WO2024012091A1 - 火电储能电流互感器状态监测系统 - Google Patents

火电储能电流互感器状态监测系统 Download PDF

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Publication number
WO2024012091A1
WO2024012091A1 PCT/CN2023/098351 CN2023098351W WO2024012091A1 WO 2024012091 A1 WO2024012091 A1 WO 2024012091A1 CN 2023098351 W CN2023098351 W CN 2023098351W WO 2024012091 A1 WO2024012091 A1 WO 2024012091A1
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Prior art keywords
state
state quantity
detection result
detection
abnormal
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Ceased
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PCT/CN2023/098351
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English (en)
French (fr)
Inventor
王绍民
林松青
薛晓峰
潘喜良
徐挺进
姜滨
梁晓斌
张宗祯
戴海鹏
葛传军
常云潇
杜武荣
杨沛豪
兀鹏越
寇水潮
王小辉
燕云飞
郭昊
殷悦
李志鹏
张立松
王劼文
代本谦
李菁华
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Xian Thermal Power Research Institute Co Ltd
Huaneng Luoyuan Power Generation Co Ltd
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Xian Thermal Power Research Institute Co Ltd
Huaneng Luoyuan Power Generation Co Ltd
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Publication of WO2024012091A1 publication Critical patent/WO2024012091A1/zh
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R35/00—Testing or calibrating of apparatus covered by the other groups of this subclass
    • G01R35/02—Testing or calibrating of apparatus covered by the other groups of this subclass of auxiliary devices, e.g. of instrument transformers according to prescribed transformation ratio, phase angle, or wattage rating
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00—Measuring or testing not otherwise provided for
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the other groups of this subclass
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R27/00—Arrangements for measuring resistance, reactance, impedance, or electric characteristics derived therefrom
    • G01R27/02—Measuring real or complex resistance, reactance, impedance, or other two-pole characteristics derived therefrom, e.g. time constant
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R27/00—Arrangements for measuring resistance, reactance, impedance, or electric characteristics derived therefrom
    • G01R27/02—Measuring real or complex resistance, reactance, impedance, or other two-pole characteristics derived therefrom, e.g. time constant
    • G01R27/025—Measuring very high resistances, e.g. isolation resistances, i.e. megohm-meters
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
    • G01R31/1209—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing using acoustic measurements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
    • G01R31/1227—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials

Definitions

  • the present application relates to the technical field of power systems, and in particular to a thermal power energy storage current transformer status monitoring method.
  • GIS gas insulated switchgear
  • GIS includes current transformers.
  • Current transformers are instruments that convert large currents on the primary side into small currents on the secondary side based on the principle of electromagnetic induction. Due to the large disparity in current values in power generation, transformation and distribution lines in actual operation, , in order to facilitate measurement and control, it is necessary to realize the functions of current conversion and electrical isolation through current transformers. If the current transformer fails, it will seriously affect the normal operation of the GIS. Therefore, it is necessary to evaluate and detect the status of the current transformer.
  • the present application aims to solve, at least to a certain extent, one of the technical problems in the related art.
  • the first purpose of this application is to propose a thermal power energy storage current transformer status monitoring method (which can also be called a current transformer status quantity detection method in GIS of thermal power energy storage systems).
  • This method can improve GIS
  • the accuracy and comprehensiveness of medium current transformer state quantity detection reduces the risks existing during the operation of the current transformer and is conducive to ensuring the stable and safe normal operation of GIS. It also reduces the complexity of detection and improves detection efficiency.
  • the second purpose of this application is to propose a thermal power energy storage current transformer status monitoring system (which can also be called a current transformer status quantity detection system in the GIS of the thermal power energy storage system).
  • the third object of this application is to provide an electronic device.
  • the first embodiment of the present application is to propose a thermal power energy storage current transformer status monitoring method, which method includes the following steps:
  • the state quantity detection data of the current transformer to be detected is input into the trained state quantity evaluation model to generate a target detection result of the current transformer to be detected.
  • thermal power energy storage current transformer status monitoring method proposed in the above embodiments of the present application may also have the following additional technical features.
  • the detection result includes whether the state quantity is abnormal, the abnormal state corresponding to the state quantity, and the degree of deterioration corresponding to the state quantity, and the corresponding calculation is based on the detection result.
  • the deduction value of the state quantity includes: determining the basic deduction value corresponding to the detection result according to the degree of deterioration, obtaining the influence factor corresponding to the status quantity, and multiplying the basic deduction value by the influence factor to obtain the deduction value.
  • the method further includes: for the pre-calibrated state quantity, based on the pre-calibrated state quantity The detection results determine whether to perform partial discharge detection or infrared leak detection.
  • the corresponding state quantity is detected according to the preset judgment condition, and the detection result corresponding to each state quantity is obtained, including: when the state quantity is vibration and When abnormal sound occurs, if vibration and abnormal sound are detected inside the current transformer during operation, the detection result is determined to be abnormal; when the state quantity is discharge sound, if it is detected that the current transformer is operating If discharge sound occurs internally, the detection result is determined to be abnormal; when the state quantity is the equipment label state, if the equipment identification on the equipment label is not recognized, the detection result is determined to be abnormal; when the When the state quantity is the secondary winding resistance value, if the deviation between the secondary winding resistance value and the factory resistance value is detected to be outside the preset error range, the detection result is determined to be abnormal; when the state quantity is When the secondary winding insulation resistance value is detected, if it is detected that the secondary winding insulation resistance value is not greater than the preset resistance threshold, the detection result is determined to be abnormal.
  • detecting the corresponding state quantity according to the preset judgment condition and obtaining the detection result corresponding to each state quantity also includes: when the state quantity is SF 6 pressure gauge state, detect the appearance and indicated pressure value of the SF 6 pressure gauge; when detecting that the appearance of the SF 6 pressure gauge is damaged or there is oil leakage, determine that the detection result is the first abnormal state ; After detecting that the pressure value indicated by the SF 6 pressure gauge is outside the preset pressure range, determine that the detection result is a second abnormal state.
  • detecting the corresponding state quantity according to the preset judgment condition and obtaining the detection result corresponding to each state quantity also includes: when the state quantity is grounded When connected, check Detect the rust and looseness of the ground connection; when it is detected that the ground connection is rusted or the paint is peeling off, the detection result is determined to be the third abnormal state; when it is detected that the ground down conductor is loose, it is determined that all The detection result is the fourth abnormal state; when it is detected that the ground wire is disconnected, the detection result is determined to be the fifth abnormal state.
  • detecting the corresponding state quantity according to the preset judgment condition and obtaining the detection result corresponding to each state quantity also includes: when the state quantity is SF When the gas density is 6 , the gas supply interval time between two adjacent gas supply operations is detected; when the gas supply interval time is less than the first time threshold and greater than or equal to the second time threshold, the detection result is determined to be the sixth Abnormal state; when the gas replenishment interval is less than the second time threshold and greater than or equal to the third time threshold, the detection result is determined to be the seventh abnormal state; when the gas replenishment interval is less than the third time When the threshold is reached, the detection result is determined to be the eighth abnormal state.
  • detecting the corresponding state quantity according to the preset judgment condition and obtaining the detection result corresponding to each state quantity also includes: when the state quantity is SF 6 gas humidity, detect the trace water value during the operation of the current transformer; when the trace water value is greater than or equal to the first concentration threshold and less than the second concentration threshold, determine the detection result to be the ninth abnormal state; when When the micro water value is greater than or equal to the second concentration threshold and less than the third concentration threshold, the first growth rate of the micro water value is further determined. When the first growth rate is greater than the growth rate threshold, the first growth rate of the micro water value is determined.
  • the detection result is the tenth abnormal state; when the trace water value is greater than or equal to the third concentration threshold, a second growth rate of the trace water value is further determined, and when the second growth rate is greater than the growth rate threshold When, the detection result is determined to be the eleventh abnormal state.
  • detecting the corresponding state quantity according to the preset judgment condition and obtaining the detection result corresponding to each state quantity also includes: when the state quantity is SF 6 decomposition products, detect the H 2 S content and SO 2 content; when the H 2 S concentration value or the SO 2 concentration value is greater than the fourth concentration threshold and less than the fifth concentration threshold, the detection result is determined to be the twelfth abnormality state; when the H 2 S concentration value or the SO 2 concentration value is greater than or equal to the fifth concentration threshold, the detection result is determined to be the thirteenth abnormal state.
  • detecting the corresponding state quantity according to the preset judgment condition and obtaining the detection result corresponding to each state quantity also includes: when the state quantity is local During discharge, the UHF signal and ultrasonic signal during partial discharge are detected; the UHF signal is matched with the preset discharge detection spectrum, and the UHF signal is matched with the preset discharge detection spectrum.
  • the detection result is determined to be the fourteenth abnormal state; when the ultrasonic signal has a detection pattern of target partial discharge, the detection result is determined to be the fifteenth abnormal state; when the ultrasonic signal When there is a detection pattern of the target partial discharge, the measurement value of the ultrasonic signal is further obtained. If the measurement value is greater than the preset decibel value, the detection result is determined to be a sixteenth abnormal state.
