transmission and distribution, insulation damage caused by factors such as equipment aging and production defects is one of the main causes of equipment shutdown and electrical accidents. Defects caused by insulation damage generally occur in areas where the insulation is relatively weak and the local electric field or magnetic field is too large, such as: bubbles, cracks, suspended metal particles, electrode burrs and other defects will appear in transformers, switchgear, GIS and high-voltage transmission cables. Defects gradually develop, and there are partial discharges (PD) problems in insulators. Partial discharge of insulators is an important sign of insulation degradation. The higher the degree of insulation degradation, the stronger the partial discharge. At the same time, partial discharge causes further degradation of the insulator, and its insulation dielectric properties may be seriously damaged. In the long run, it may eventually lead to insulation failure of electrical equipment. The insulator is likely to have through discharge and catastrophic accidents. Statistics show that nearly half of the switchgear burning accidents are caused by partial discharge induced by insulation defects. For example, there were 68 switchgear fire accidents, of which 36 were caused by partial discharge caused by insulation faults, accounting for 53%.

[0044] Partial discharge detection is simple to operate and efficient. It is the most applicable method in the diagnosis and fault assessment of insulation defects of primary equipment in substations in related technologies and has been widely used. From insulation damage to insulation failure, primary equipment generally undergoes a slow partial discharge process until insulation breakdown, insulation breakdown, flames, or even fires occur. Therefore, the purpose of equipment insulation defect detection and assessment can be achieved by detecting and analyzing partial discharge signals. Substation partial discharge sensors meet the partial discharge detection needs of the four most commonly used electrical equipment: GIS, high-voltage switchgear, various transformers, and high-voltage cables. They can detect the three main parameters of discharge amount, discharge time, and discharge frequency. In related technologies, ultra-high frequency antennas are often used to detect and receive ultra-high frequency (UHF) signals generated by partial discharge of transformers to achieve online monitoring of partial discharge faults of transformers. Ultra-high frequency detection is divided into ultra-high frequency narrowband detection and ultra-high frequency ultra-wideband detection. The former has a center frequency of more than 500MHz and a bandwidth of more than ten MHz or tens of MHz, while the latter has a bandwidth of several GHz. UHF and UWB detection technology has attracted people's attention due to its advantages such as high noise suppression ratio and more information contained. UHF detection technology usually refers to UHF and UWB detection. The sensors used for UHF partial discharge detection are mainly microstrips, and antenna sensors use microstrip antennas as sensors.

[0045] Partial discharge detection equipment was first developed abroad in 1919, and the Xilin bridge detection method was first applied to the field of partial discharge in 1924. In 1970, JUDD et al. first proposed the UHF detection method. Until today, UHF partial discharge detection technology has begun to mature, and the main manufacturers include MD, ABB, etc. However, due to the large differences in load and topology between my country's power grid and foreign power grids, the application effect of foreign partial discharge products in partial discharge detection in my country's power grid is not ideal. For example, the foreign corona discharge frequency is shown in the literature to be below 300MHz, while the corona discharge frequency in my country can reach 600MHz. Therefore, it is necessary to develop partial discharge online detection equipment suitable for my country's power system based on the domestic power grid environment.

[0046] The earliest research on partial discharge detection technology in China began in 1999. Major universities and power research institutions have invested a lot of manpower and material resources and have made great progress. In particular, a complete UHF partial discharge detection technology standard system was established in 2014, and various forms of partial discharge detection products began to appear. However, the substation working environment is a complex multi-physical field coupling scenario of electromagnetic, humidity, heat, equipment vibration, etc. The current detection technology standards are mostly aimed at the impact of a single variable on the sensor, and do not consider the reliability requirements in the substation coupling scenario. As a result, the electromagnetic compatibility and environmental weather resistance standards used as references for the production and manufacturing of existing sensors are no longer applicable to the complex multi-physical field coupling conditions of substations such as electromagnetic, humidity, heat, and equipment vibration, resulting in insufficient sensor reliability, and the need to improve protection and anti-interference performance.

[0047] Next, the partial discharge detection scheme in the related art will be introduced and the problems existing therein will be analyzed.

[0048] (1) Ultra-high frequency partial discharge detection technology:

[0049] Each partial discharge in the insulating medium of electrical equipment is accompanied by a very steep current pulse, such as Figure 1a As shown, it radiates electromagnetic waves to the surroundings.

[0050] refer to Figure 1b , the UHF electromagnetic waves radiated by partial discharge are received by UHF (300M-3GHz) sensors to realize the detection of partial discharge. This method is called the ultra-high frequency detection method of partial discharge.

[0051] The spectrum characteristics of the electromagnetic waves radiated by partial discharge are related to the geometry of the partial discharge source and the dielectric strength of the discharge gap. When the discharge gap is relatively small and the dielectric strength of the discharge gap is relatively high, the discharge process is relatively short, the steepness of the current pulse is relatively large, and the ultra-high frequency components of the radiated electromagnetic wave signal are relatively rich.

[0052] However, the disadvantages of UHF partial discharge detection technology are:

[0053] It is susceptible to UHF electromagnetic interference in the environment.

[0054] External sensors cannot detect fully metal-enclosed electrical equipment.

[0055] A quantitative description of the degree of defect degradation has not yet been achieved.

[0056] (2) High-frequency partial discharge detection technology:

[0057] High-frequency partial discharge detection method is one of the commonly used measurement methods for partial discharge defect detection and location of power equipment. Its detection frequency range is usually between 3MHz and 30MHz. High-frequency partial discharge detection technology can be widely used in partial discharge detection of power equipment such as power cables and their accessories, transformers, reactors, rotating motors, etc. Its high-frequency pulse current signal can be coupled by an inductive coupling sensor or a capacitive coupling sensor, or by a specially designed probe.

[0058] High-frequency partial discharge detection methods are mainly divided into capacitive sensors and inductive sensors according to the sensor type. Among the inductive sensors, the high-frequency current sensor (HFCT) has the advantages of strong portability, easy installation, and good anti-interference ability on site, so it is the most widely used. Its working method is to detect the discharge pulse current signal flowing through the ground wire, neutral point wiring and cable body of the power equipment. High-frequency current sensors mostly use Rogowski coil structure, such as Figure 2 shown.

[0059] However, the disadvantages of high frequency partial discharge detection technology are:

[0060] The installation method of high-frequency current sensors also limits the application scope of this detection technology.

[0061] The ability to resist electromagnetic interference is relatively weak.

[0062] (3) Ultrasonic partial discharge detection:

[0063] Electrical equipment generates sound waves during the discharge process. From the perspective of energy, discharge is a process of instantaneous energy burst, a process in which electrical energy is released in the form of sound energy, light energy, heat energy, electromagnetic energy, etc. When electrical breakdown occurs in the air gap, the discharge is completed instantaneously, and its electrical energy is instantly converted into heat energy, causing the expansion of the gas in the discharge center. The result of this instantaneous expansion is propagated in the form of sound waves, which is the initial sound source. As the initial sound waves propagate, the gas in the propagation area is heated to form an isothermal zone, whose temperature is higher than the ambient temperature. When these gases cool, they begin to contract again. The result of the contraction is a subsequent wave of lower frequency and intensity, which can be audible sound waves or ultrasonic waves. Ultrasonic waves refer to sound waves with a vibration frequency greater than 20KHz that cannot be heard or felt by people in a natural environment.

