Fault discrimination method, system and device for on-line monitoring of low-oil equipment and medium

Abstract

The application provides a fault discrimination method, system, device and medium for online monitoring of an oil-starved equipment, comprising the following steps: collecting physical quantity optical signals of a three-phase oil-starved equipment in a running state through a MEMS optical fiber sensor according to a set sampling interval; compensating the collected physical quantity optical signals to eliminate interference of external factors and obtaining physical quantity optical signals in a standard environment; and discriminating faults of the three-phase oil-starved equipment based on the physical quantity optical signals of the three-phase oil-starved equipment in the running state collected by the MEMS optical fiber sensor and the compensated physical quantity optical signals. The fault discrimination method and system for monitoring the state of the oil-starved equipment provided by the application can judge whether the oil-starved equipment is faulty by comparing the corresponding physical quantity optical signals of the three-phase oil-starved equipment, reduce fault misjudgment by compensating to reduce the influence of environmental factors, and intuitively determine whether a fault occurs.

Description

Technical Field

[0001] The present invention relates to the technical field of state monitoring of low-oil equipment, and in particular to a fault identification method, system, equipment and medium for online monitoring of low-oil equipment. Background Art

[0002] Low-oil equipment, such as instrument transformers, circuit breakers, and bushings, is a vital component of the power system and plays a critical role in the normal operation of the power grid. Most low-oil equipment is installed outdoors, facing harsh operating environments. Failures are inevitable during long-term operation. If maintenance personnel fail to detect potential faults in a timely manner, they could potentially cause power grid accidents, threatening people's property and lives. The operational status of low-oil equipment is often determined by the quality of its insulation, which can be best assessed by analyzing the quality of the insulating oil. Existing systems for monitoring the status of low-oil equipment are limited in scope and have certain shortcomings.

[0003] Currently, condition monitoring systems for low-oil power equipment mostly use active sensors. Active sensors require an external power supply, and insulating oil is typically present inside power equipment. Replacing the power supply for sensors installed within power equipment is inherently complex. Furthermore, active sensors are susceptible to environmental influences such as electromagnetic fields, temperature, and pressure, leading to inaccurate transmission of monitoring signals. Furthermore, existing active sensors are generally large and heavy, making them difficult to monitor within the confined spaces of power equipment. These limitations hinder the convenience and accuracy of condition monitoring for low-oil power equipment. Summary of the Invention

[0004] In view of the defects in the prior art, the purpose of the present invention is to provide a fault identification method, system, equipment and medium for online monitoring of low-oil equipment.

[0005] A first aspect of the present invention provides a fault identification method for online monitoring of low-oil equipment, comprising:

[0006] According to the set sampling interval, the MEMS optical fiber sensor collects the physical quantity optical signals of the operating status of the three-phase low-oil equipment, wherein the physical quantity optical signals include optical signals corresponding to gas physical quantities and optical signals corresponding to non-gas physical quantities;

[0007] Compensating the collected physical quantity light signal to eliminate interference from external factors and obtain the physical quantity light signal under a standard environment;

[0008] Based on the physical quantity optical signal of the operating status of the three-phase low-oil equipment collected by the above-mentioned MEMS optical fiber sensor and the compensated physical quantity optical signal, the fault of the three-phase low-oil equipment is judged.

[0009] Optionally, the optical signals corresponding to the gas physical quantities include optical signals corresponding to the hydrogen and acetylene contents dissolved in the insulating oil of the three-phase low-oil equipment, and optical signals corresponding to the total refractive index of the gas precipitated in the insulating oil.

[0010] Optionally, the optical signal corresponding to the non-gaseous physical quantity includes an optical signal corresponding to the temperature and pressure of insulating oil in a three-phase low-oil equipment.

[0011] Optionally, compensating the collected physical quantity optical signal to obtain a sampling signal under a standard environment includes:

[0012] First, the collected physical quantity light signal is subjected to temperature self-calibration compensation: the self-calibration compensation of the temperature sensor is used to reduce the occurrence of temperature drift;

[0013] Secondly, the physical quantity optical signal after the temperature self-calibration compensation is compensated for the sensor fiber structure parameters: according to the thermal expansion coefficient of the MEMS fiber sensor, the influence of the sensor structure parameters on the fiber temperature sensor demodulation formula is compensated;

[0014] Finally, gradient compensation is performed on the physical quantity optical signal after the optical fiber structural parameters of the above-mentioned sensor are compensated: the differences between the working environment temperature and 20°C and the working environment pressure and 101.3kPa are calculated respectively, and the temperature and pressure are compensated with a gradient of 1°C and 0.5kPa based on the calculated differences.