  • the second embodiment of the present application also proposes a thermal power energy storage current transformer status monitoring system, including the following modules:
  • An acquisition module used to acquire multiple state quantities of the current transformer, and obtain the preset judgment conditions corresponding to each of the state quantities
  • a detection module configured to detect corresponding state quantities according to the preset judgment conditions and obtain detection results corresponding to each of the state quantities
  • a training module configured to calculate the deduction value of the corresponding state quantity according to the detection result, use the detection results corresponding to the multiple status quantities and the deduction values corresponding to the multiple status quantities as training data, and perform the preset
  • the state quantity evaluation model based on artificial intelligence is trained to obtain the trained state quantity evaluation model
  • a generation module configured to input the state quantity detection data of the current transformer to be detected into the trained state quantity evaluation model, and generate the target detection result of the current transformer to be detected.
  • the third embodiment of the present application also provides an electronic device on which a computer program is stored, including a memory, a processor, and a program stored on the memory and capable of running on the processor. Computer program.
  • the processor executes the computer program, the thermal power energy storage current transformer status monitoring method as described in any one of the above embodiments is implemented.
  • the present application can improve the accuracy and comprehensiveness of current transformer state quantity detection in the GIS of the thermal power energy storage system, and reduce the problems that exist during the operation of the current transformer. Risk reduction is conducive to ensuring the stable, safe and normal operation of GIS, and also reduces the complexity of detection and improves detection efficiency.
  • Figure 1 is a flow chart of a thermal power energy storage current transformer status monitoring method proposed in an embodiment of the present application
  • Figure 2 is a schematic structural diagram of a thermal power energy storage current transformer status monitoring system proposed in an embodiment of the present application.
  • Figure 1 is a flow chart of a thermal power energy storage current transformer status monitoring method proposed by an embodiment of the present application. As shown in Figure 1, the method includes the following steps:
  • Step S101 Acquire multiple state quantities of the current transformer in the GIS, and obtain the preset judgment conditions corresponding to each state quantity.
  • GIS Gas Insulated Switchgear
  • the thermal power energy storage system is the energy storage system of the thermal power plant.
  • GIS uses sulfur hexafluoride gas with better insulation performance as the insulation and arc extinguishing medium, so it can greatly reduce the size of the substation.
  • GIS can include: circuit breakers, isolation switches, grounding switches, voltage transformers, and current transformers. , lightning arresters, busbars, cable terminals and incoming and outgoing line casings and other sub-equipments.
  • the state quantity of the current transformer is a parameter that represents the operating state of the current transformer, and may include working parameters related to the operating state of the current transformer and equipment parameters of each component in the current transformer.
  • the preset judgment conditions are the detection methods and judgment basis used to detect whether the corresponding state quantity is abnormal. Since the current transformer needs to detect many parameters, this application obtains multiple state quantities and obtains the preset judgment conditions corresponding to each state quantity.
  • multiple state quantities of the current transformer are selected for detection based on the actual detection purpose, detection needs, differences between different GIS and other factors.
  • the state quantities After determining the state quantities, the historical operating experience and expert knowledge are obtained in advance. The judgment conditions corresponding to each state quantity determined by other methods.
  • the multiple status quantities of the current transformer obtained may include: vibration and abnormal sound, discharge sound, equipment label status, secondary winding resistance value, secondary winding insulation resistance value, sulfur hexafluoride (SF 6 ) Pressure gauge status, ground connection status, SF 6 gas density, SF 6 gas humidity, SF 6 decomposition products and partial discharge parameters, etc.
  • SF 6 sulfur hexafluoride
  • Step S102 Detect the corresponding state quantity according to the preset judgment conditions, and obtain the detection result corresponding to each state quantity.
  • the state quantity is normal according to whether the state quantity satisfies the corresponding preset judgment condition, so as to detect each state quantity. For example, when the state quantity satisfies the preset condition, it is judged that the state quantity is normal, and when the state quantity does not satisfy the The status quantity is judged to be abnormal under preset conditions.
  • the detection results obtained by detecting the corresponding state quantity according to the preset judgment conditions may include whether the state quantity is abnormal, the abnormal state corresponding to the state quantity, and the degree of degradation corresponding to the state quantity.
  • Different abnormal states represent different degrees of abnormality.
  • the degree of abnormality is represented by the degree of deterioration.
  • One abnormal state can correspond to one or more degrees of deterioration.
  • the detection result is determined to be abnormal.
  • the judgment condition in this example is whether vibration and abnormal sound occur inside the current transformer during operation.
  • the subsequent explanation of the judgment condition can refer to this example, and will not be repeated in subsequent examples.
  • the vibration of the current transformer can be detected through a vibration sensor, and whether vibration occurs inside the current transformer during operation can be determined based on whether the vibration sensor outputs a vibration displacement signal.
  • the sound inside the current transformer can be detected with a sound tester.
  • the volume of the sound inside the current transformer is greater than the preset number of decibels, it will be determined that there is an abnormal sound inside the current transformer during operation, and then in the When vibration and abnormal sound are detected inside the current transformer during operation, the detection result is determined to be abnormal.
  • the detection result is determined to be abnormal.
  • the signal characteristics of the discharge sound can be determined through a large amount of research and analysis in advance, and then the acoustic emission detection device is used to detect the sound that occurs inside the current transformer during operation. If the signal characteristics of the discharge sound are the same as those detected in advance, If the sound signal is detected, it is determined that a discharge sound occurs inside the current transformer during operation, and then the detection result is determined to be abnormal.
  • the detection result is determined to be abnormal.
  • the equipment identification on the current transformer equipment label may be incomplete or blurred due to paint peeling off or damage during long-term operation, resulting in failure to identify or incorrect identification, so the equipment needs to be inspected.
  • the integrity of the identification is recognized.
  • the current equipment identification can be compared with the initial identification through manual comparison, or the image of the equipment identification can be collected through a camera device, and then the collected image can be identified through image recognition technology to determine whether it can be identified.
  • the device identification or judging whether the matching degree between the identified device identification and the initial identification is greater than the threshold. If the matching degree is low, it is deemed that the device identification has not been recognized.
  • the detection result is determined to be abnormal.
  • the detection result is determined to be abnormal.
  • the preset error range is the allowable resistance error range. If the deviation between the secondary winding resistance value and the factory resistance value is detected to be outside the preset error range, it means that the secondary winding resistance value is different from the factory resistance value. The deviation is obvious, and the detection result is determined to be abnormal.
  • the preset resistance threshold can be the minimum safe resistance value required to ensure the safe operation of the current transformer.
  • the preset resistance threshold is 2M ⁇ . If it is detected that the secondary winding insulation resistance is not greater than 2M ⁇ , then Confirm that the detection result is abnormal.
  • the state quantity is the state of the SF 6 pressure gauge
  • the appearance and indicated pressure value of the SF 6 pressure gauge are detected.
  • the detection result is the first abnormal state.
  • the detection result is determined to be the second abnormality. state.
  • the status of the SF 6 pressure gauge is detected from two perspectives: the appearance of the pressure gauge and the indicated pressure value. If damage to the appearance is detected or oil leakage is detected on the housing of the pressure gauge through the oil quantity detection equipment, Then it is determined that the detection result is the first abnormal state, that is, the first abnormal state refers to the abnormal state in which the appearance of the SF 6 pressure gauge is damaged or oil leaks. The subsequent explanation of the abnormal state can refer to this example, and will not be repeated in subsequent examples. . Further, detect whether the pressure value indicated by the SF 6 pressure gauge is outside the preset pressure range, where the preset pressure range is the predetermined pressure value range under the normal operating state of the current transformer. If the pressure value indicated by the pressure gauge If the pressure value exceeds this range, it means that the pressure gauge indication is abnormal, and the detection result is determined to be the second abnormal state.
  • this application can determine the degree of degradation corresponding to the abnormal state quantity.
  • the degree of degradation can be set to four levels, from low to high. I to IV, the degree of deterioration corresponding to each abnormal state quantity can be determined in advance by combining historical experience knowledge and relevant detection regulations. After detecting an abnormal state quantity, the corresponding degree of degradation can be obtained. For example, if a current mutual inductance is detected If vibration and abnormal sound occur inside the device during operation, it can be determined that the degree of deterioration corresponding to the abnormality is III.
  • the same state quantity can have multiple abnormal states, and each abnormal state corresponds to a different degree of degradation. For example, in the above embodiment, the degree of deterioration corresponding to the first abnormal state is III, and the degree of deterioration corresponding to the second abnormal state is IV.
  • the state quantity is the ground connection state
  • the rust and looseness of the ground connection are detected.
  • the detection result is determined to be the third abnormal state.
  • the detection result is determined to be the fourth abnormal state.
  • the detection result is determined to be the fifth abnormal state.
  • the detection result is determined to be the third abnormal state. Further, the grounding down conductor is detected.
  • the grounding down conductor is a metal conductor that connects the electrical equipment and the ground body. If it is detected that the grounding down conductor is loose, the detection result is determined to be the fourth abnormal state. If the grounding down conductor is detected, has fallen off, that is, the equipment originally connected through the ground down lead has been disconnected from the ground, then the detection result is determined to be the fifth abnormal state.
  • the degree of deterioration corresponding to the third abnormal state to the fifth abnormal state gradually increases, for example, from II to IV respectively.
  • the gas supply interval time between two adjacent gas supply operations is detected.
  • the detection result is determined to be the sixth abnormal state;
  • the detection result is determined to be the seventh abnormal state;
  • the detection result is determined to be the eighth abnormal state.
  • the replenishment time between each two gas replenishment operations can be adjusted.
  • Check the gas interval Measurement In this example, it is assumed that the first time threshold is two years, the second time threshold is one year, and the third time threshold is half a year. According to the interval of obtaining gas replenishment, when the interval is not less than one year and less than two years, it can be determined The recognition result is the sixth abnormal state. When the interval is not less than half a year and less than one year, the recognition result can be determined to be the seventh abnormal state. When the interval is less than half a year, the recognition result can be determined to be the eighth abnormal state.