[0064] refer to Figure 3 The ultrasonic signal generated when partial discharge occurs in the high-voltage switch cabinet can be detected at the cabinet gap using a non-contact ultrasonic sensor, or it can be detected on the cabinet using a contact ultrasonic sensor.

[0065] However, ultrasonic testing technology has the following disadvantages:

[0066] Ultrasonic partial discharge detection technology is greatly affected by mechanical vibration. Since ultrasonic detection is a detection method based on mechanical waves, it is greatly affected by mechanical vibration.

[0067] Ultrasonic partial discharge detection technology has the disadvantages of great difficulty in identifying discharge type patterns and a small detection range.

[0068] Ultrasonic partial discharge detection technology is not sensitive to internal discharges in insulation.

[0069] (4) Partial discharge detection using transient ground voltage method:

[0070] When local discharge occurs in high-voltage electrical equipment, a magnetic field will be generated around the changing electric field, and the magnetic field generated by the discharge will propagate outward in the form of electromagnetic waves. Since the shielding layer will be discontinuous at the insulation part, gasket connection, cable insulation terminal and other parts due to molding and other reasons, the electromagnetic waves generated by the local discharge will propagate outside the metal shielding shell of the equipment through the discontinuities of these shielding bodies. After the capacitive sensor senses these electromagnetic waves, it will generate a certain transient voltage pulse signal to the ground.

[0071] refer to Figure 4In the related technology, most of the transient ground voltage detection equipment uses capacitive detectors to detect discharge pulses. When partial discharge occurs, the voltage and current pulse signals propagate along the inner surface of the metal shell of the test piece, and when encountering gaps such as openings and joints, they are transmitted out of the equipment, and then propagate along the outer surface of the metal shell to the ground. Its instantaneous voltage value varies in the range of several millivolts to several volts and exists for a very short time. After the discharge pulse is detected by the external capacitive detector, it enters the acquisition unit for processing after a series of amplification and filtering, and finally the discharge signal is analyzed by the test software.

[0072] However, the transient voltage detection technology has the following disadvantages:

[0073] Since transient voltage pulses must pass through the gaps between the metal shells of the equipment and be transmitted from the inner surface to the outer surface before they can be detected, this detection technology is not suitable for power equipment with completely sealed metal shells.

[0074] The discharge component is enclosed in a metal shell, and the sensor of the transient voltage detection device is difficult to penetrate into the interior of the switch device, so it is difficult to eliminate the influence of environmental electromagnetic noise during the detection process.

[0075] The transient ground voltage detection technology is not sensitive to surface discharge and insulator surface discharge.

[0076] Moreover, the current status of substation sensors is as follows:

[0077] With the rapid development of UHV voltage levels, the maximum limits of transient electric and magnetic fields in some areas of the substation site have exceeded the standard, the electromagnetic interference of the converter station is more serious, and the failure rate of localized electronic devices is high. The current standards can no longer guide the application, which brings challenges to the application of smart IoT sensors integrated with primary equipment. The digital construction of substations requires the deep integration of IoT sensors with the primary power system. However, the influencing factors of multi-physical field coupling near or inside and outside main equipment such as transformers and switches are not yet clear, which will bring high electromagnetic compatibility failure rate risks to IoT sensors, especially in transient electromagnetic environment conditions. The current grid voltage level has exceeded 1000kV, but domestic actual measurement research is insufficient. The electromagnetic compatibility and environmental weather resistance standards used as references for the production and manufacturing of existing sensors are no longer applicable to the complex electromagnetic, humidity, heat, equipment vibration and other multi-physical field coupling conditions of substations, resulting in inherent deficiencies in sensor reliability design. In the application process, it is affected by the coupling of multi-physical fields such as electromagnetic compatibility, and the failure rate is high, which restricts the industrial application of IoT sensors.

[0078] IoT sensors installed on and near the substation equipment still have poor long-term reliability and lifespan mismatch with power transmission and transformation equipment in the complex working conditions and electromagnetic environment of the substation. There is a lack of in-depth research on the failure mechanism of sensors in the complex environment of substations, the mechanism of transient electric and magnetic field conduction coupling to components is unclear, and there is a lack of evaluation methods for transient ground potential rise. The multi-level protection performance of typical sensors in substations of different voltage levels needs to be improved in terms of sensing materials, key microstructures, component processing technology and packaging methods.

[0079] With regard to existing domestic and foreign partial discharge detection products, there are defects such as uneven detection accuracy for different partial discharge types, weak adaptability, single product form, high product price, and weak resistance to industrial interference. This disclosure improves the life prediction ability of the sensor itself by analyzing the failure modeling under the influence of complex electromagnetic, humidity, heat, equipment vibration and other multi-physical field coupling in substations, and introduces the changed algorithm for noise filtering, signal type identification and high-precision detection to improve the environmental adaptability and reliability of partial discharge sensors. It aims to provide a detection solution that can comprehensively monitor the partial discharge of substation equipment in a multi-physical field coupling environment and has a failure self-detection function to improve the operating stability and safety of substation equipment.

[0080] The failure mechanism research mainly consists of two parts: theoretical research and experimental verification. The theoretical research part is carried out by establishing a failure physical model and simulating and analyzing the stress environment. Model building is to model the complex working environment of the sensor to achieve targeted research on the research problem; simulation is the process of solving the model. By establishing an appropriate physical model, adding model assumptions, material properties and boundary conditions to simulate the working conditions of the sensor in a strong impact environment, the failure mechanism is analyzed.

[0081] Compared with the reliability assessment technology based on statistics, failure physics is an analysis method that starts from the internal cause and generation mechanism of failure. It analyzes the relationship between failure mode, mechanism and influence by considering the specific environmental stress and application load of electronic components, and combines the reliability characterization model under the specific failure mechanism to determine the impact of the underlying components on the system reliability. By dividing the failure severity level and combining the failure frequency, the priority of the failure mechanism in the system under study is determined, and the critical faults of the system are defined. Among them, the failure mechanism can simultaneously reflect the failure process of electronic components. According to the failure characteristics caused by it, the failure mechanism with instantaneous failure characteristics is classified as overstress type, while the failure mechanism accompanied by continuous performance damage during the failure process is wear-out type, such as Figure 5 shown.

[0082] During the service life of components, they may be affected by various types of loads. When they are subjected to different types of loads, the induced failure mechanisms may have coupling effects, which to a certain extent increases the probability of component failure. When components are in service, the mechanical load is mainly physical shock or vibration load under environmental stress. The failure of components or systems under mechanical load may be manifested as elastic deformation, fracture, fatigue, etc. At the same time, affected by the environment and mission profile, the source of external heat load of components is temperature conduction of adjacent components, high and low temperature stresses imposed by the outside world during the mission execution phase, and alternating temperature stresses between mission phases. The key physical quantity for analyzing the influence of thermal load is the thermal expansion coefficient, which can reflect the changes in the internal dimensions and physical properties of components. In addition, the main sources of electrical loads include electrical shocks, current oscillations, etc., and the main failure causes caused by them are electromigration, voltage surface breakdown or dielectric breakdown. Electrical loads can also induce electrolytic corrosion inside components and generate leakage current.