[0015] Optionally, the method further comprises optimizing the compensated sampling signal to make the measured optical signal more accurate.

[0016] Optionally, the determining of the fault of the oil-poor equipment includes:

[0017] First, the MEMS fiber optic sensor collects the physical quantity optical signal of the three-phase low-oil equipment operating status, and uses phase loss judgment to eliminate the fault of the monitoring system hardware equipment;

[0018] Secondly, for the compensated physical quantity light signal, the maximum value of the change is selected by using the bull's eye deviation judgment;

[0019] Finally, the maximum value of the selected change is determined based on the bull's eye deviation, and a comparison is made to determine whether a fault has occurred;

[0020] Further:

[0021] Phase loss judgment: judging whether a fault occurs based on whether the collected signals of phases A, B, and C of the low-oil equipment are missing;

[0022] The target deviation judgment comprises: respectively calculating the target deviations of the refractive index, hydrogen content and acetylene content of the three-phase low-oil equipment, selecting the value with the largest target deviation among the refractive index, hydrogen content and acetylene content, and transmitting the compensated signal of the selected value to the comparison judgment;

[0023] The comparison and judgment is to compare the above-mentioned compensated signal with the corresponding reference optical signal to determine whether a fault occurs.

[0024] A second aspect of the present invention provides a fault diagnosis system for online monitoring of low-oil equipment, comprising:

[0025] A signal acquisition module, which uses a MEMS fiber optic sensor to collect physical quantity optical signals of the operating status of the three-phase low-oil equipment at a set sampling interval. The physical quantity optical signals include optical signals corresponding to gas physical quantities and optical signals corresponding to non-gas physical quantities;

[0026] a signal compensation module, which compensates the physical quantity light signal collected by the signal collection module to obtain the physical quantity light signal under a standard environment;

[0027] A fault identification module is provided, which identifies the fault of the three-phase low-oil equipment based on the physical quantity optical signal of the three-phase low-oil equipment operating status collected by the MEMS optical fiber sensor and the physical quantity optical signal compensated by the above-mentioned signal compensation module.

[0028] The third aspect of the present invention provides a fault diagnosis device for online monitoring of low-oil equipment, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor is used to execute the fault diagnosis method for online monitoring of low-oil equipment when executing the program.

[0029] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is used to execute the fault identification method for online monitoring of low-oil equipment.

[0030] Compared with the prior art, the embodiments of the present invention have at least one of the following beneficial effects:

[0031] The fault identification method and system for low-oil equipment status monitoring provided by the present invention can obtain a more accurate monitoring signal (optical signal) by compensating the collected optical signal, thereby improving the accuracy of fault identification in the overall monitoring and avoiding judgment errors caused by inaccurate signal collection.

[0032] The fault identification method and system for low-oil equipment status monitoring provided by the present invention compensate for the temperature drift phenomenon of the MEMS fiber optic temperature sensor, the thermal expansion coefficient of the fiber optic, and the ambient temperature and pressure through temperature self-calibration compensation, sensor fiber optic structural parameter compensation, and gradient compensation, thereby improving the stability of the sensor's long-term operation and the accuracy of fault identification.

[0033] The fault discrimination method and system for low-oil equipment status monitoring provided by the present invention can distinguish a variety of situations by organically linking and judging a plurality of fault discrimination methods, thereby improving the accuracy of the entire monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments with reference to the following drawings:

[0035] Figure 1 This is a flow chart of a fault determination method according to an embodiment of the present invention;

[0036] Figure 2 This is a module block diagram of a fault identification system according to an embodiment of the present invention;

[0037] Figure 3 This is a structural diagram of an optical fiber MEMS sensor for collecting optical signals of gas-related physical quantities according to one embodiment of the present invention;

[0038] Figure 4 This is a structural diagram of an optical fiber MEMS sensor for collecting optical signals of non-gas physical quantities according to an embodiment of the present invention;

[0039] Figure 5 A flowchart of the steps of a multi-physical quantity fault diagnosis method in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0040] The present invention will be described in detail below with reference to specific embodiments. The following examples will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those skilled in the art, several changes and improvements can be made without departing from the scope of the present invention. These all fall within the scope of protection of the present invention.