  • the degree of deterioration corresponding to the sixth abnormal state to the eighth abnormal state gradually increases, and due to the long air supply interval, the same abnormal state can be further divided according to the actual interval time.
  • determine the degree of deterioration that is, in this example, one abnormal state can correspond to multiple degrees of deterioration.
  • the degree of deterioration can be from I to II.
  • the degree of deterioration is I.
  • the degree of deterioration can be I.
  • the degree of deterioration is II.
  • the detection result when the state quantity is SF 6 gas humidity, the micro-water value during operation of the current transformer is detected, and when the micro-water value is greater than or equal to the first concentration threshold and less than the second concentration threshold, the detection result is determined It is the ninth abnormal state; when the trace water value is greater than or equal to the second concentration threshold and less than the third concentration threshold, the first growth rate of the trace water value is further determined. When the first growth rate is greater than the growth rate threshold, the detection result is determined to be The tenth abnormal state; when the trace water value is greater than or equal to the third concentration threshold, the second growth rate of the trace water value is further determined. When the second growth rate is greater than the growth rate threshold, the detection result is determined to be the eleventh abnormal state.
  • the first concentration threshold is 300 ⁇ L/L
  • the second concentration threshold is 500 ⁇ L/L
  • the third concentration threshold is 800 ⁇ L/L
  • the growth rate threshold is set to 15%.
  • the trace water concentration when the trace water concentration is greater than 300 ⁇ L/L and less than 500 ⁇ L/L, it can be determined that the identification result is the ninth abnormal state.
  • the trace water concentration is greater than 500 ⁇ L/L and less than 800 ⁇ L/L, it is further determined that During the operation of the current transformer, the growth rate of the microwater value concentration is determined by comparing the microwater value concentration at different times.
  • the identification result is the tenth abnormal state.
  • the microwater value concentration is greater than 800 ⁇ L/L, and the growth rate of the microwater value concentration is determined to be 30% in the above manner, which is greater than the growth rate threshold, the recognition result can be determined to be the eleventh abnormal state.
  • the H 2 S content and the SO 2 content are detected; when the H 2 S concentration value or the SO 2 concentration value is greater than the fourth concentration threshold and less than the fifth concentration When the threshold value is reached, the detection result is determined to be the twelfth abnormal state; when the H 2 S concentration value or SO 2 concentration value is greater than or equal to the fifth concentration threshold value, the detection result is determined to be the thirteenth abnormal state.
  • the H 2 S content and the SO 2 content are detected separately, assuming that the fourth concentration threshold is 1 ⁇ L/L and the fifth concentration threshold is 2 ⁇ L/L.
  • the detection result is determined to be the twelfth abnormal state, in which the H 2 S concentration value is not less than 1 ⁇ L. /L, but when the SO 2 concentration value is not less than 1 ⁇ L/L, but less than 2 ⁇ L/L, the degree of deterioration is III to IV.
  • the degree of deterioration is II to III.
  • the detection is determined.
  • the result is the thirteenth abnormal state, in which the degree of deterioration is IV when the H 2 S concentration value is not less than 2 ⁇ L/L, and the degree of deterioration is III when the SO 2 concentration value is not less than 2 ⁇ L/L.
  • the UHF signal and ultrasonic signal during partial discharge are detected; the UHF signal is matched with the preset discharge detection spectrum, and the UHF signal is matched with the preset discharge detection spectrum.
  • the preset discharge detection patterns do not match, the detection result is determined to be the fourteenth abnormal state; when the ultrasonic signal has the detection pattern of the target partial discharge, the detection result is determined to be the fifteenth abnormal state; when the ultrasonic signal has the target partial discharge, the detection result is determined to be the fifteenth abnormal state.
  • the measurement value of the ultrasonic signal is further obtained. If the measurement value is greater than the preset decibel value, the detection result is determined to be the sixteenth abnormal state.
  • UHF signals and ultrasonic signals are detected separately.
  • the preset discharge detection pattern is the pattern detected by similar equipment under the same conditions.
  • the detection pattern of target partial discharge is typical in this field. Detection pattern of partial discharge.
  • UHF signal does not match the preset discharge detection spectrum, that is, there is a significant difference from the spectrum detected by similar equipment under the same conditions.
  • the detection result is the fourteenth abnormal state; when the UHF signal is detected to have a typical partial discharge detection pattern, the detection result is determined to be the fifteenth abnormal state; when the ultrasonic signal is detected to have a typical partial discharge detection pattern and the ultrasonic signal
  • the measured value is greater than the preset decibel value, for example, when it is greater than 10dB, the detection result is determined to be the sixteenth abnormal state.
  • the preset decibel value in this example can be determined in advance based on a large number of experiments and historical experience.
  • the sealing, rust state and damage state of the mechanism box can be detected respectively.
  • the sealing performance can be tested by the dry air method or the tracer gas method.
  • the test result is determined to be the seventeenth abnormal state.
  • a water level sensor or a humidity sensor can be used to detect whether there is accumulated water in the mechanism box.
  • the humidity in the mechanism box under normal conditions can be determined in advance. The value is 10%.
  • the recognition result can be determined to be the eighteenth abnormal state.
  • the identification result can be determined to be the nineteenth abnormal state.
  • the damage state it is detected whether the heater and other devices in the mechanism box are damaged. If the device is damaged, the recognition result can be determined to be the twentieth abnormal state.
  • the degree of deterioration of the seventeenth abnormal state is I
  • the degree of deterioration of the eighteenth abnormal state is IV
  • the degree of deterioration of the nineteenth and twentieth abnormal states is II.
  • the state quantity is the working state of the control auxiliary circuit components
  • the detection result is determined to be abnormal.
  • the status quantity is a published family defect or equipment fault information of the same manufacturer or the same model of equipment or fault information of equipment of the same period
  • the test result is determined to be the twenty-first abnormal state.
  • the test result is determined to be the twenty-second abnormal state.
  • the twenty-first abnormal state The corresponding degree of deterioration is II, and the degree of deterioration corresponding to the twenty-second abnormal state is IV.
  • the state quantity is the state of the SF 6 gas density relay
  • it can be detected by referring to the detection method of the SF 6 pressure gauge state, and the installation position of the density relay can also be further detected. If the density installed outdoors is detected If the relay is not equipped with a rain cover, the detection result is determined to be abnormal.
  • this application detects multiple state quantities based on preset judgment conditions, and obtains the detection results corresponding to each state quantity.
  • the detection result corresponding to the above-mentioned state quantity it also includes: for the pre-calibrated state quantity, determining whether to perform partial discharge detection or infrared leak detection based on the detection result of the pre-calibrated state quantity.
  • the pre-calibrated state quantity is a predetermined state quantity that needs to be further detected when an abnormality occurs. For example, for vibrations and abnormal sounds, external factors should be further excluded when detecting abnormal vibrations; for discharge sounds, partial discharge detection should be performed when suspected internal discharge sounds occur; for pressure gauges, when abnormal pressure gauge indications are detected When the pressure gauge indicates an abnormality, the reason for the abnormal indication of the pressure gauge should be confirmed. If the reason is not the gauge, infrared leak detection needs to be carried out to find the location of the leak; for SF 6 gas density, when an abnormality in the gas replenishment interval is detected, infrared leak detection should be carried out; for SF 6 decomposition products, partial discharge detection should be carried out when abnormal decomposition products are detected. Therefore, through partial discharge detection or infrared leak detection, the location of the abnormality and the cause of the abnormality can be further determined, which is beneficial to subsequent elimination of the abnormality.
  • Step S103 Calculate the deduction value of the corresponding state quantity based on the detection results, use the detection results corresponding to the multiple state quantities and the deduction values corresponding to the multiple state quantities as training data, and evaluate the preset artificial intelligence-based state quantity.
  • the model is trained and the state quantity evaluation model after training is obtained.
  • deduction value can reflect the severity of the abnormality and the degree of impact on the current transformer. The higher the deduction value It means the greater the harm to the current transformer.
  • calculating the deduction value of the corresponding state quantity according to the detection result includes: determining the basic deduction value corresponding to the detection result according to the degree of deterioration, and obtaining the influence factor corresponding to the abnormal state quantity, and base The base deduction value is multiplied by the impact factor to obtain the deduction value.
  • mapping relationship between the basic deduction value and the degree of deterioration.
  • the mapping relationship is determined in advance. After the degree of deterioration in the detection result is determined, the corresponding basic deduction value can be directly obtained.
  • the basic deduction values corresponding to deterioration degrees I to IV are 2, 4, 8 and 10 respectively.
  • the influence factor can be approximately regarded as the weight of the state quantity affecting the state of the current transformer. It can be understood that different state quantities have different effects on the state evaluation of the current transformer. For example, the abnormality of the pressure gauge indication is more harmful than the equipment label. Fuzzy, therefore, this application sets a corresponding impact factor for each state quantity in advance. After detecting the abnormality of the state quantity, the corresponding basic deduction value is determined based on the degree of deterioration determined by the detection, and the corresponding impact of the state quantity is obtained. Factor, multiply the basic deduction value by the impact factor to get the deduction value.
  • the preset state quantity evaluation model can be various types of neural network models. That is, this application is based on artificial intelligence technology and generates the evaluation results of the current transformer state quantity through the trained neural network model. Among them, the type of the preset state quantity detection model can be set according to actual needs. For example, the Long Short-Term Memory Artificial Neural Network (Long Short-Term Memory, referred to as LSTM) model is selected as the state quantity evaluation model.