[0083] Electronic components include devices and components. Devices include diodes, triodes, etc., and also transistor integrated circuits. Components include resistors, capacitors, inductors, etc. The failure of components refers to the phenomenon that electronic components cannot perform their original functions. The failure mode of electronic components is the manifestation of failure. For example, transistors have failure modes such as open circuit, short circuit and parameter drift. The main cause of failure is the occurrence of failure mechanisms such as hot carrier effect, gate oxide breakdown, electromigration, corrosion, etc. caused by various loads during service.

[0084] The research on failure mechanisms disclosed in this disclosure focuses on two aspects. On the one hand, a corresponding failure physical model is established based on the analysis of the failure mechanism, so that the life of components can be calculated, which serves as a prerequisite for quantitative reliability evaluation from the underlying component level to the system level; on the other hand, the interaction between multiple failure mechanisms is comprehensively considered to determine the coupling relationship between the failure mechanisms.

[0085] refer to Figure 6 , which is a structural schematic diagram of a partial discharge detection system provided by an exemplary embodiment of the present disclosure.

[0086] A partial discharge detection system, the partial discharge detection system is used to detect partial discharge information in a substation to be monitored, the system comprising:

[0087] The multi-physics field coupled partial discharge detection module 110 is configured to obtain electromagnetic environment simulation information in the substation to be monitored; obtain size information of the partial discharge sensor, vibration sensor and temperature sensor in the substation to be monitored and position information in the substation to be monitored, and construct a partial discharge sensor simulation model, a vibration sensor simulation model and a temperature sensor simulation model;

[0088] The partial discharge detection signal analysis module 120 is configured to iterate the following steps to perform multi-physical field coupling simulation based on the electromagnetic environment simulation information, the partial discharge sensor simulation model, the vibration sensor simulation model and the temperature sensor simulation model to obtain a multi-physical field coupling simulation result: perform a heat conduction finite element simulation based on the temperature sensor simulation model; perform an electric field finite element simulation based on the partial discharge sensor simulation model and the electromagnetic environment simulation information; and perform a thermal stress finite element simulation based on the vibration sensor simulation model.

[0089] A signal transmission module 130, configured to transmit signals in the partial discharge detection system;

[0090] The host display and analysis unit 140 is configured to perform a failure self-test on the partial discharge detection system based on the multi-physics field coupling simulation result to obtain a self-test result of the partial discharge detection system.

[0091] In specific implementation, the failure self-check partial discharge detection system includes four parts: host display and analysis unit, signal transmission module, partial discharge detection signal analysis module, and multi-physics field coupling partial discharge detection module. The host display and analysis unit is used to receive and process the transmitted data, perform multi-physics field simulation, sensor failure analysis self-check, partial discharge image display and sensor action feedback; the signal transmission module is used to perform protocol adaptation, signal conversion and transmission of various processed sensor signals; the partial discharge detection signal analysis module is used to convert various types of partial discharge signals, including signal filtering, amplification, etc.; the multi-physics field coupling partial discharge detection module integrates various sensors to realize different types of sensor signal acquisition.

[0092] It should be noted that, for the convenience of description, the above device is described by dividing it into various modules according to its functions. Of course, when implementing the present disclosure, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0093] refer to Figure 7 , which is a more detailed structural schematic diagram of the partial discharge detection system provided by the exemplary embodiment of the present disclosure.

[0094] Multi-physics field coupled partial discharge detection module: It consists of a main sensor and an environmental stress sensor. The main sensor is a partial discharge sensor, which is used to capture the partial discharge signal of the equipment in a simulated environment. The sensor is arranged at key parts of the equipment to ensure that possible partial discharge areas can be fully covered. The environmental stress sensor is a vibration sensor and a temperature sensor, which is used to monitor the working status of the equipment at different temperatures during operation and the mechanical loads it may be subjected to.

[0095] Partial discharge detection signal analysis module: According to the use scenario of the partial discharge sensor, the ultra-high frequency / high frequency / ultrasonic / transient ground voltage detection method is used to amplify, filter, detect and process the collected partial discharge signals to identify the type of partial discharge fault, increase signal recognition, and thus improve detection sensitivity and anti-interference ability.

[0096] Signal transmission module: Use communication technologies such as Bluetooth or 4G wireless to complete transmission protocol conversion, realize real-time transmission of detection data, and improve data transmission efficiency and deployment flexibility.

[0097] Host display and analysis unit: Receive and process the transmitted data, analyze the partial discharge phase spectrum, pulse sequence phase spectrum, etc., set the failure self-detection algorithm according to the sensor simulation results, and determine whether the equipment is in a failure state according to the analysis results. It also has data storage, reading, visualization display and sensor clock synchronization control functions.

[0098] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment systems, the present disclosure further provides a failure self-detection method for a partial discharge detection system.

[0099] Under the influence of multiple physical fields in substations (such as electromagnetic fields, mechanical vibrations, temperature changes, etc.), the self-test method of partial discharge (PD) sensors needs to comprehensively consider the interference of these external factors to ensure the accuracy and reliability of the detection results. The following is the design of the self-test method, which aims to evaluate the working status of partial discharge sensors in the complex environment of substations through a series of automated test steps, including their sensitivity, anti-interference ability, positioning accuracy, etc. The self-test process should be carried out without affecting the normal operation of the substation and simulate the actual operating conditions as much as possible.

[0100] refer to Figure 8 , which is a flow chart of a failure self-detection method for a partial discharge detection system provided by an exemplary embodiment of the present disclosure.

[0101] A failure self-check method for a partial discharge detection system, wherein the partial discharge detection system is used to detect partial discharge information in a substation to be monitored, wherein the partial discharge detection system includes a partial discharge sensor, and the method includes:

[0102] Step S210: Acquire electromagnetic environment simulation information in the substation to be monitored.

[0103] Step S220: Obtain size information of the partial discharge sensor, vibration sensor and temperature sensor in the substation to be monitored and position information in the substation to be monitored, and construct a partial discharge sensor simulation model, a vibration sensor simulation model and a temperature sensor simulation model.

[0104] During specific implementation, other environmental stress sensors in addition to the vibration sensor and the temperature sensor may also be configured.

[0105] Step S230, based on the electromagnetic environment simulation information, the partial discharge sensor simulation model, the vibration sensor simulation model and the temperature sensor simulation model, iterate the following steps to perform multi-physical field coupling simulation to obtain multi-physical field coupling simulation results: perform thermal conduction finite element simulation based on the temperature sensor simulation model; perform electric field finite element simulation based on the partial discharge sensor simulation model and the electromagnetic environment simulation information; perform thermal stress finite element simulation based on the vibration sensor simulation model.

[0106] In this exemplary embodiment, the heat conduction finite element simulation based on the temperature sensor simulation model includes:

[0107] Adding material parameters to the temperature sensor simulation model;

[0108] Meshing the temperature sensor simulation model;

[0109] Adding a temperature load and boundary conditions corresponding to the temperature load to the temperature sensor simulation model;

[0110] The temperature field distribution information of the temperature sensor at the position in the substation to be monitored is determined as the temperature simulation result.