[0041] Reference Figure 1 FIG. 1 is a flow chart of a fault identification method for online monitoring of low-oil equipment in one embodiment of the present invention, including:

[0042] S100, collecting physical quantity optical signals of the low-oil equipment according to the set sampling interval, wherein the physical quantity optical signals include optical signals corresponding to gas physical quantities and optical signals corresponding to non-gas physical quantities; in a specific application, the gas physical quantity optical signals include at least optical signals corresponding to the hydrogen and acetylene contents dissolved in the insulating oil of the three-phase low-oil equipment; the non-gas physical quantity optical signals include optical signals corresponding to the temperature, pressure and refractive index of the three phases A, B and C in the three-phase low-oil equipment.

[0043] S200 , compensating the collected physical quantity optical signal to obtain the physical quantity optical signal under a standard environment; specifically, the compensation includes temperature self-calibration compensation, optical fiber structure parameter compensation, and gradient compensation.

[0044] S300: Based on the compensated physical quantity optical signal, a fault of the oil-deficient equipment is determined.

[0045] This embodiment can obtain a more accurate monitoring signal (optical signal) by compensating the collected optical signal, thereby improving the accuracy of fault judgment in the overall monitoring and avoiding judgment errors caused by inaccurate signal collection.

[0046] In some preferred embodiments, when executing S200, compensation of the collected physical quantity light signal may be performed in the following order:

[0047] S201, temperature self-calibration compensation:

[0048] MEMS fiber optic temperature sensors can experience temperature drift after long-term operation, seriously affecting their performance. Therefore, self-calibration compensation of the collected physical quantity optical signal through the temperature sensor can effectively reduce the occurrence of temperature drift and improve the stability of the sensor during long-term operation.

[0049] S202, sensor optical fiber structural parameter compensation:

[0050] The physical quantity optical signal after temperature self-calibration compensation of S201 is compensated for the influence of sensor structural parameters by modifying the parameters of the optical fiber temperature sensor demodulation formula according to the thermal expansion coefficient of the MEMS optical fiber sensor.

[0051] Optical fiber is one of the main sensing elements of MEMS fiber optic sensors. Different batches of optical fiber are used in the production process of different fiber optic sensors. The thermal expansion of different batches of optical fiber varies, leading to deviations in the demodulation process of the fiber optic sensor. By inputting the sensor's thermal expansion coefficient α in advance, the effects of the sensor's structural parameters can be compensated for through parameter correction in the fiber optic temperature sensor demodulation formula, thereby improving the sensor's performance. The fiber optic temperature sensor demodulation formula is as follows:

[0052] ΔλB=λB(α+ξ)ΔT

[0053] ΔλB is the change in the optical signal, λB is the initial value of the optical signal, ξ is the thermo-optical coefficient, and ΔT is the temperature change.

[0054] S203, gradient compensation:

[0055] The physical quantity optical signal after the optical fiber structural parameters of the above sensor are compensated is used to calculate the difference between the working environment temperature and 20°C, and the working environment pressure and 101.3kPa, respectively. Based on the calculated differences, temperature and pressure compensation is performed with a gradient of 1°C and 0.5kPa.

[0056] MEMS fiber optic sensors can only detect optical signals in the transformer's operating environment, not the standard 20°C, 101.3kPa ambient conditions required by national standards. A gradient compensation table is created, with temperature gradients divided into 1°C intervals and pressure gradients divided into 0.5kPa intervals. Different gradients have different temperature or pressure compensation values. By calculating the difference between the operating environment temperature and 20°C and the operating environment pressure and 101.3kPa, appropriate gradients are selected for temperature and pressure compensation, improving fault identification accuracy.

[0057] The above embodiment compensates for the temperature drift phenomenon of the MEMS fiber optic temperature sensor, the thermal expansion coefficient of the fiber optic, and the ambient temperature and pressure through temperature self-calibration compensation, sensor fiber structural parameter compensation, and gradient compensation, thereby improving the stability of the sensor during long-term operation and the accuracy of fault identification.

[0058] In some preferred embodiments, after S200, the compensated sampled signal may be optimized. Specifically, the collected optical signal is compensated for environmental physical quantities and optimized through curve fitting and signal screening methods. Signal compensation achieves an experimental environment that meets national standards, improving the reliability of the measured data. Optimization and screening are performed to ensure that the measured optical signal is more accurate and highly consistent with the actual data curve. Specifically, in some embodiments, compensation for refractive index, hydrogen content, and acetylene content using temperature and pressure can further achieve high-precision multi-physical quantity linkage fault diagnosis.