  • LSTM Long Short-Term Memory Artificial Neural Network
  • the detection results and corresponding deduction values corresponding to each detected state quantity are used as training data, and after being divided into a training set, a verification set, and a test set according to a preset ratio,
  • the model is trained through the training data in the training set.
  • the specific training process can refer to the training method of the neural network model in related technologies, including various preprocessing such as binarization, smoothing and filtering of the data, and then the processed
  • the data is used for feature extraction and selection, and the weight of each layer parameter in the state quantity detection model is trained. For example, the weight of each state quantity evaluation in the state quantity evaluation model is determined based on the influence factors of each state quantity and the degree of degradation corresponding to each abnormal state. And by defining the loss function and using the gradient descent method to train the state quantity detection model. After the training is completed, the model can also be tested with the data in the test set to verify whether its prediction accuracy meets the requirements.
  • Step S104 Input the state quantity detection data of the current transformer to be detected into the trained state quantity evaluation model to generate a target detection result of the current transformer to be detected.
  • the target detection result includes the overall detection result of the current transformer in the GIS.
  • the overall detection result may include the final score corresponding to the current transformer after deducting the deduction value corresponding to the abnormal state quantity, and the evaluation level to which the final score belongs.
  • the target detection results may also include the deduction value and evaluation results of each state quantity of the current transformer.
  • the state quantity detection data of the current transformer to be detected is input to the trained state quantity evaluation model, where the state quantity detection data can be each state.
  • the detection result data of the quantity can also be the actual detection number value of the state quantity.
  • the actual detection number value is input into the state quantity evaluation model, and the evaluation model detects whether the state quantity is abnormal based on the judgment conditions.
  • the state quantity evaluation model evaluates the input data Perform detection and evaluation and output the target detection results of the current transformer to be detected.
  • the state quantity evaluation model can output an evaluation table for the current transformer.
  • the evaluation table includes the final score and evaluation grade of the current current transformer, and subsequently lists the deduction points for each state quantity. values and evaluation results.
  • This score is the overall evaluation score of the current transformer, which can reflect the status of the current transformer in the GIS.
  • the evaluation grade corresponding to the evaluation score is predetermined based on historical operating experience. For example, 90 to 100 points is excellent, 70 to 90 is good, etc., and then the evaluation grade is determined based on the overall evaluation score.
  • the test results of each status quantity, including deduction values and evaluation values, are summarized and listed in the evaluation details section of the evaluation form.
  • the thermal power energy storage current transformer status monitoring method detects the current transformer status quantity from two perspectives: the overall and specific details.
  • the state quantity evaluation model completed through training can be more quickly , conveniently generate the overall detection results of the current transformer, and summarize the detection results of each state quantity, improve the accuracy, comprehensiveness and detection efficiency of the current transformer state quantity detection, and reduce the labor costs required for detection, facilitating timely Maintaining current transformers reduces potential risks during the operation of current transformers, which is beneficial to ensuring the normal operation of GIS.
  • FIG. 2 is a structural schematic diagram of a thermal power energy storage current transformer status monitoring system proposed in the embodiment of this application.
  • the system includes an acquisition module 100, a detection module 200, a training module 300 and a generation module 400.
  • the acquisition module 100 is used to acquire multiple state quantities of the current transformer, and acquire the preset judgment conditions corresponding to each state quantity.
  • the detection module 200 is used to detect corresponding state quantities according to preset judgment conditions and obtain detection results corresponding to each state quantity.
  • the training module 300 is used to calculate the deduction value of the corresponding state quantity according to the detection result, use the detection results corresponding to the multiple state quantities and the deduction values corresponding to the multiple state quantities as training data, and perform the preset artificial intelligence-based
  • the state quantity evaluation model is trained to obtain the trained state quantity evaluation model.
  • the generation module 400 is used to input the state quantity detection data of the current transformer to be detected into the trained state quantity evaluation model, and generate the target detection result of the current transformer to be detected.
  • the detection results include whether the state quantity is abnormal, the abnormal state corresponding to the state quantity, and the degree of degradation corresponding to the state quantity.
  • the training module 300 is specifically configured to: determine the detection result corresponding to the degree of deterioration.
  • the basic deduction value is obtained, and the influence factor corresponding to the state quantity is obtained.
  • the basic deduction value is multiplied by the influence factor to obtain the deduction value.
  • the detection module 200 is also configured to: for a pre-calibrated state quantity, determine whether to perform partial discharge detection or infrared leak detection based on the detection result of the pre-calibrated state quantity.
  • the detection module 200 is also configured to: when the state quantity is vibration and abnormal sound, if it is detected that vibration and abnormal sound occur inside the current transformer during operation, determine that the detection result is abnormal; when When the status quantity is the discharge sound, if a discharge sound is detected inside the current transformer during operation, the detection result is determined to be abnormal; when the status quantity is the equipment label status, if the equipment identification on the equipment label is not recognized, the detection is determined.
  • the result is abnormal; when the state quantity is the secondary winding resistance value, if the deviation between the secondary winding resistance value and the factory resistance value is detected to be outside the preset error range, the detection result is determined to be abnormal; when the state quantity is the secondary winding resistance value, the detection result is abnormal.
  • the secondary winding insulation resistance value is detected, if it is detected that the secondary winding insulation resistance value is not greater than the preset resistance threshold, the detection result is determined to be abnormal.
  • the detection module 200 is also used to: detect the appearance and indicated pressure value of the SF 6 pressure gauge when the state quantity is the SF 6 pressure gauge state; when the SF 6 pressure is detected When the appearance of the gauge is damaged or there is oil leakage, the detection result is determined to be the first abnormal state; when the pressure value indicated by the SF 6 pressure gauge is detected to be outside the preset pressure range, the detection result is determined to be the second abnormal state.
  • the detection module 200 is also used to: detect the rust and looseness of the ground connection when the state quantity is the ground connection state; and detect the presence of corrosion or paint peeling in the ground connection. , the detection result is determined to be the third abnormal state; when it is detected that the grounding down conductor is loose, the detection result is determined to be the fourth abnormal state; when the grounding wire is detected to be off, the detection result is determined to be the fifth abnormal state.
  • the detection module 200 is also used to: when the state quantity is SF 6 gas density, detect the gas supply interval time between two adjacent gas supply operations; during the gas supply interval When the time is less than the first time threshold and greater than or equal to the second time threshold, the detection result is determined to be the sixth abnormal state; when the gas refill interval is less than the second time threshold and greater than or equal to the third time threshold, the detection result is determined to be the seventh abnormal state. state; when the gas refill interval is less than the third time threshold, the detection result is determined to be the eighth abnormal state.
  • the detection module 200 is also used to: detect the micro-water value during the operation of the current transformer when the state quantity is SF 6 gas humidity; when the micro-water value is greater than or equal to the first concentration threshold and less than the second concentration threshold, the detection result is determined to be the ninth abnormal state; when the trace water value is greater than or equal to the second concentration threshold and less than the third concentration threshold, the first growth rate of the trace water value is further determined, and in the first When the growth rate is greater than the growth rate threshold, the detection result is determined to be the tenth abnormal state; when the trace water value is greater than or equal to the third concentration threshold, the second growth rate of the trace water value is further determined, and when the second growth rate is greater than the growth rate threshold , confirm that the detection result is the eleventh abnormal state.
  • the detection module 200 is also used to: detect the H 2 S content and SO 2 content when the state quantity is SF 6 decomposition product; when the H 2 S concentration value or SO 2 When the concentration value is greater than the fourth concentration threshold and less than the fifth concentration threshold, the detection result is determined to be the twelfth abnormal state; when the H 2 S concentration value or the SO 2 concentration value is greater than or equal to the fifth concentration threshold, the detection result is determined to be the tenth abnormal state.
  • the detection module 200 is also used to: detect the H 2 S content and SO 2 content when the state quantity is SF 6 decomposition product; when the H 2 S concentration value or SO 2 When the concentration value is greater than the fourth concentration threshold and less than the fifth concentration threshold, the detection result is determined to be the twelfth abnormal state; when the H 2 S concentration value or the SO 2 concentration value is greater than or equal to the fifth concentration threshold, the detection result is determined to be the tenth abnormal state.
  • the detection module 200 is also used to: when the state quantity is partial discharge, detect the UHF signal and ultrasonic signal during partial discharge; compare the UHF signal with the preset The discharge detection pattern is matched. When the UHF signal does not match the preset discharge detection pattern, the detection result is determined to be the fourteenth abnormal state; when the UHF signal has the detection pattern of the target partial discharge, the detection result is determined to be Fifteenth abnormal state; in ultrasound When the signal contains the detection pattern of the target partial discharge, the measurement value of the ultrasonic signal is further obtained. If the measurement value is greater than the preset decibel value, the detection result is determined to be the sixteenth abnormal state.
  • the thermal power energy storage current transformer status monitoring system in the embodiment of the present application detects the status quantity of the current transformer from two perspectives: the overall and the specific details.
  • the state quantity evaluation model completed through training can be faster and more convenient. Generate the overall detection results of the current transformer, and summarize the detection results of each state quantity, improve the accuracy, comprehensiveness and detection efficiency of the current transformer state quantity detection, and reduce the labor costs required for detection, so as to facilitate timely detection of current Maintenance of transformers reduces potential risks during operation of current transformers, which is beneficial to ensuring the normal operation of GIS.
  • this application also proposes an electronic device.