[0111] In this exemplary embodiment, the thermal stress finite element simulation based on the vibration sensor simulation model includes:

[0112] Adding material parameters to the vibration sensor simulation model;

[0113] acquiring a vibration condition, and determining a vibration function based on the vibration condition;

[0114] Meshing the vibration sensor simulation model;

[0115] Adding boundary conditions to the vibration sensor simulation model;

[0116] The vibration stress distribution information of the vibration sensor in the substation to be monitored is determined as the vibration simulation result.

[0117] refer to Fig. 9 , in specific implementation, first generate and calibrate the reference signal:

[0118] Step 1: During the substation shutdown or low load period, use a partial discharge source with known characteristics (such as a pulse current source or an ultrasonic source) as a reference signal source.

[0119] Step 2: Place the reference signal source near the sensor and adjust its output to cover the sensor's detection range.

[0120] Step 3: Record the sensor's response to the reference signal, including signal strength, waveform characteristics, etc., as a reference for subsequent self-tests.

[0121] When implemented, electromagnetic interference testing includes:

[0122] Step 4: Turn on other electrical equipment in the substation to simulate the electromagnetic environment in actual operation.

[0123] Step 5: Use an EMI generator to generate EMI of different frequencies and intensities around the sensor.

[0124] Step 6: Use the finite element method to analyze the electromagnetic field stress characteristics at the sensor installation location. First, build a physical simulation model of the sensor based on the actual structural dimensions; then define the material properties of each part of the sensor, and mesh each part, dividing the overall trend to be solved into a finite number of small networks; finally, set boundary conditions, and set and solve the electromagnetic environment parameters under the action of high-frequency transient electromagnetic fields, transient ground potential rise, and steady-state electromagnetic interference; combine the simulation results to analyze the coupling mechanism of high-frequency transient electric fields, magnetic fields, transient ground potential rise, and other electromagnetic phenomena.

[0125] When implemented, mechanical vibration testing includes:

[0126] In order to improve the simulation efficiency, the transformer can be simplified, ignoring the unimportant auxiliary structures inside the transformer, and retaining the four core components of three-phase winding, core, core clamp and insulating pad. Combined with the installation position of the sensor, the vibration of the core, core clamp and box during the operation of the transformer is mainly studied.

[0127] Firstly, a simulation model is established according to the structural dimensions of the sensor, and the model is simplified and assumed, small structures such as leads are ignored, and all parts are assumed to be rigid bodies. The deformation characteristics of the material are not considered in the simulation process. The material parameters of the sensor are set, and the vibration function is defined according to the obtained vibration conditions. The physical model is then meshed and relevant boundary conditions are set. Finally, the model is solved and calculated to obtain the internal vibration stress distribution of the sensor under the normal working environment.

[0128] When implemented, the temperature change test includes:

[0129] The main reasons for the heating of the sensor are the heating of the power device at the location and the energy consumption of the sensor itself. This part of the heat is distributed throughout the device through heat conduction and other heat transfer methods. The main forms of sensor heat dissipation are: heat conduction, thermal radiation, and thermal convection.

[0130] In the heat transfer of sensor devices, heat conduction is determined by the internal materials of the device, heat radiation can be ignored, and heat convection is determined by the heat dissipation performance. The heat conduction and heat convection parameters jointly determine the boundary of the finite element simulation model.

[0131] The thermal environment includes high temperature constant temperature and high and low temperature shock environment. Based on the methods and processes of relevant test tests and combined with the corresponding test standards of the sensor, the load and boundary condition application methods and processes of the temperature environment are clarified. First, according to the actual size of the sensor and its location in the substation, a simulation environment model of the sensor and the substation surrounding is constructed; then, corresponding parameters are added to the materials in various places in the physical model, such as relative dielectric constant, thermal conductivity, relative magnetic permeability, etc.; then, the various parts of the physical model are meshed, and the type and size of the mesh are important factors affecting the calculation results; consider the physical fields applicable to each part and apply them one by one, and set the corresponding boundary conditions; obtain the temperature field distribution at the sensor installation location through calculation.

[0132] refer to Fig.10 , where temperature simulation includes the following processes:

[0133] Geometric modeling;

[0134] Import material parameters, set mesh, apply temperature loads, and set boundary conditions;

[0135] Complete thermal analysis of temperature field simulation and import the results into the structural field;

[0136] Structural field simulation settings, including total simulation steps, minimum subsection, nonlinear convergence, etc.;

[0137] Complete structural field simulation analysis;

[0138] Determine whether the structural field is converged;

[0139] Yes, extract simulation results and complete data post-processing

[0140] No, complete the structural field simulation analysis.

[0141] During specific implementation, comprehensive performance testing includes:

[0142] Under actual working conditions, comprehensive environmental stress analysis is often difficult due to the existence of expensive sensor samples, difficulty in accurately loading multiple environmental loads, difficulty in simulating the combined effects of environmental stresses, long environmental testing time, and peripheral signal pickup deviations. The use of three-dimensional finite element simulation can solve the problem of device stress analysis, and combined with the above test results, a comprehensive performance evaluation of the sensor can be performed.

[0143] When the transformer and sensor are working, the power consumption will cause the device to heat up. Due to the different thermal expansion coefficients between the layers of materials, thermal stress will be generated between the layers of the device under the action of the temperature field. According to the heat transfer finite element theory, the heat conduction finite element equation is:

[0144] ;

[0145] In the formula, is the heat capacity matrix, is the heat conduction matrix, is the node temperature, is the time-variant node temperature vector, is the nodal heat flux vector, is the facing heat flux vector, is the heat flux load vector, is the heat source heat generation vector, the temperature vector is the sum of all heat transfer vectors.

[0146] The electric field finite element equation satisfies the following formula:

[0147] ;

[0148] in, is the conductivity matrix, is the node voltage, is the current load.

[0149] Temperature changes cause changes in resistance, thermal conductivity, and specific heat capacity, which results in thermal-electric coupling. It can be calculated according to the electric field equation:

[0150] ;

[0151] In the formula is the shape function matrix, is the potential gradient vector matrix, is the electrical conductivity. The finite element equation for thermal stress is:

[0152] ;

[0153] In the formula, is the unit node force matrix, is the node displacement, is the node temperature load. Because there is coupling between heat conduction, electrical conduction and thermal stress, the multi-field coupling results can be obtained by repeatedly iterating the above formula.

[0154] The multi-physics coupling simulation process is as follows: first, build a circuit simulation model to solve the device characteristics and surrounding heat; then use the heat power consumption and heat flux for the thermal-mechanical coupling of the finite element model; finally, obtain the thermal and force field distribution results. Among them, the finite element model simulation includes the following steps: 3D model construction, material property input, contact setting, meshing, thermal field solution, and thermal-mechanical field coupling analysis.

[0155] The above environmental simulation is completed as the data basis for the self-test of the partial discharge detection system. The self-test process simulates the actual operating conditions as much as possible to improve the reliability of the test results. Through the above steps, a comprehensive self-test of the partial discharge sensor under the influence of the multi-physical field of the substation can be achieved to ensure its accuracy and reliability in actual operation.

[0156] In this exemplary embodiment, the electric field finite element simulation based on the partial discharge sensor simulation model and the electromagnetic environment simulation information includes:

[0157] Converting a partial discharge signal obtained from the electromagnetic environment simulation information based on the partial discharge sensor simulation model to obtain a converted partial discharge signal;

[0158] The electric field finite element simulation is performed based on the converted partial discharge signal.