[0059] In some preferred embodiments, when executing the above S300, the fault of the low-oil equipment can be judged by phase loss judgment, target deviation judgment, and comparison judgment. Specifically, in some embodiments, the following specific operations can be used:

[0060] (1) Phase loss judgment: For the three-phase optical signals of the low-oil equipment operating status collected by S100, observe whether the collected signals are missing. If any of the three-phase optical signals is missing, it is judged that the hardware equipment of the online monitoring system of the low-oil equipment is faulty and a fault warning is issued; otherwise, a bull's-eye deviation judgment is performed;

[0061] (2) Bull's-eye deviation judgment: First, calculate the average values ​​of the refractive index, hydrogen content, and acetylene content of the three phases A, B, and C after S200 compensation, then calculate the absolute deviation of each refractive index, hydrogen content, and acetylene content from the average value, and finally select the one with the largest bull's-eye deviation among the refractive index, hydrogen content, and acetylene content;

[0062] (3) Comparative judgment: For the acquisition signals with the largest target deviation of the refractive index, hydrogen content and acetylene content output after the target deviation judgment, calculate the size relationship between the acquisition signal and the corresponding reference light signal. If any acquisition signal is greater than the corresponding reference light signal, the low-oil equipment in that phase is faulty. If all acquisition signals are less than the corresponding reference light signal, the low-oil equipment is normal.

[0063] The three methods above are used in combination. Specifically, first, phase loss is used to eliminate hardware faults in the monitoring system. If the phase loss judgment indicates a device failure, an alarm is generated, eliminating the need for the two subsequent judgments. If the phase loss judgment indicates normal operation, a bull's-eye deviation judgment is performed. The maximum value of the deviation is selected through the bull's-eye deviation judgment, and finally, a comparison is performed to determine whether a fault has occurred. By organically linking multiple fault diagnosis methods, a variety of situations can be identified, improving the accuracy of the entire monitoring process.

[0064] In the above embodiment, the bull's eye deviation is judged, where the bull's eye refers to the average value of three input signals (refractive index, soluble hydrogen content, and soluble acetylene content), and the deviation refers to the deviation between the input signal of each phase and the deviation value; the three bull's eye deviation values ​​(refractive index, soluble hydrogen content, and soluble acetylene content) are calculated and it is determined which one has the largest bull's eye deviation value, and the maximum value is selected for comparison and judgment.

[0065] In other embodiments, after completing S300, the operational status and monitoring data of the low-oil equipment can be further stored, where the stored data includes the collected physical optical signals, the processed physical optical signals, and the judgment data. The stored data can also be uploaded to a cloud platform.

[0066] Reference Figure 2As shown, based on the same technical concept as above, in another embodiment of the present invention, a fault discrimination system for online monitoring of low-oil equipment is provided, which specifically includes: a signal acquisition module, a signal compensation module and a fault discrimination module; the signal acquisition module collects physical quantity optical signals of the three-phase low-oil equipment according to the set sampling interval, and the physical quantity optical signals include optical signals corresponding to gas physical quantities and optical signals corresponding to non-gas physical quantities; the signal compensation module compensates the physical quantity optical signals collected by the signal acquisition module to obtain physical quantity optical signals under a standard environment; the fault discrimination module judges the fault of the low-oil equipment based on the physical quantity optical signals of the three-phase low-oil equipment collected by the above-mentioned signal acquisition module and the physical quantity optical signals compensated by the signal compensation module.

[0067] In addition, in some embodiments, the gas-related and non-gas-related optical signals of the A, B, and C phases of a three-phase low-oil device can be collected and measured using corresponding fiber-optic MEMS sensors. The physical quantity optical signals collected by each type of fiber-optic MEMS sensor are transmitted to the signal compensation module, which is then further transmitted to the judgment module. The judged signal is then transmitted to the cloud platform via the data module. This allows users to monitor the status of the low-oil device immediately even when they are not on-site. This system is easy to use, has a wide range of applications, and provides excellent monitoring results, effectively meeting user requirements for online monitoring of low-oil devices.

[0068] In some embodiments, a MEMS fiber optic gas sensor can be used to measure the dissolved gas physical quantity signals in the insulating oil of low-oil equipment; a MEMS fiber optic temperature and pressure sensor can be used to measure the non-gas physical quantity signals of the insulating oil of low-oil equipment, thereby obtaining gas physical quantity optical signals and non-gas physical quantity optical signals.