  • the electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor.
  • the processor executes the When the computer program is described, the thermal power energy storage current transformer status monitoring method as described in any of the above embodiments is implemented.
  • references to the terms “one embodiment,” “some embodiments,” “an example,” “specific examples,” or “some examples” or the like means that specific features are described in connection with the embodiment or example. , structures, materials or features are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the specific features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, those skilled in the art may combine and combine different embodiments or examples and features of different embodiments or examples described in this specification unless they are inconsistent with each other.
  • first and second are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of indicated technical features. Therefore, features defined as “first” and “second” may explicitly or implicitly include at least one of these features.
  • “plurality” means at least two, such as two, three, etc., unless otherwise expressly and specifically limited.
  • a "computer-readable medium” may be any medium that can contain, store, communicate, propagate, or transmit a program to A device for or used in conjunction with an instruction execution system, device or device.
  • Non-exhaustive list of computer readable media include the following: electrical connections with one or more wires (electronic device), portable computer disk cartridges (magnetic device), random access memory (RAM), Read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable compact disc read-only memory (CDROM).
  • the computer-readable medium may even be paper or other suitable medium on which the program may be printed, as the paper or other medium may be optically scanned, for example, and subsequently edited, interpreted, or otherwise suitable as necessary. process to obtain the program electronically and then store it in computer memory.
  • various parts of the present application can be implemented in hardware, software, firmware, or a combination thereof.
  • various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system.
  • a suitable instruction execution system For example, if it is implemented in hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: discrete logic gate circuits with logic functions for implementing data signals; Logic circuits, application specific integrated circuits with suitable combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), etc.
  • the program can be stored in a computer-readable storage medium.
  • the program can be stored in a computer-readable storage medium.
  • each functional unit in various embodiments of the present application can be integrated into a processing module, or each unit can exist physically alone, or two or more units can be integrated into one module.
  • the above integrated modules can be implemented in the form of hardware or software function modules. If the integrated module is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.
  • the storage media mentioned above can be read-only memory, magnetic disks or optical disks, etc.

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Abstract

本申请提出了一种火电储能电流互感器状态监测方法，该方法包括：获取GIS中电流互感器的多个状态量，并获取每个状态量对应的预设判断条件；根据预设判断条件对对应的状态量进行检测，获得每个状态量对应的检测结果；根据检测结果计算对应的状态量的扣分值，将多个状态量对应的检测结果和多个状态量对应的扣分值作为训练数据，对预先设置的基于人工智能的状态量评价模型进行训练；将待检测的电流互感器的状态量检测数据输入至训练完成的状态量评价模型，生成待检测电流互感器的目标检测结果。

Description

火电储能电流互感器状态监测系统 技术领域
本申请涉及电力系统技术领域，尤其涉及一种火电储能电流互感器状态监测方法。
背景技术
目前，在大规模的发电站中，比如，大装机容量的火力发电站中，通常需要设置气体绝缘开关设备(Gas Insulated Switchgear，简称GIS)来缩小发电站的体积，实现小型化，因此GIS在高压和超高压领等领域被广泛应用。通常GIS为免维护设计，故障率较低，但GIS内部存在的一些缺陷，最初可能无害，也不容易发现，随着运行年限的延长，在多种因素的影响下，会造成故障的持续累计或发展，影响设备的安全运行。
其中，GIS包括电流互感器，电流互感器是依据电磁感应原理将一次侧大电流转换成二次侧小电流的仪器，由于实际运行中发电、变电和配电等线路中电流值悬殊较大，为便于测量和控制需要通过电流互感器实现电流变换和电气隔离的功能。若电流互感器发生故障将会严重影响GIS的正常运行，因此需要对电流互感器的状态进行评价和检测。
相关技术中，通常是通过人工的方式对电流互感器的各个部件、参数和整体性能进行检测。然而，实际应用中，由于电流互感器包含的部件和待检测的参数数量较多，上述检测方式得到的检测结果可能存在误差，无法对电流互感器进行全面的检测，且检测过程较为复杂，检测效率较低。
发明内容
本申请旨在至少在一定程度上解决相关技术中的技术问题之一。
为此，本申请的第一个目的在于提出一种火电储能电流互感器状态监测方法(也可称为火电储能系统的GIS之中电流互感器状态量检测方法)，该方法可以提高GIS中电流互感器状态量检测的准确性和全面性，降低电流互感器运行过程中存在的风险，有利于保障GIS的稳定、安全的正常运行，并且，降低了检测的复杂程度，提高检测效率。