[0159] In this exemplary embodiment, the converting and processing the partial discharge signal acquired from the electromagnetic environment simulation information based on the partial discharge sensor simulation model to obtain the converted partial discharge signal includes:

[0160] Performing gain processing on the partial discharge signal to obtain a gained partial discharge signal;

[0161] Performing bandpass filtering on the gained partial discharge signal to obtain a bandpass filtered partial discharge signal;

[0162] Performing orthogonal mixing processing on the partial discharge signal after the bandpass filtering to obtain a partial discharge signal after orthogonal mixing;

[0163] Performing anti-aliasing filtering on the partial discharge signal after the orthogonal mixing to obtain a partial discharge signal after anti-aliasing filtering;

[0164] Performing analog-to-digital processing on the partial discharge signal after the anti-aliasing filtering to obtain the partial discharge signal in the form of a digital signal;

[0165] Anti-interference and noise reduction processing is performed on the partial discharge signal in the form of a digital signal to obtain the converted partial discharge signal.

[0166] In this exemplary embodiment, the step of performing anti-interference and noise reduction processing on the partial discharge signal in the form of a digital signal to obtain the converted partial discharge signal includes:

[0167] Performing empirical mode decomposition on the partial discharge signal in the form of a digital signal to obtain an intrinsic mode function waveform;

[0168] Filtering the intrinsic mode function waveforms that are less than a threshold value among the intrinsic mode function waveforms to obtain filtered intrinsic mode function waveforms;

[0169] The filtered intrinsic mode function waveform is subjected to Hilbert transformation to obtain the transformed partial discharge signal.

[0170] In the specific implementation, this part is mainly reflected in two aspects: signal detection and ADC sampling technology. For the anti-interference problem of signal detection, the HHT (Hilbert-Huang Transform) transform method is adopted. As a transform method suitable for extracting the characteristic values ​​of partial discharge non-stationary signals, it has high anti-interference and noise reduction capabilities. For the high bandwidth problem of partial discharge signals, an ADC sampling technology with high sampling speed and high dynamic range is proposed, which can not only extend the detection bandwidth of partial discharge signals, but also detect weak power signals.

[0171] refer to Fig.11 , in specific implementation, the partial discharge detection signal analysis process includes:

[0172] The overall architecture of the substation partial discharge online monitoring system is simplified to sensor antenna, main processing platform, and display host. However, different modules inside the processing unit need to be hierarchical. The received signal passes through the antenna, gain amplifier matrix, amplification range of 20-35dB, bandpass filter, bandpass range of 300-1500MHz, and then the orthogonal mixer, which multiplies the partial discharge receiving signal and the local oscillator signal by two orthogonal local oscillator carrier signals to obtain two orthogonal output signals I and Q. Then there is a 0-35MHz anti-aliasing filter, and then through ADC sampling at a sampling rate of 80MHz, the analog signal is converted into a digital signal and enters the digital processor. When the main processor needs to change the local oscillator frequency, the processor's FPGASPI interface controls the local oscillator VCO chip to generate different local oscillator frequencies to cover the entire receiving frequency band. In addition, when the gain amplifier circuit needs to change the gain multiple, the switching signal of the processor FPGA is also used to change the amplifier multiple to adapt to partial discharge signals of different intensities.

[0173] Finally, the received signal enters the processor, and the FPGA completes the digital I and Q merging, receiving triggering, merging of different frequency bands, HHT conversion, calculation of discharge times N, calculation of discharge amount Q, and accurate conversion of phase value. After all processing is completed, the processed partial discharge signal is sent to the display unit pC through the Ethernet port, and the partial discharge signal is displayed by the display unit software. The display host is a set of software display interface running on a PC.

[0174] The partial discharge sensing antenna selects UHF (300M-3GHz) ultra-high frequency sensors or high frequency sensors (capacitive sensors and inductive sensors) according to different working conditions to adapt to specific environments such as substation switch cabinets (ultrasonic + transient ground voltage), GIS (ultra-high frequency), transformers (ultra-high frequency / high frequency / ultrasonic), cables (high frequency), etc.

[0175] When the partial discharge signal propagates to the partial discharge sensor antenna, it is a weak wireless signal. In order to extract this weak signal, the processing unit needs an ADC chip with a high dynamic range. Some partial discharge detection products in related technologies use 22-bit ADCs, which raises the cost of partial discharge products. Using signal processing technology to reduce the sampling frequency requirements for partial discharge signal sampling is the key to realizing ultra-high frequency / high frequency partial discharge signal detection. A logarithmic detection module is used in the analog front end of partial discharge to filter out the high-frequency carrier in the partial discharge signal and only retain the time domain envelope of the partial discharge signal, which compresses the dynamic range of the signal and can greatly reduce the sampling frequency of the ADC. According to theoretical calculations, after using the logarithmic detection module, a 12-bit ADC chip with a bit width can be used for front-end analog sampling, reducing equipment costs and improving the sensitivity of partial discharge signal detection.

[0176] The sensor receives the signal and enters the host after passing through the ADC. Noise reduction is the core technology to achieve high sensitivity of partial discharge signals. Partial discharge signals are typical non-stationary signals. The fast Fourier transform (FFT) threshold denoising and interference in related technologies are no longer applicable, and wavelet transform is also difficult to apply because it is difficult to obtain suitable basis functions for partial discharge signals. The HHT transform algorithm can extract the characteristic value IMF function of the interference signal from industrial interference and noise, and calculate the noise threshold of each IMF, return all noise below the threshold to zero, and directly eliminate the confirmed narrowband interference signal. The HHT transform is an efficient algorithm for anti-interference and noise reduction of partial discharge signals.

[0177] refer to Fig.12 ,When implemented specifically, the HHT transformation implementation process includes:

[0178] Input signal;

[0179] Perform EMD (Empirical Mode Decomposition) to obtain IMF1;

[0180] Determine whether IMF1 is a valid high-frequency component;

[0181] If yes, calculate the amplitude of added white noise and the overall average number of times;

[0182] Perform EEMD (Ensemble Empirical Mode Decomposition);

[0183] Determine whether the requirements are met;

[0184] If it is not satisfied, after setting the expected decomposition error, calculate the amplitude of added white noise and the overall average number of times;

[0185] If satisfied, perform energy estimation and wavelet transform multi-scale decomposition on IMFs, and calculate RP j , if RP j ≥1, jump to the Hilbert transform to find the disturbance start time, if RP j <1, calculate RP j ;

[0186] Decomposition scale threshold quantization processing;

[0187] Inverse wavelet transform to reconstruct the signal;

[0188] Use Hilbert transform to find the starting time of the disturbance;

[0189] Output the result.

[0190] In specific implementation, the HHT transformation steps are:

[0191] 1) Determine all extreme values ​​of the time series x(t);

[0192] 2) Perform cubic interpolation on the maximum value sequence and the minimum value sequence to obtain the upper envelope u(t) and lower envelope v(t) of x(t);

[0193] 3) Calculate the mean of the upper and lower envelope signals;

[0194] M(t)=[u(t)+v(t)] / 2;

[0195] 4) Calculate the detail signal d(t):

[0196] d(t)=x(t)-m(t);

[0197] If d(t) satisfies the two conditions of modal function: <1> The difference between the number of extreme points and zero-crossing points is no more than 1. <2> The mean value of the kth point x(k)=0;

[0198] Then d(t) is the intrinsic mode function IMF of x(t).