[0069] Specifically, Figure 3This is a structural diagram of a fiber optic MEMS sensor for collecting optical signals of gas-related physical quantities according to an embodiment of the present invention. In this specific example, the MEMS fiber optic gas sensor includes: a MEMS fiber optic gas sensor oil inlet 31, a MEMS fiber optic gas sensor oil chamber 32, a MEMS fiber optic gas sensor semi-permeable membrane 33, a MEMS fiber optic gas sensor air chamber 34, and a MEMS fiber optic gas sensor core 37. These components are all arranged on the sensor body, wherein: the MEMS fiber optic gas sensor oil inlet 31 is located on one side of the sensor body, and the other end extends into the sensor body; the MEMS fiber optic gas sensor oil chamber 32 is located inside the sensor body, and is arranged at the end of the MEMS fiber optic gas sensor oil inlet 31 extending into the sensor body, and is connected to the MEMS fiber optic gas sensor core 37. The MEMS fiber optic gas sensor oil inlet 31 is connected, and insulating oil flows into the MEMS fiber optic gas sensor oil chamber 32 through the MEMS fiber optic gas sensor oil inlet 31. The MEMS fiber optic gas sensor air chamber 34 is disposed within the sensor body, and the MEMS fiber optic sensor chip 37 is encapsulated within the MEMS fiber optic gas sensor air chamber 34, which is used to measure physical parameters of dissolved gases in the insulating oil of oil-depleted equipment. The MEMS fiber optic gas sensor semipermeable membrane 33 is disposed between the MEMS fiber optic gas sensor oil chamber 32 and the MEMS fiber optic gas sensor air chamber 34, and the insulating oil passes through the MEMS fiber optic gas sensor semipermeable membrane 33 to complete the oil-gas separation of the insulating oil. Furthermore, the first MEMS fiber optic sensor also includes a slot 35, in which a sealing ring is installed to strengthen the seal at the connection between the MEMS fiber optic gas sensor and other components.

[0070] Figure 4 This is a structural diagram of a fiber optic MEMS sensor for collecting optical signals of non-gaseous physical quantities according to one embodiment of the present invention. In this specific example, the MEMS fiber optic temperature and pressure sensor includes: a MEMS fiber optic temperature and pressure sensor oil inlet 41, a MEMS fiber optic temperature and pressure sensor oil chamber 42, and a MEMS fiber optic temperature and pressure sensor chip 45. These components are all arranged on the sensor body. The MEMS fiber optic temperature and pressure sensor oil inlet 41 is located on the side of the sensor body and communicates with the MEMS fiber optic temperature and pressure sensor oil chamber 42 inside the sensor body, with the other end extending into the interior of the sensor body. The MEMS fiber optic sensor chip 45 is encapsulated in the MEMS fiber optic temperature and pressure sensor oil chamber 42 and is used to measure non-gaseous physical quantity parameters of low-oil equipment. Furthermore, the MEMS fiber optic temperature and pressure sensor also includes a slot 43, in which a sealing ring is installed to strengthen the seal at the connection between the second MEMS fiber optic sensor and other components.

[0071] Fiber optic sensors are passive devices that can effectively achieve electrical isolation. The optical fiber itself is resistant to high temperatures and high pressures. They can also monitor the status of various low-oil equipment (pressure, temperature, gas content, etc.) and aggregate the monitored signals onto the same optical cable. MEMS technology enables the size of equipment to be scaled down to the micron and millimeter levels. In this embodiment, MEMS technology is applied to fiber optic sensors. The use of the aforementioned MEMS fiber optic sensor with a specific structure can address some of the shortcomings of current active sensors, achieving electromagnetic isolation. Temperature and electromagnetic fields have little effect on the insulation level of the optical fiber, resulting in more accurate measured data that is more suitable for low-oil equipment status monitoring.

[0072] In some embodiments, the fault identification module determines whether the online monitoring system has a fault by whether a phase is missing; determines the fault of the low-oil equipment by comparing the physical quantity with the limit value; and determines the potential fault of the low-oil equipment by the change in the physical quantity. Specifically, the fault identification module includes: a bull's eye deviation judgment submodule: respectively calculates the bull's eye deviation of the temperature, pressure, refractive index, hydrogen content, and acetylene content of the three-phase low-oil equipment, selects the value with the largest bull's eye deviation among the temperature, pressure, refractive index, hydrogen content, and acetylene content, and transmits the compensated signal of the selected amount to the absolute value judgment; a comparison judgment submodule, compares the signal value selected based on the bull's eye deviation judgment with the corresponding reference light signal, compares the deviation of the signal, and determines whether the low-oil equipment has a fault based on the change. This embodiment improves the accuracy of the monitoring system through the fault identification module and multiple comparison judgment modules working together.

[0073] In order to better illustrate the above technical solution, a preferred embodiment is provided below to describe the detailed steps. However, it should be understood that the present invention is not limited to the following embodiment.