本申请的第二个目的在于提出一种火电储能电流互感器状态监测系统(也可称为火电储能系统的GIS之中电流互感器状态量检测系统)。
本申请的第三个目的在于提出一种电子设备。
为达上述目的，本申请的第一方面实施例在于提出一种火电储能电流互感器状态监测方法，该方法包括以下步骤：
获取GIS中电流互感器的多个状态量，并获取每个所述状态量对应的预设判断条件；
根据所述预设判断条件对对应的状态量进行检测，获得每个所述状态量对应的检测结果；
根据所述检测结果计算对应的状态量的扣分值，将所述多个状态量对应的检测结果和所述多个状态量对应的扣分值作为训练数据，对预先设置的基于人工智能的状态量评价模型进行训练，获得训练完成的状态量评价模型；
将待检测的电流互感器的状态量检测数据输入至所述训练完成的状态量评价模型，生成所述待检测电流互感器的目标检测结果。
另外，根据本申请上述实施例提出的火电储能电流互感器状态监测方法，还可以具有如下附加的技术特征。
可选地，在本申请的一个实施例中，检测结果包括所述状态量是否异常、所述状态量对应的异常状态和所述状态量对应的劣化程度，所述根据所述检测结果计算对应的状态量的扣分值，包括：根据所述劣化程度确定所述检测结果对应的基础扣分值，并获取所述状态量对应的影响因子，将所述基础扣分值乘以所述影响因子得到所述扣分值。
可选地，在本申请的一个实施例中，在所述获得每个所述状态量对应的检测结果之后，所述方法还包括：对于预先标定的状态量，根据所述预先标定的状态量的检测结果确定是否执行局部放电检测或红外检漏。
可选地，在本申请的一个实施例中，根据所述预设判断条件对对应的状态量进行检测，获得每个所述状态量对应的检测结果，包括：当所述状态量为振动和异常声响时，若检测出所述电流互感器运行中内部出现振动和异常声响，则确定所述检测结果为异常；当所述状态量为放电声时，若检测出所述电流互感器运行中内部出现放电声，则确定所述检测结果为异常；当所述状态量为设备标牌状态时，若未识别出所述设备标牌上的设备标识，则确定所述检测结果为异常；当所述状态量为二次绕组电阻值时，若检测出所述二次绕组电阻值与出厂电阻值的偏差在预设的误差范围之外，则确定所述检测结果为异常；当所述状态量为二次绕组绝缘电阻值时，若检测出所述为二次绕组绝缘电阻值不大于预设的阻值阈值，则确定所述检测结果为异常。
可选地，在本申请的一个实施例中，根据所述预设判断条件对对应的状态量进行检测，获得每个所述状态量对应的检测结果，还包括：当所述状态量为SF6压力表状态时，检测所述SF6压力表的外观和指示的压力值；在检测出所述SF6压力表的外观破损或存在渗漏油时，确定所述检测结果为第一异常状态；在检测出所述SF6压力表指示的压力值在预设的压力范围之外，确定所述检测结果为第二异常状态。
可选地，在本申请的一个实施例中，根据所述预设判断条件对对应的状态量进行检测，获得每个所述状态量对应的检测结果，还包括：当所述状态量为接地连接状态时，检 测所述接地连接的锈蚀状况和松动状况；在检测出所述接地连接存在锈蚀或油漆剥落时，确定所述检测结果为第三异常状态；在检测出接地引下线发生松动时，确定所述检测结果为第四异常状态；在检测出接地线脱落时，确定所述检测结果为第五异常状态。
可选地，在本申请的一个实施例中，根据所述预设判断条件对对应的状态量进行检测，获得每个所述状态量对应的检测结果，还包括：当所述状态量为SF6气体密度时，检测相邻两次补气操作之间的补气间隔时间；在所述补气间隔时间小于第一时间阈值且大于等于第二时间阈值时，确定所述检测结果为第六异常状态；在所述补气间隔时间小于所述第二时间阈值且大于等于第三时间阈值时，确定所述检测结果为第七异常状态；在所述补气间隔时间小于所述第三时间阈值时，确定所述检测结果为第八异常状态。
可选地，在本申请的一个实施例中，根据所述预设判断条件对对应的状态量进行检测，获得每个所述状态量对应的检测结果，还包括：当所述状态量为SF6气体湿度时，检测所述电流互感器运行中的微水值；在所述微水值大于等于第一浓度阈值且小于第二浓度阈值时，确定所述检测结果为第九异常状态；在所述微水值大于等于所述第二浓度阈值且小于第三浓度阈值时，进一步确定所述微水值的第一增长速度，在所述第一增长速度大于增长速度阈值时，确定所述检测结果为第十异常状态；在所述微水值大于等于所述第三浓度阈值时，进一步确定所述微水值的第二增长速度，在所述第二增长速度大于所述增长速度阈值时，确定所述检测结果为第十一异常状态。
可选地，在本申请的一个实施例中，根据所述预设判断条件对对应的状态量进行检测，获得每个所述状态量对应的检测结果，还包括：当所述状态量为SF6分解物时，检测H2S含量和SO2的含量；在H2S浓度值或者SO2浓度值大于第四浓度阈值且小于第五浓度阈值时，确定所述检测结果为第十二异常状态；在所述H2S浓度值或者所述SO2浓度值大于等于所述第五浓度阈值时，确定所述检测结果为第十三异常状态。
可选地，在本申请的一个实施例中，根据所述预设判断条件对对应的状态量进行检测，获得每个所述状态量对应的检测结果，还包括：当所述状态量为局部放电时，检测所述局部放电时的特高频信号和超声波信号；将所述特高频信号与预设的放电检测图谱进行匹配，在所述特高频信号与所述预设的放电检测图谱不匹配时，确定所述检测结果为第十四异常状态；在所述特高频信号存在目标局部放电的检测图谱时，确定所述检测结果为第十五异常状态；在所述超声波信号存在所述目标局部放电的检测图谱时，进一步获取所述超声波信号的测量值，若所述测量值大于预设分贝值，则确定所述检测结果为第十六异常状态。
为达上述目的，本申请的第二方面实施例还提出了一种火电储能电流互感器状态监测系统，包括以下模块:
获取模块，用于获取电流互感器的多个状态量，并获取每个所述状态量对应的预设判断条件；
检测模块，用于根据所述预设判断条件对对应的状态量进行检测，获得每个所述状态量对应的检测结果；
训练模块，用于根据所述检测结果计算对应的状态量的扣分值，将所述多个状态量对应的检测结果和所述多个状态量对应的扣分值作为训练数据，对预先设置的基于人工智能的状态量评价模型进行训练，获得训练完成的状态量评价模型；
生成模块，用于将待检测的电流互感器的状态量检测数据输入至所述训练完成的状态量评价模型，生成所述待检测电流互感器的目标检测结果。
为了实现上述实施例，本申请第三方面实施例还提出了一种电子设备，其上存储有计算机程序，包括存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时，实现如上述实施例中任一项所述的火电储能电流互感器状态监测方法。
本申请的实施例提供的技术方案至少带来以下有益效果：本申请可以提高火电储能系统的GIS中的电流互感器状态量检测的准确性和全面性，降低电流互感器运行过程中存在的风险，有利于保障GIS的稳定、安全的正常运行，并且，降低了检测的复杂程度，提高检测效率。
本发明附加的方面和优点将在下面的描述中部分给出，部分将从下面的描述中变得明显，或通过本发明的实践了解到。
附图说明
本申请上述的和/或附加的方面和优点从下面结合附图对实施例的描述中将变得明显和容易理解，其中：
图1为本申请实施例提出的一种火电储能电流互感器状态监测方法的流程图；
图2为本申请实施例提出的一种火电储能电流互感器状态监测系统的结构示意图。
具体实施方式
下面详细描述本发明的实施例，所述实施例的示例在附图中示出，其中自始至终相同或类似的标号表示相同或类似的元件或具有相同或类似功能的元件。下面通过参考附图描述的实施例是示例性的，旨在用于解释本发明，而不能理解为对本发明的限制。
下面参考附图详细描述本发明实施例所提出的一种火电储能电流互感器状态监测方法和系统。
图1为本申请实施例提出的一种火电储能电流互感器状态监测方法的流程图，如图1所示，该方法包括以下步骤：
步骤S101，获取GIS中电流互感器的多个状态量，并获取每个状态量对应的预设判断条件。
其中，气体绝缘开关设备(Gas Insulated Switchgear，简称GIS)，用于将火电储能系统的相关一次设备经优化设计有机地组合成一个整体，火电储能系统即火力发电站的储能系统。GIS采用绝缘性能较佳的六氟化硫气体做绝缘和灭弧介质，所以能大幅度缩小变电站的体积，GIS之中可以包括：断路器、隔离开关、接地开关、电压互感器、电流互感器、避雷器、母线、电缆终端和进出线套管等子设备。
其中，电流互感器的状态量是表示电流互感器运行状态的参数，可以包括与电流互感器的运行状态相关的工作参数以及电流互感器中各部件的设备参数等。预设的判断条件是用于检测对应的状态量是否异常的检测方式和判断依据。由于电流互感器需要检测的参数较多，因此，本申请获取多个状态量，并获取其中每个状态量对应的预设判断条件。
在本申请实施例中，根据实际检测目的、检测需要和不同GIS的差别等因素，选取电流互感器的多个状态量进行检测，在确定状态量之后，再获取预先结合历史运行经验和专家知识等方式确定的各个状态量对应的判断条件。
举例而言，获取的电流互感器的多个状态量可以包括：振动和异常声响、放电声、设备标牌状态、二次绕组电阻值、二次绕组绝缘电阻值、六氟化硫(SF6)压力表状态、接地连接状态、SF6气体密度、SF6气体湿度、SF6分解物和局部放电参数等。
步骤S102，根据预设判断条件对对应的状态量进行检测，获得每个状态量对应的检测结果。
具体的，根据状态量是否满足对应的预设判断条件判断该状态量是否正常，以对各状态量进行检测，比如，在状态量满足预设条件时判断该状态量正常，在状态量不满足预设条件时判断该状态量异常。
可以理解的是，对于同一状态量，可能从不同的方面对其进行检测，比如，对于电流互感器中的某一设备，可以检测该设备的外观、连接状况和运行数据等，并且，同一状态量的异常程度也可能不同，比如存在轻微异常和严重异常。因此，在本申请一个实施例中，根据预设判断条件对对应的状态量进行检测得到的检测结果，可以包括状态量是否异常、状态量对应的异常状态和状态量对应的劣化程度。其中，不同的异常状态表示不同的异常程度，本申请实施例中由劣化程度表示异常程度，一个异常状态可以对应一个或多个劣化程度。
为了更加清楚的描述本申请根据预设判断条件对对应的状态量进行检测，获得每个状态量对应的检测结果的具体实现过程，下面在一些实施例中进行示例性说明。