[0199] The hardware part of the partial discharge detection signal analysis module includes three parts: RF channel, mixer and main processor.

[0200] like Fig.13 As shown, the processing unit consists of: low noise amplifier (amplification 20dB), bandpass filter (300M-1500MHz), orthogonal mixer (cosine and sinine two-way orthogonal local oscillator signal), anti-aliasing filter (0-35MHz), ADC (ad9635, 80MHz sampling frequency), main processor (Xilinxxz7c015). The RF link block diagram is as follows Fig.14 shown.

[0201] The signal transmission module is an efficient signal transmission module for the partial discharge self-test system. It uses chips such as a digital signal processor (DSP) or a field programmable gate array (FPGA) to filter, amplify and encode the collected digital signals. The specific implementation of the adaptive filtering algorithm and the digital signal amplification technology can be selected and optimized according to actual needs. It has the advantages of fast transmission speed, strong anti-interference ability and high reliability. At the same time, the transmission unit uses wireless transmission technologies such as Bluetooth, Wi-Fi or Zigbee to send the processed signal to the host display and analysis unit of the partial discharge self-test system. During the transmission process, the efficient transmission protocol and automatic retransmission mechanism designed by the present disclosure are adopted to ensure accurate and reliable transmission of the signal.

[0202] Step S240: performing a failure self-test on the partial discharge detection system based on the multi-physics field coupling simulation result to obtain a partial discharge detection system self-test result.

[0203] In this exemplary embodiment, the performing a failure self-test on the partial discharge detection system based on the multi-physics field coupling simulation result to obtain a self-test result of the partial discharge detection system includes:

[0204] Performing partial discharge signal type identification based on the multi-physics field coupling simulation result to obtain the partial discharge type corresponding to the multi-physics field coupling simulation result;

[0205] Based on the partial discharge type and the multi-physics field coupling simulation result, a failure self-test is performed on the partial discharge detection system to obtain a partial discharge detection system self-test result.

[0206] In this exemplary embodiment, the identifying of the type of partial discharge signal based on the multi-physics field coupling simulation result to obtain the type of partial discharge corresponding to the multi-physics field coupling simulation result includes:

[0207] Inputting the partial discharge signal into a pre-trained partial discharge type recognition model to obtain a partial discharge type corresponding to the partial discharge sensor output by the partial discharge type recognition model;

[0208] The training method of the partial discharge type recognition model includes:

[0209] Collect known partial discharge signals corresponding to the partial discharge defect type;

[0210] Building a partial discharge information library based on the known partial discharge signal and the partial discharge defect type corresponding to the known partial discharge signal;

[0211] Constructing a partial discharge type recognition model to be trained;

[0212] The partial discharge information library is used as an input to train the partial discharge type recognition model to be trained.

[0213] In this exemplary embodiment, the performing a failure self-test on the partial discharge detection system based on the partial discharge type and the multi-physics field coupling simulation result to obtain a partial discharge detection system self-test result includes:

[0214] Determining functional modules in the partial discharge detection system;

[0215] Performing a failure self-test on the partial discharge detection system to obtain a self-test result of the partial discharge detection system, determining the working state of the functional module, and constructing a failure logic relationship diagram;

[0216] Based on the failure logic relationship diagram, construct a failure mode fault tree;

[0217] Based on the failure mode fault tree, a failure self-test is performed on the partial discharge detection system to obtain a self-test result of the partial discharge detection system.

[0218] In the specific implementation, the self-test unit-partial discharge type identification is analyzed: there are many types of partial discharge, and the different discharge types of these different devices need to be distinguished by the neural network algorithm, which is suitable for the discrimination of different discharge types of different high-voltage equipment. The error back propagation algorithm (Error Back-propagation Training, referred to as BP network) can approximate any continuous function, has a strong nonlinear mapping ability, and the parameters such as the number of intermediate layers of the network, the number of processing units in each layer, and the learning coefficient of the network can be set according to the specific situation. It is very flexible, so it plays an important role in many application fields. It is composed of a multi-layer feedforward network and is trained using the error reversal propagation algorithm, so that it has a strong learning ability. In the pattern recognition classifier network structure, it mainly calls the input / output mapping relationship stored inside. This mapping relationship is written into the structure in advance without the need to describe it again. In terms of learning rules, it is mainly to pass the signal through the two processes of propagation and error back propagation. After continuous learning, the steepest descent method is used to obtain the best solution, thereby obtaining the minimum error square sum of the neural network.

[0219] The BP neural network is divided into three parts, including the input layer, hidden layer and output layer. The eigenvalue data (the environmental simulation results output by the multi-physics field coupled partial discharge detection module) is input into the input layer. The dimension of the data determines the number of neurons in the input layer. The number of input data dimensions is the number of neurons in the input layer. The number of hidden layers is generally adjusted during training. Generally speaking, when training a neural network, the number of neurons in the hidden layer needs to be adjusted continuously until the optimal number of layers is obtained. The number of neurons in the output layer is determined by the number of categories that need to be identified, that is, the number of output layers to be set.

[0220] For the randomly generated initial values ​​of weights, there must be an error between the actual response output and the expected output. The artificial neural network continuously learns the training samples and adjusts the weights to gradually reduce the error. When the error reaches the specified level, the network learning process ends.

[0221] refer to Fig.15 , the BP neural network training process is as follows:

[0222] Step 1: Initialization;

[0223] Step 2: Set network parameters and provide learning samples;

[0224] Step 3: Calculate the input value and output value of the middle layer;

[0225] Step 4: Calculate the input and output values ​​of each unit in the output layer;

[0226] Step 5: Calculate the generalized error of each neuron in the output layer;

[0227] Step 6: Calculate the generalized error of each neuron in the middle layer;

[0228] Step 7: Correct the connection parameter value of the output layer;

[0229] Step 8: Adjust the connection weights between the intermediate layer and the output layer and the thresholds of each unit in the output layer;

[0230] Step 9: Adjust the connection weights between the input layer and the middle layer and the thresholds of each unit in the output layer;

[0231] Step 10: Update learning mode;

[0232] Step 11: Determine whether all training is completed. If completed, update the number of learning times; if not completed, return to step 2;

[0233] Step 12: Update the number of learning times;

[0234] Step 13: Determine whether to stop the calculation based on the error value and the number of iterations. If not, return to step 2.

[0235] The specific steps to implement the BP neural network are:

[0236] 1) Collect partial discharge signals with known partial discharge defect types;

[0237] 2) Establish a partial discharge information database, which includes partial discharge waveform signals of several defect types and corresponding defect types;

[0238] 3) Build a neural network BP for pattern recognition;

[0239] 4) Using the waveform and corresponding defect type in the partial discharge information library as input, training the convolutional neural network;

[0240] 5) Collect multiple partial discharge signals of the defect type to be determined (environmental simulation results output by the multi-physics field coupled partial discharge detection module);

[0241] 6) The partial discharge waveform data of the undetermined defect type is input into the trained convolutional neural network, and the defect type with the largest proportion in the output is taken as the final defect type.