[0074] Figure 5 A flowchart of the steps of the multi-physical quantity fault identification method in a preferred embodiment of the present invention. Figure 5 As shown, a fault diagnosis method for low-oil equipment condition monitoring based on MEMS sensing principles is described. The gas-related physical quantities collected are the refractive index of the insulating oil and the parameters of the hydrogen and acetylene content dissolved in the insulating oil. The non-gas-related physical quantities collected are the parameters corresponding to the temperature and pressure of the insulating oil. The fault diagnosis method specifically includes the following steps:

[0075] S1, initial value setting: given the limit values ​​of insulating oil refractive index, dissolved hydrogen content in oil and dissolved acetylene content in oil under standard environment, recorded as reference value N, C CH2 、C C2H2 , given sampling interval t, given gradient compensation table;

[0076] S2, signal acquisition: respectively collect the temperature, pressure, refractive index of insulating oil precipitated gas, content of dissolved hydrogen in oil and content of dissolved acetylene in oil of the three-phase A, B and C of the three-phase low-oil equipment. H2 i. C C2H2 i(i=1,2,3);

[0077] S3, performing temperature self-calibration compensation on the optical signal collected by S2: dynamic temperature compensation is performed through an adaptive filtering algorithm; the specific adaptive filtering algorithm can adopt existing technologies, for example, adaptively adjusting the optimal filtering of the filter according to the characteristics of the input signal;

[0078] S4, further performing sensor structural parameter compensation on the optical signal after S3 compensation: performing optical fiber expansion coefficient compensation by demodulating parameter correction of the optical fiber temperature sensor;

[0079] S5, further gradient compensation is performed on the optical signal after S4 compensation:

[0080] Calculate the difference between the working environment temperature and 20℃, and the working environment pressure and 101.3kPa: ΔT i =|T i -20|,ΔP i =|P i -101.3|Calculate ΔT separately i and ΔP i (i=1,2,3);

[0081] According to the calculated difference ΔT i and ΔP i The temperature and pressure compensation is performed with a gradient of 1°C and 0.5kPa, that is, the gradient is selected to compensate the refractive index of the insulating oil precipitated gas, the content of dissolved hydrogen in the oil, and the content of dissolved acetylene in the oil for temperature and pressure. The compensated gas refractive index, the content of dissolved hydrogen in the oil, and the content of dissolved acetylene in the oil are respectively recorded as N 0 i 、 and

[0082] S6, perform phase loss judgment on the optical signal collected in S2: determine whether the collected optical signal is missing. If the optical signal is not missing, proceed to S7, i.e., bull's-eye deviation judgment; if there is optical signal missing, determine the transmission channel where the signal is missing, and output the optical path abnormality of the channel;

[0083] S7, judge the center deviation of the optical signal after S5 compensation:

[0084] First, calculate the signal center value N0, and in:

[0085]

[0086]

[0087]

[0088] Then, calculate the center deviation value N' of each three-phase compensated signal i 、 and in:

[0089]

[0090]

[0091]

[0092] Finally, determine the three-phase center deviation N' of the insulating oil gas refractive index, the dissolved hydrogen content in the oil, and the dissolved acetylene content in the oil i 、 and The maximum value of the corresponding output is N' max 、 and

[0093] S8, compare and judge based on the maximum value of the center deviation output by S7:

[0094] The maximum value N' of the target deviation of the selected insulating oil refractive index, dissolved hydrogen content in the oil, and dissolved acetylene content in the oil is determined based on the S7 target deviation. max 、 and Compare it with the corresponding reference values ​​N and C H2 、C C2H2 Compare the size, if N′ max 、 and If any one of the items is greater than its corresponding reference value, the corresponding oil-poor equipment fault is output; otherwise, the system returns to S2 and starts a new cycle.

[0095] This embodiment uses compensated physical quantity optical signals to identify faults in low-oil equipment, improving the accuracy of fault diagnosis. Furthermore, by comparing phase loss detection, it can be used to determine if a low-oil device is faulty. If all devices display a fault warning, it can be determined to be environmental interference. Furthermore, this comparative judgment reduces false positives and allows for intuitive identification of a fault. The linkage of multiple comparison and judgment modules enhances the accuracy of the monitoring system.

[0096] Based on the same technical concept as above, in another embodiment of the present invention, a fault diagnosis device for online monitoring of low-oil equipment is also provided, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the program, it is used to execute the fault diagnosis method for online monitoring of low-oil equipment in the above embodiment.

[0097] Based on the same technical concept as above, in another embodiment of the present invention, a computer-readable storage medium is also provided, on which a computer program is stored. When the program is executed by a processor, it is used to execute the fault judgment method for online monitoring of low-oil equipment in the above embodiment.