在本申请一个实施例中，当状态量为振动和异常声响时，若检测出电流互感器运行中内部出现振动和异常声响，则确定检测结果为异常。可以理解，该示例中判断条件为电流互感器运行中内部出现是否出现振动和异常声响，后续对判断条件的解释可参照本示例，后续示例中均不再赘述。具体实施时可以通过振动传感器对电流互感器进行振动检测，根据振动传感器是否输出振动位移信号判断电流互感器运行中内部是否出现振动。并且，可以通过声音测试仪对电流互感器内部的声音进行检测，若检测电流互感器内部的声音的音量大于预设的分贝数，则判定检测出电流互感器运行中内部出现异常声响，进而在检测出电流互感器运行中内部出现振动和异常声响时，确定检测结果为异常。
以及，当状态量为放电声时，若检测出电流互感器运行中内部出现放电声，则确定检测结果为异常。具体实施时，可以预先通过大量研究分析，确定放电声的信号特点，再通过声发射检测装置对电流互感器运行中内部出现的声音进行检测，若检测出与预先确定的放电声的信号特点相同的声音信号，则判定检测出电流互感器运行中内部出现放电声，进而确定检测结果为异常。
以及，当状态量为设备标牌状态时，若未识别出设备标牌上的设备标识，则确定检测结果为异常。在本示例中，电流互感器设备标牌上的设备标识在长期运行过程中由于油漆脱落或发生损坏等原因，可能存在不齐全或者模糊的情况，导致不能被识别出或者识别错误，因此需要对设备标识的完整性进行识别。具体实施时，可以通过人工比较的方式将当前的设备标识与初始的标识进行比较，或者，通过摄像装置采集设备标识的图像，再通过图像识别技术对采集的图像进行识别，确定是否可识别出设备标识或判断识别出的设备标识与初始的标识的匹配程度是否大于阈值,匹配程度较低时视为未识别出设备标识。进而若未识别出设备标牌上的设备标识，则确定检测结果为异常。
以及，当状态量为二次绕组电阻值时，若检测出二次绕组电阻值与出厂电阻值的偏差在预设的误差范围之外，则确定检测结果为异常。具体的，预设的误差范围是允许的阻值误差范围，若检测出二次绕组电阻值与出厂电阻值的偏差在预设的误差范围之外，则表明二次绕组电阻值与出厂电阻值偏差明显，进而确定检测结果为异常。
以及，当状态量为二次绕组绝缘电阻值时，若检测出为二次绕组绝缘电阻值不大于预设的阻值阈值，则确定检测结果为异常。具体的，预设的阻值阈值可以是保证电流互感器安全运行所需的最低安全电阻值，比如，预设的阻值阈值为2MΩ，若检测出二次绕组绝缘电阻值不大于2MΩ，则确定检测结果为异常。
在本申请一个实施例中，当状态量为SF6压力表状态时，检测SF6压力表的外观和指示的压力值，在检测出SF6压力表的外观破损或存在渗漏油时，确定检测结果为第一异常状态，在检测出SF6压力表指示的压力值在预设的压力范围之外，确定检测结果为第二异常 状态。
具体而言，对于SF6压力表状态，从压力表的外观和指示的压力值两个角度进行检测，若检测出外观存在破损或通过油量检测设备检测出压力表外壳上有渗漏油，则确定检测结果为第一异常状态，即第一异常状态是指SF6压力表外观破损或存在渗漏油的异常状态，后续对异常状态的解释可参照本示例，后续示例中均不再赘述。进一步的，检测SF6压力表指示的压力值是否在预设的压力范围之外，其中，预设的压力范围是预先确定的电流互感器正常运行状态下的压力值范围，若压力表指示的压力值超出该范围则表示压力表指示异常，进而确定检测结果为第二异常状态。
需要说明的是，本申请在确定任一状态量异常后，可确定该异常的状态量对应的劣化程度，在本申请一个实施例中，劣化程度可以设置为四个级别，从低至高为由I至IV，每个异常的状态量对应的劣化程度可以预先结合历史经验知识和相关检测规章等方式确定，在检测出状态量异常后即可获取对应的劣化程度，比如，若检测出电流互感器运行中内部出现振动和异常声响，则可确定该异常对应的劣化程度为Ⅲ。并且，由上述描述可知，同一状态量可具有多个异常状态，每个异常状态对应的劣化程度不同。比如，上述实施例中第一异常状态对应的劣化程度为Ⅲ，第二异常状态对应的劣化程度为Ⅳ。
在本申请一个实施例中，当状态量为接地连接状态时，检测接地连接的锈蚀状况和松动状况，在检测出接地连接存在锈蚀或油漆剥落时，确定检测结果为第三异常状态，在检测出接地引下线发生松动时，确定检测结果为第四异常状态，在检测出接地线脱落时，确定检测结果为第五异常状态。
具体而言，在通过自然电位法或电流密度法检测出接地连接有锈蚀，或观测出油漆剥落时，确定检测结果为第三异常状态。进一步的，对接地引下线进行检测，接地引下线是连接电气设备与接地体的金属导体，若检测出接地引下线发生松动确定检测结果为第四异常状态，若检测出接地引下线已脱落，即原本通过接地引下线连接的设备已与接地断开，则确定检测结果为第五异常状态。其中，第三异常状态至第五异常状态对应的劣化程度逐渐增大，比如，分别对应由Ⅱ至Ⅳ。
在本申请一个实施例中，当状态量为SF6气体密度时，检测相邻两次补气操作之间的补气间隔时间，在补气间隔时间小于第一时间阈值且大于等于第二时间阈值时，确定检测结果为第六异常状态；在补气间隔时间小于第二时间阈值且大于等于第三时间阈值时，确定检测结果为第七异常状态；在补气间隔时间小于第三时间阈值时，确定检测结果为第八异常状态。
举例而言,通过调取数据库中存储的补气操作的记录数据,或者对不同时间段下的气体密度进行检测并获取记录的数据等多种方式,对每两次补气操作之间的补气间隔时间进行检 测。在本示例中，假设第一时间阈值为两年，第二时间阈值为一年，第三时间阈值为半年，根据获取补气间隔时间，当间隔时间不小于一年且小于两年，可以确定识别结果为第六异常状态，当该间隔时间不小于半年且小于一年，可以确定识别结果为第七异常状态，当该间隔时间小于半年，可以确定识别结果为第八异常状态。
需要说明的是，在本示例中，第六异常状态至第八异常状态对应的劣化程度逐渐增大，并且由于补气间隔时间较长，可在同一异常状态下根据实际的间隔时间进行进一步划分，确定劣化程度，即本示例中一个异常状态可以对应多个劣化程度。比如，对于第六异常状态，劣化程度可以为I至Ⅱ，其中，当两次补齐间隔在一年半至两年的区间时，劣化程度为I，当两次补齐间隔在一年至一年半的区间时，劣化程度为Ⅱ。
在本申请一个实施例中，当状态量为SF6气体湿度时，检测电流互感器运行中的微水值，在微水值大于等于第一浓度阈值且小于第二浓度阈值时，确定检测结果为第九异常状态；在微水值大于等于第二浓度阈值且小于第三浓度阈值时，进一步确定微水值的第一增长速度，在第一增长速度大于增长速度阈值时，确定检测结果为第十异常状态；在微水值大于等于第三浓度阈值时，进一步确定微水值的第二增长速度，在第二增长速度大于增长速度阈值时，确定检测结果为第十一异常状态。
举例而言，假设第一浓度阈值为300μL/L，第二浓度阈值为500μL/L，第三浓度阈值为800μL/L，以及，增长速度阈值设置为15％。根据检测的微水值浓度，当微水值浓度大于300μL/L且小于500μL/L，可以确定识别结果为第九异常状态，当微水值浓度大于500μL/L且小于800μL/L，进一步确定在电流互感器运行期间，微水值浓度的增长速度，通过比较不同时刻下微水值浓度确定微水值浓度的增长速度为20％时，由于第一增长速度大于增长速度阈值，则可以确定识别结果为第十异常状态。当该微水值浓度大于800μL/L，并且按照上述方式确定微水值浓度的增长幅度为30％，大于增长速度阈值时，可以确定识别结果为第十一异常状态。
在本申请一个实施例中，当状态量为SF6分解物时，检测H2S含量和SO2的含量；在H2S浓度值或者SO2浓度值大于第四浓度阈值且小于第五浓度阈值时，确定检测结果为第十二异常状态；在H2S浓度值或者SO2浓度值大于等于第五浓度阈值时，确定检测结果为第十三异常状态。
举例而言，在本实施例中对H2S含量和SO2的含量分别进行检测，假设第四浓度阈值为1μL/L，第五浓度阈值为2μL/L。通过气体浓度检测设备检测出H2S浓度值或者SO2浓度值不小于1μL/L，但小于2μL/L时，确定检测结果为第十二异常状态，其中，H2S浓度值不小于1μL/L，但小于2μL/L时的劣化程度为Ⅲ至Ⅳ，SO2浓度值不小于1μL/L，但小于2μL/L时的劣化程度为Ⅱ至Ⅲ。当检测出H2S浓度值或者SO2浓度值不小于2μL/L时，确定检测 结果为第十三异常状态，其中，H2S浓度值不小于2μL/L时的劣化程度为Ⅳ，SO2浓度值不小于2μL/L时的劣化程度为Ⅲ。
在本申请一个实施例中，当状态量为局部放电时，检测局部放电时的特高频信号和超声波信号；将特高频信号与预设的放电检测图谱进行匹配，在特高频信号与预设的放电检测图谱不匹配时，确定检测结果为第十四异常状态；在特高频信号存在目标局部放电的检测图谱时，确定检测结果为第十五异常状态；在超声波信号存在目标局部放电的检测图谱时，进一步获取超声波信号的测量值，若测量值大于预设分贝值，则确定检测结果为第十六异常状态
举例而言，在本实施例中对特高频信号和超声波信号分别进行检测，预设的放电检测图谱是在同等条件下同类设备检测的图谱，目标局部放电的检测图谱是本领域中典型的局部放电的检测图谱。在本示例中，在检测出特高频信号与预设的放电检测图谱不匹配时，即与同等条件下同类设备检测的图谱存在明显区别，比如，图谱中数据的差值过大时，确定检测结果为第十四异常状态；在检测出特高频信号具有典型局部放电的检测图谱时，确定检测结果为第十五异常状态；在检测出超声波信号存在典型局部放电的检测图谱并且超声波信号的测量值大于预设分贝值，比如，大于10dB时，确定检测结果为第十六异常状态，本示例中的预设分贝值可以预先结合大量实验和历史经验知识确定。
需要说明的是，除了对上述实施例中的GIS之中电流互感器的状态量进行检测之外，在本发明的其他实施例中还可以对电流互感器的其它状态量进行检测，以提高检测的全面性。下面继续参照一些示例详细说明状态量检测和异常状态的识别：
作为第一种示例，当状态量为机构箱的状态时，可以对机构箱的密封性、锈蚀状态和损坏状态分别进行检测。在检测密封时，可以通过干空气法或示踪气体法检测密封性，当检测出密封不良时，确定检测结果为第十七异常状态。进一步的，在检测出密封不良后进一步检测机构箱内是否有积水，具体实施时可以通过水位传感器或湿度传感器检测机构箱内是否有积水，比如，预先确定正常状态下机构箱内的湿度值为10％，当通过湿度传感器检测出实际的湿度值为20％，则湿度值超过预设湿度阈值，进而可以确定识别结果为第十八异常状态。在检测锈蚀状态时，检测机构箱中的接触器、继电器节点是否锈蚀，以及检测机构、端子是否排锈蚀，若发生锈蚀则可以确定识别结果为第十九异常状态。在检测损坏状态时，检测机构箱中的加热器等装置是否发生损坏，若装置损坏则可以确定识别结果为第二十异常状态。其中，第十七异常状态的劣化程度为I，第十八异常状态的劣化程度为Ⅳ，第十九异常状态和第二十异常状态的劣化程度为Ⅱ。