[0242] The four parameters of the input sample, namely, the discharge amount, equivalent rise time, equivalent half-wave time and main discharge frequency band, are used as characteristic values; there are X intermediate hidden layers; there are m output layers, corresponding to m types of discharges.

[0243] The local discharge signal type recognition based on BP neural network algorithm includes three steps: BP neural network construction, BP neural network training and BP neural network classification.

[0244] refer to Fig.16 , the specific process is as follows:

[0245] BP neural network construction includes system modeling and building a suitable BP neural network;

[0246] BP neural network training includes BP neural network initialization, BP neural network training until the end of training;

[0247] BP neural network classification includes BP neural network classification of test data.

[0248] During the specific implementation, analyze the self-test unit-failure analysis:

[0249] Fault tree analysis (also known as FTA) is a method of using logical reasoning to make reliability judgments under specified conditions. It breaks down the causes of system failures step by step from the overall (system) to the parts (units) in a tree-like manner, and analyzes them layer by layer until the bottom layer of the system, thereby finding all the causes of failures and drawing a logical structure diagram of the failures.

[0250] The electronic equipment of power sensors is generally divided into four functional modules, namely sensor module, filter module, control board, and power module. The specific functions of each module are shown in the following table (function table of power sensor component modules). The normal working state of these functional modules is an important prerequisite for ensuring the safe operation of power equipment.

[0251] Function table of power sensor module components

[0252]

[0253] refer to Fig.17 In order to more intuitively show the relationship between various failure modes, the reliability block diagram is visualized, and the symbols of failure modes at each level correspond to the following Fig.17 As shown, the failure rates of X1, X2, ..., X9 representing the underlying failure modes are q1, q2, q3, ..., q9 respectively.

[0254] refer to Fig.18In order to accurately locate the cause of the fault from the system level to each unit and component level, a failure logic relationship diagram connecting each level is established. At the same time, a logical premise is provided for establishing the failure rate function from the component level, unit level to the system level. A fault tree of sensor detection function degradation (failure mode a) is established to intuitively reflect the relationship between various failure modes in the actual circuit, such as the "or" relationship between failure mode X1, failure mode X2, and failure mode X3, that is, the failure of failure mode b1 is determined by the competition between X1, X2, and X3.

[0255] It can also reflect the cut-set unit of the analyzed circuit system, that is, the minimum set unit that causes the system to fail. Fig.18 It can be seen that the failure modes from the top logic gate to the bottom components are all "OR" gates, so the fault tree produces a total of 9 cut sets, namely C1={X1}, C2={X2}, C3={X3}, C4={X4}, C5={X5}, C6={X6}, C7={X7}, C8={X8}, C9={X9}.

[0256] In this exemplary embodiment, it also includes:

[0257] The analyzing the converted partial discharge signal to obtain the partial discharge detection result specifically includes:

[0258] Analyzing the converted partial discharge signal to obtain amplitude information, phase angle information and frequency information of the converted partial discharge signal;

[0259] Based on the amplitude information, the phase angle information and the frequency information, a two-dimensional spectrum and a three-dimensional spectrum are constructed as the partial discharge detection result.

[0260] The multi-physics field coupling simulation results are analyzed to obtain partial discharge detection results.

[0261] In specific implementation, the host display unit:

[0262] It is a software display interface that integrates the monitoring of the working status of temperature sensors and partial discharge sensors, on-site temperature display, alarm prompts and outputs, event recording and data recording. It runs on a PC and is used to display the two-dimensional PRPD and three-dimensional PRPD graphs of partial discharge signals, as well as the partial discharge neural network classification function. It can also perform some parameter configurations on the processing unit, and set the gain, attenuation and number of channels.

[0263] In summary, the present disclosure is applied to the partial discharge detection environment of a substation:

[0264] Comprehensive multi-physics field coupling: For the first time, the coupling of multiple physical fields such as electromagnetic field, temperature field, and stress field is incorporated into the partial discharge detection system, which is closer to the actual operating conditions of the equipment, ensures that the simulated environment is highly consistent with the actual working conditions, and improves the accuracy and practicality of the detection results.

[0265] High-precision partial discharge detection technology: using advanced sensors and signal processing algorithms to improve detection sensitivity and anti-interference capabilities. Main sensor: adapt to the complex working conditions of substation switchgear (ultrasonic + transient ground voltage), GIS (UHF), transformer (UHF / HF / ultrasonic), cable (high frequency)

[0266] Wireless data transmission technology: realize real-time and reliable data transmission, and improve the flexibility and scalability of the system.

[0267] Intelligent data analysis and failure prediction algorithms: Based on advanced algorithms such as big data and machine learning technology, deep mining and analysis of massive detection data are carried out to achieve real-time monitoring of equipment health status and failure warning, significantly improving operation and maintenance efficiency and fault prevention capabilities.

[0268] The beneficial effects brought by the technical solution disclosed in this disclosure include:

[0269] Improve the life prediction capability of the sensor itself by analyzing failure modeling under the influence of multi-physical field coupling;

[0270] The modified algorithm is introduced to perform noise filtering, signal type recognition and high-precision detection to improve the environmental adaptability and reliability of partial discharge sensors;

[0271] It can comprehensively monitor the partial discharge of substation equipment in a multi-physical field coupling environment and has a failure self-detection function, thereby improving the operational stability and safety of substation equipment and ensuring a stable supply of electricity;

[0272] It can detect partial discharge more accurately, discover potential fault hazards in time, and provide strong support for the maintenance and management of substations.

[0273] It has strong intelligent features, can automatically analyze and process the detected data, quickly identify the type and location of partial discharge, and give corresponding early warning information. This helps operation and maintenance personnel take timely measures to avoid further expansion of faults and reduce maintenance costs and power outage time.

[0274] Improving the economic benefits of substations by timely detecting and dealing with partial discharge problems can extend the service life of equipment and reduce the cost of equipment replacement and maintenance.

[0275] It should be noted that the method of the embodiment of the present disclosure can be performed by a single device, such as a computer or a server. The method of the present embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present disclosure, and the multiple devices will interact with each other to complete the described method.

[0276] It should be noted that the above describes some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0277] In addition, although the operations of the disclosed method are described in a particular order in the accompanying drawings, this does not require or imply that the operations must be performed in this particular order, or that all the operations shown must be performed to achieve the desired results. On the contrary, the steps depicted in the flow chart can be performed in a different order. Additionally or alternatively, some steps can be omitted, multiple steps can be combined into one step, and / or one step can be decomposed into multiple steps.

[0278] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0279] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0280] A person skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application (including the claims) is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0281] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present application difficult to understand, the known power / ground connections to the integrated circuit (IC) chip and other components may or may not be shown in the provided drawings. In addition, the device may be shown in the form of a block diagram to avoid making the embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present application are to be implemented (that is, these details should be fully within the scope of understanding of those skilled in the art). Where specific details (e.g., circuits) are set forth to describe exemplary embodiments of the present application, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details or with changes in these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0282] Although the present application has been described in conjunction with specific embodiments thereof, many alternatives, modifications and variations of these embodiments will be apparent to those skilled in the art in light of the foregoing description.