[0098] It should be noted that the steps in the method provided by the present invention can be implemented using the corresponding modules, devices, units, etc. in the system. Those skilled in the art can refer to the technical solution of the system to implement the step flow of the method, that is, the embodiments in the system can be understood as preferred examples for implementing the method, which will not be elaborated here.

[0099] Those skilled in the art will appreciate that, in addition to implementing the system and its various devices provided by the present invention in purely computer-readable program code, it is entirely possible to implement the same functions of the system and its various devices provided by the present invention in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. by logically programming the method steps. Therefore, the system and its various devices provided by the present invention can be considered a hardware component, and the devices included therein for implementing the various functions can also be considered as structures within the hardware component; the devices for implementing the various functions can also be considered as both software modules implementing the method and structures within the hardware component.

[0100] The above describes specific embodiments of the present invention. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art may make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. The embodiments of this application and the features in the embodiments may be combined with each other in any manner unless there is a conflict.

Claims

1. A fault identification method for online monitoring of three-phase low-oil equipment, characterized in that: include: According to the set sampling interval, the MEMS optical fiber sensor collects the physical quantity optical signals of the operating status of the three-phase low-oil equipment, wherein the physical quantity optical signals include optical signals corresponding to gas physical quantities and optical signals corresponding to non-gas physical quantities; Compensating the collected physical quantity light signal to eliminate interference from external factors and obtain a physical quantity light signal under a standard environment; wherein compensating the collected physical quantity light signal includes: First, the collected physical quantity light signal is subjected to temperature self-calibration compensation, wherein the temperature self-calibration compensation adopts the self-calibration compensation of the temperature sensor to reduce the occurrence of temperature drift; Secondly, the physical quantity optical signal after the temperature self-calibration compensation is compensated for the sensor fiber structure parameters: according to the thermal expansion coefficient of the MEMS fiber optic sensor, the influence of the sensor structure parameters on the fiber optic temperature sensor demodulation formula is compensated; the fiber optic temperature sensor demodulation formula is as follows: Dl B =λ B (a+ξ)ΔT Δλ B is the optical signal variation, λ B is the initial value of the optical signal, ξ is the thermo-optical coefficient, and ΔT is the temperature change; Finally, gradient compensation is performed on the physical quantity optical signal after the above-mentioned sensor fiber structure parameter compensation: the difference between the working environment temperature and 20°C and the working environment pressure and 101.3kPa is calculated respectively, and the temperature and pressure compensation are performed based on the calculated differences with a gradient of 1°C and 0.5kPa respectively. Based on the physical quantity light signal of the three-phase low-oil equipment operating status collected by the above-mentioned MEMS optical fiber sensor and the compensated physical quantity light signal, a phase loss judgment is performed based on the collected physical quantity light signal of the three-phase low-oil equipment operating status to determine whether the hardware equipment of the monitoring system has failed. If the phase loss judgment is normal, a bull's eye deviation judgment and a comparative judgment are performed in sequence based on the compensated physical quantity light signal to determine whether the three-phase low-oil equipment has failed, wherein: Phase loss judgment: judging whether the hardware equipment of the monitoring system is faulty based on whether the collected signals of phases A, B, and C of the three-phase low-oil equipment are missing; The target deviation judgment comprises: respectively calculating the target deviations of the refractive index, hydrogen content and acetylene content of the three-phase low-oil equipment, selecting the value with the largest target deviation among the refractive index, hydrogen content and acetylene content, and transmitting the compensated signal of the selected value to the comparison judgment; The comparison and judgment is to compare the compensated signal of the selected amount with the corresponding reference optical signal to determine whether the three-phase low-oil equipment has a fault.

2. The fault identification method for online monitoring of three-phase low-oil equipment according to claim 1 is characterized in that: The optical signals corresponding to the gas physical quantities include optical signals corresponding to the hydrogen and acetylene contents dissolved in the insulating oil of the three-phase low-oil equipment, and optical signals corresponding to the total refractive index of the precipitated gas in the insulating oil; The optical signals corresponding to the non-gaseous physical quantities include optical signals corresponding to the temperature and pressure of the insulating oil of the three-phase low-oil equipment.

3. The fault identification method for online monitoring of three-phase low-oil equipment according to claim 1 is characterized in that: The method also includes optimizing the compensated sampling signal to make the measured optical signal more accurate.

4. The fault identification method for online monitoring of three-phase low-oil equipment according to claim 1 is characterized in that: The fault identification of the three-phase low-oil equipment includes: First, the MEMS fiber optic sensor collects the physical quantity optical signal of the three-phase low-oil equipment operating status, and uses phase loss judgment to eliminate the fault of the monitoring system hardware equipment; Secondly, for the compensated physical quantity light signal, the maximum value of the change is selected by using the bull's eye deviation judgment; Finally, the maximum value of the change is selected according to the target deviation, and the three-phase low-oil equipment is determined to be faulty through comparison.

5. The fault identification method for online monitoring of three-phase low-oil equipment according to claim 1 is characterized in that: Phase loss judgment: For the optical signals of the three-phase low-oil equipment operating status, observe whether the collected signals are missing. If any of the three-phase optical signals are missing, it is determined that the hardware equipment of the online monitoring system of the three-phase low-oil equipment is faulty and a fault warning is issued; otherwise, a bull's-eye deviation judgment is performed; Bull's-eye deviation judgment: First, calculate the average values ​​of the refractive index, hydrogen content, and acetylene content of the three phases A, B, and C after the above compensation. Then calculate the absolute deviation of each refractive index, hydrogen content, and acetylene content from the average value. Finally, select the one with the largest bull's-eye deviation among the refractive index, hydrogen content, and acetylene content. Comparative judgment: For the acquisition signal with the largest target deviation of the refractive index, hydrogen content and acetylene content output after the target deviation judgment, calculate the size relationship between the acquisition signal and the corresponding reference light signal. If any acquisition signal is greater than the corresponding reference light signal, the low-oil equipment of that phase is faulty. If all acquisition signals are less than the corresponding reference light signal, the three-phase low-oil equipment is normal.

6. A fault identification system for online monitoring of three-phase low-oil equipment, adopting the fault identification method according to any one of claims 1 to 5, characterized in that: include: A signal acquisition module, which uses a MEMS fiber optic sensor to collect physical quantity optical signals of the operating status of the three-phase low-oil equipment at a set sampling interval. The physical quantity optical signals include optical signals corresponding to gas physical quantities and optical signals corresponding to non-gas physical quantities; A signal compensation module, which compensates the physical quantity light signal collected by the signal collection module to obtain a physical quantity light signal under a standard environment; wherein the compensation of the collected physical quantity light signal includes: First, the collected physical quantity light signal is subjected to temperature self-calibration compensation, wherein the temperature self-calibration compensation adopts the self-calibration compensation of the temperature sensor to reduce the occurrence of temperature drift; Secondly, the physical quantity optical signal after the temperature self-calibration compensation is compensated for the sensor fiber structure parameters: according to the thermal expansion coefficient of the MEMS fiber optic sensor, the influence of the sensor structure parameters on the fiber optic temperature sensor demodulation formula is compensated; the fiber optic temperature sensor demodulation formula is as follows: Dl B =λ B (a+ξ)ΔT Δλ B is the optical signal variation, λ B is the initial value of the optical signal, ξ is the thermo-optical coefficient, and ΔT is the temperature change; Finally, gradient compensation is performed on the physical quantity optical signal after the above-mentioned sensor fiber structure parameter compensation: the difference between the working environment temperature and 20°C and the working environment pressure and 101.3kPa is calculated respectively, and the temperature and pressure compensation are performed based on the calculated differences with a gradient of 1°C and 0.5kPa respectively. The fault identification module is based on the physical quantity optical signal of the three-phase low-oil equipment operating status collected by the MEMS optical fiber sensor and the physical quantity optical signal compensated by the above-mentioned signal compensation module. According to the physical quantity optical signal of the three-phase low-oil equipment operating status collected, the module performs phase loss judgment to determine whether the hardware equipment of the monitoring system has a fault. If the phase loss judgment is normal, the module then performs a target deviation judgment and a comparison judgment based on the compensated physical quantity optical signal in sequence to determine whether the three-phase low-oil equipment has a fault. Phase loss judgment: judging whether the hardware equipment of the monitoring system is faulty based on whether the collected signals of phases A, B, and C of the three-phase low-oil equipment are missing; The target deviation judgment comprises: respectively calculating the target deviations of the refractive index, hydrogen content and acetylene content of the three-phase low-oil equipment, selecting the value with the largest target deviation among the refractive index, hydrogen content and acetylene content, and transmitting the compensated signal of the selected value to the comparison judgment; The comparison and judgment is to compare the compensated signal of the selected amount with the corresponding reference optical signal to determine whether the three-phase low-oil equipment has a fault.

7. A fault diagnosis device for online monitoring of three-phase low-oil equipment, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: When the processor executes the program, it is used to execute the fault identification method for online monitoring of three-phase low-oil equipment as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, it is used to execute the fault identification method for online monitoring of three-phase low-oil equipment as described in any one of claims 1-5.

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