[0283] The embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the scope of protection of the present application.

[0284] Although the spirit and principle of the present disclosure have been described with reference to several specific embodiments, it should be understood that the present disclosure is not limited to the disclosed specific embodiments, and the division of various aspects does not mean that the features in these aspects cannot be combined to benefit, and such division is only for the convenience of expression. The present disclosure is intended to cover various modifications and equivalent arrangements included in the spirit and scope of the attached claims. The scope of the attached claims conforms to the broadest interpretation, thereby including all such modifications and equivalent structures and functions.

Claims

1. A failure self-detection method for a partial discharge detection system, characterized in that: The partial discharge detection system is used to detect partial discharge information in a substation to be monitored, the partial discharge detection system includes a partial discharge sensor, and the method includes: Acquiring electromagnetic environment simulation information in the substation to be monitored; Acquire the size information of the partial discharge sensor, vibration sensor and temperature sensor in the substation to be monitored and their location information in the substation to be monitored, and construct a partial discharge sensor simulation model, a vibration sensor simulation model and a temperature sensor simulation model; Based on the electromagnetic environment simulation information, the partial discharge sensor simulation model, the vibration sensor simulation model and the temperature sensor simulation model, iterate the following steps to perform multi-physical field coupling simulation to obtain a multi-physical field coupling simulation result: perform heat conduction finite element simulation based on the temperature sensor simulation model; perform electric field finite element simulation based on the partial discharge sensor simulation model and the electromagnetic environment simulation information; perform thermal stress finite element simulation based on the vibration sensor simulation model; Performing a failure self-check on the partial discharge detection system based on the multi-physics field coupling simulation result to obtain a self-check result of the partial discharge detection system, specifically comprising: performing a partial discharge signal type identification based on the multi-physics field coupling simulation result to obtain a partial discharge type corresponding to the multi-physics field coupling simulation result; performing a failure self-check on the partial discharge detection system based on the partial discharge type and the multi-physics field coupling simulation result to obtain a self-check result of the partial discharge detection system.

2. The method according to claim 1, characterized in that The heat conduction finite element simulation based on the temperature sensor simulation model includes: Adding material parameters to the temperature sensor simulation model; Meshing the temperature sensor simulation model; Adding a temperature load and boundary conditions corresponding to the temperature load to the temperature sensor simulation model; The temperature field distribution information of the temperature sensor at the position in the substation to be monitored is determined as a temperature simulation result.

3. The method according to claim 1, characterized in that The performing electric field finite element simulation based on the partial discharge sensor simulation model and the electromagnetic environment simulation information includes: Converting a partial discharge signal obtained from the electromagnetic environment simulation information based on the partial discharge sensor simulation model to obtain a converted partial discharge signal; The electric field finite element simulation is performed based on the converted partial discharge signal.

4. The method according to claim 3, characterized in that The converting and processing the partial discharge signal obtained from the electromagnetic environment simulation information based on the partial discharge sensor simulation model to obtain the converted partial discharge signal includes: Performing gain processing on the partial discharge signal to obtain a gained partial discharge signal; Performing bandpass filtering on the gained partial discharge signal to obtain a bandpass filtered partial discharge signal; Performing orthogonal mixing processing on the partial discharge signal after the bandpass filtering to obtain a partial discharge signal after orthogonal mixing; Performing anti-aliasing filtering on the partial discharge signal after the orthogonal mixing to obtain a partial discharge signal after anti-aliasing filtering; Performing analog-to-digital processing on the partial discharge signal after the anti-aliasing filtering to obtain the partial discharge signal in the form of a digital signal; Anti-interference and noise reduction processing is performed on the partial discharge signal in the form of a digital signal to obtain the converted partial discharge signal.

5. The method according to claim 4, characterized in that The step of performing anti-interference and noise reduction processing on the partial discharge signal in the form of a digital signal to obtain the converted partial discharge signal comprises: Performing empirical mode decomposition on the partial discharge signal in the form of a digital signal to obtain an intrinsic mode function waveform; Filtering the intrinsic mode function waveforms that are less than a threshold value among the intrinsic mode function waveforms to obtain filtered intrinsic mode function waveforms; The filtered intrinsic mode function waveform is subjected to Hilbert transformation to obtain the transformed partial discharge signal.

6. The method according to claim 1, characterized in that The performing of thermal stress finite element simulation based on the vibration sensor simulation model comprises: Adding material parameters to the vibration sensor simulation model; acquiring a vibration condition, and determining a vibration function based on the vibration condition; Meshing the vibration sensor simulation model; Adding boundary conditions to the vibration sensor simulation model; The vibration stress distribution information of the vibration sensor in the substation to be monitored is determined as a vibration simulation result.

7. The method according to claim 1, characterized in that The identifying of the local discharge signal type based on the multi-physics field coupling simulation result to obtain the local discharge type corresponding to the multi-physics field coupling simulation result includes: Inputting the partial discharge signal into a pre-trained partial discharge type recognition model to obtain a partial discharge type corresponding to the partial discharge sensor output by the partial discharge type recognition model; The training method of the partial discharge type recognition model includes: Collect known partial discharge signals corresponding to the partial discharge defect type; Building a partial discharge information library based on the known partial discharge signal and the partial discharge defect type corresponding to the known partial discharge signal; Constructing a partial discharge type recognition model to be trained; The partial discharge information library is used as an input to train the partial discharge type recognition model to be trained.

8. The method according to claim 1, characterized in that The performing a failure self-test on the partial discharge detection system based on the partial discharge type and the multi-physics field coupling simulation result to obtain a self-test result of the partial discharge detection system includes: Determining functional modules in the partial discharge detection system; Performing a failure self-test on the partial discharge detection system to obtain a self-test result of the partial discharge detection system, determining the working state of the functional module, and constructing a failure logic relationship diagram; Based on the failure logic relationship diagram, construct a failure mode fault tree; Based on the failure mode fault tree, a failure self-test is performed on the partial discharge detection system to obtain a self-test result of the partial discharge detection system.

9. A partial discharge detection system, characterized in that: The partial discharge detection system is used to detect partial discharge information in a substation to be monitored, and the system comprises: The multi-physics field coupled partial discharge detection module is configured to obtain electromagnetic environment simulation information in the substation to be monitored; obtain size information of the partial discharge sensor, vibration sensor and temperature sensor in the substation to be monitored and position information in the substation to be monitored, and construct a partial discharge sensor simulation model, a vibration sensor simulation model and a temperature sensor simulation model; The partial discharge detection signal analysis module is configured to iterate the following steps to perform multi-physical field coupling simulation based on the electromagnetic environment simulation information, the partial discharge sensor simulation model, the vibration sensor simulation model and the temperature sensor simulation model to obtain a multi-physical field coupling simulation result: perform a heat conduction finite element simulation based on the temperature sensor simulation model; perform an electric field finite element simulation based on the partial discharge sensor simulation model and the electromagnetic environment simulation information; and perform a thermal stress finite element simulation based on the vibration sensor simulation model. A signal transmission module, configured to transmit signals in the partial discharge detection system; The host display and analysis unit is configured to perform a failure self-test on the partial discharge detection system based on the multi-physics field coupling simulation result to obtain a self-test result of the partial discharge detection system.

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