作为第二种示例，当状态量为控制辅助回路元器件工作状态时，检测回路中的元器件是否损坏、失灵，端子排是否锈蚀、脏污严重或接线桩头松动发热，若检测出现上述任一中现 象，则确定检测结果为异常，本示例中的异常状态量对应的劣化程度为Ⅱ。
作为第三种示例，当状态量为设备外壳工作状态时，若检测出设备外壳是否发生锈蚀或变形，则确定检测结果为异常。
作为第四种示例，当状态量为已发布的家族缺陷或者同厂家的设备故障信息或同型号设备故障信息或者同时期设备的故障信息时，检测是否对电流互感器的故障信息进行整改，当检测出一般缺陷未整改时，则确定检测结果为第二十一异常状态，当检测出重大缺陷未整改时，则确定确定检测结果为第二十二异常状态，其中，第二十一异常状态对应的劣化程度为Ⅱ，第二十二异常状态对应的劣化程度为Ⅳ。
作为第五种示例，当状态量为SF6气体密度继电器状态时，可以参照SF6压力表状态的检测方式进行检测，并且，还可以进一步检测密度继电器的安装位置，若检测出户外安装的密度继电器未设置防雨罩，则确定检测结果为异常。
由此，本申请根据预设判断条件对多个状态量进行了检测，并获得每个状态量对应的检测结果。
基于上述检测实施例，为了进一步提高状态量检测的准确性，以及根据检测结果确定异常原因，从而便于后续排除异常，充分保证GIS的安全运行，在本申请一个实施例中，在获得每个所述状态量对应的检测结果之后，还包括：对于预先标定的状态量，根据预先标定的状态量的检测结果确定是否执行局部放电检测或红外检漏。
在本实施例中，预先标定的状态量是预先确定的在发生异常时需要进一步检测的状态量。举例而言，对于振动和异常声响，在检测出现异常振动时应进一步排除外部因素；对于放电声，当出现疑似内部放电声时应进行局部放电检测；对于压力表，当检测出压力表指示异常时，应确认压力表指示异常的原因，如非表计原因，需进行红外检漏，查找漏气地点；对于SF6气体密度，当检测出补气间隔时间异常时应进行红外检漏；对于SF6分解物，当检测到异常分解产物时应进行局部放电检测。由此，通过局部放电检测或红外检漏，进一步确定异常的位置和异常的原因等，有利于后续排除异常。
步骤S103，根据检测结果计算对应的状态量的扣分值，将多个状态量对应的检测结果和多个状态量对应的扣分值作为训练数据，对预先设置的基于人工智能的状态量评价模型进行训练，获得训练完成的状态量评价模型。
其中，为了实现对GIS电流互感器的状态量进行评价，对不同的检测结果计算对应的扣分值，扣分值可以反映异常的严重程度和对电流互感器的影响程度，扣分值越高表示对电流互感器的危害越大。
在本申请一个实施例中，根据检测结果计算对应的状态量的扣分值，包括：根据劣化程度确定检测结果对应的基础扣分值，并获取发生异常的该状态量对应的影响因子，将基 础扣分值乘以影响因子得到扣分值。
具体而言，基础扣分值与劣化程度具有映射关系，该映射关系预先确定，在确定检测结果中的劣化程度后，可直接获取对应的基础扣分值。举例而言，劣化程度I至Ⅳ对应的基础扣分值依次为2、4、8和10。
其中，影响因子可近似视为该状态量影响电流互感器状态的权重，可以理解，不同的状态量对电流互感器状态评价的影响是不同的，比如，压力表指示异常的危害性大于设备标牌模糊，因此，本申请预先对每个状态量设置了对应的影响因子，在检测出该状态量异常后，根据检测确定的劣化程度确定对应的基础扣分值，并获取该状态量对应的影响因子，将基础扣分值乘以影响因子得到扣分值。
进一步的，将获取的多个状态量对应的检测结果和对应的扣分值作为训练数据，对预先设置的状态量评价模型进行训练。其中，预先设置的状态量评价模型可以是各种类型的神经网络模型，即，本申请基于人工智能技术，通过训练完成的神经网络模型生成电流互感器状态量的评价结果。其中，预置的状态量检测模型的类型可以根据实际需要设置，比如，选择长短期记忆人工神经网络(Long Short-Term Memory，简称LSTM)模型作为状态量评价模型。
具体实施时，作为一种可能的实现方式，将检测出的每个状态量对应的检测结果和对应的扣分值作为训练数据，按照预设比例划分为训练集、验证集和测试集后，通过训练集中的训练数据对模型进行训练，具体训练过程可以参照相关技术中的神经网络模型的训练方法，包括对数据进行二值化、平滑和滤波等各种预处理后，然后对处理后的数据进行特征抽取和选择，训练状态量检测模型中各层参数的权重，比如，根据各状态量的影响因子和各异常状态对应的劣化程度确定状态量评价模型中对各状态量评价的权重。并通过定义损失函数并利用梯度下降法训练状态量检测模型。在训练完成后还可以通过测试集中的数据对模型进行测试，验证其预测精度是否符合需求。
步骤S104，将待检测的电流互感器的状态量检测数据输入至训练完成的状态量评价模型，生成待检测电流互感器的目标检测结果。
其中，目标检测结果包括GIS之中电流互感器的整体检测结果，整体检测结果可以包括扣除异常状态量对应的扣分值后，电流互感器对应的最终分数，以及最终分数所属的评价等级。目标检测结果还可以包括电流互感器每个状态量的扣分值和评价结果。
具体的，在训练完成状态量评价模型后，实际进行检测时，将当前待检测的电流互感器的状态量检测数据输入至训练完成的状态量评价模型，其中，状态量检测数据可以是各个状态量的检测结果数据，也可以是状态量的实际检测数数值，将实际检测数数值输入状态量评价模型，由评价模型基于判断条件检测状态量是否异常。然后状态量评价模型对输入的数据 进行检测和评价并输出当前待检测的电流互感器的目标检测结果。在本申请一个实施例中，状态量评价模型可以输出针对电流互感器的评价表，评价表中包括当前电流互感器的最终分数和评价等级，并在后续列出每一项状态量的扣分值和评价结果。
举例而言，将满分100分后扣除多个异常状态量对应的扣分值后，剩余85分，该分数是电流互感器的整体评价分数，该分数可反映GIS之中电流互感器的状态。并且，状态量评价模型在训练时，结合历史运行经验预先确定评价分数对应的评价等级，比如，90分至100分为优秀，70至90为良好等，进而根据整体评价分数确定评价等级。并在评价表的评价细节部分汇总列出每个状态量的检测结果，包括扣分值和评价值。
综上所述，本申请实施例的火电储能电流互感器状态监测方法，该方法从整体和具体细节两个角度对电流互感器状态量进行检测，通过训练完成的状态量评价模型可以更加快速、便捷的生成电流互感器的整体检测结果，并汇总每个状态量的检测结果，提高电流互感器状态量检测的准确性、全面性和检测效率，并减少检测所需的人工成本，便于及时对电流互感器进行维护，减少电流互感器在运行过程中存在的潜在风险，有利于保障GIS的正常运行。
为了实现上述实施例，本申请还提出了一种火电储能电流互感器状态监测系统，图2为本申请实施例提出的一种火电储能电流互感器状态监测系统的结构示意图，如图2所示，该系统包括获取模块100、检测模块200、训练模块300和生成模块400。
其中，获取模块100，用于获取电流互感器的多个状态量，并获取每个状态量对应的预设判断条件。
检测模块200，用于根据预设判断条件对对应的状态量进行检测，获得每个状态量对应的检测结果。
训练模块300，用于根据检测结果计算对应的状态量的扣分值，将多个状态量对应的检测结果和多个状态量对应的扣分值作为训练数据，对预先设置的基于人工智能的状态量评价模型进行训练，获得训练完成的状态量评价模型。
生成模块400，用于将待检测的电流互感器的状态量检测数据输入至训练完成的状态量评价模型，生成待检测电流互感器的目标检测结果。
可选地，在本申请的一个实施例中，检测结果包括状态量是否异常、状态量对应的异常状态和状态量对应的劣化程度，训练模块300具体用于：根据劣化程度确定检测结果对应的基础扣分值，并获取状态量对应的影响因子，将基础扣分值乘以影响因子得到扣分值。
可选地，在本申请的一个实施例中，检测模块200还用于：对于预先标定的状态量，根据预先标定的状态量的检测结果确定是否执行局部放电检测或红外检漏。
可选地，在本申请的一个实施例中，检测模块200还用于：当状态量为振动和异常声响时，若检测出电流互感器运行中内部出现振动和异常声响，则确定检测结果为异常；当 状态量为放电声时，若检测出电流互感器运行中内部出现放电声，则确定检测结果为异常；当状态量为设备标牌状态时，若未识别出设备标牌上的设备标识，则确定检测结果为异常；当状态量为二次绕组电阻值时，若检测出二次绕组电阻值与出厂电阻值的偏差在预设的误差范围之外，则确定检测结果为异常；当状态量为二次绕组绝缘电阻值时，若检测出为二次绕组绝缘电阻值不大于预设的阻值阈值，则确定检测结果为异常。
可选地，在本申请的一个实施例中，检测模块200还用于：当状态量为SF6压力表状态时，检测SF6压力表的外观和指示的压力值；在检测出SF6压力表的外观破损或存在渗漏油时，确定检测结果为第一异常状态；在检测出SF6压力表指示的压力值在预设的压力范围之外，确定检测结果为第二异常状态。
可选地，在本申请的一个实施例中，检测模块200还用于：当状态量为接地连接状态时，检测接地连接的锈蚀状况和松动状况；在检测出接地连接存在锈蚀或油漆剥落时，确定检测结果为第三异常状态；在检测出接地引下线发生松动时，确定检测结果为第四异常状态；在检测出接地线脱落时，确定检测结果为第五异常状态。
可选地，在本申请的一个实施例中，检测模块200还用于：当状态量为SF6气体密度时，检测相邻两次补气操作之间的补气间隔时间；在补气间隔时间小于第一时间阈值且大于等于第二时间阈值时，确定检测结果为第六异常状态；在补气间隔时间小于第二时间阈值且大于等于第三时间阈值时，确定检测结果为第七异常状态；在补气间隔时间小于第三时间阈值时，确定检测结果为第八异常状态。
可选地，在本申请的一个实施例中，检测模块200还用于：当状态量为SF6气体湿度时，检测电流互感器运行中的微水值；在微水值大于等于第一浓度阈值且小于第二浓度阈值时，确定检测结果为第九异常状态；在微水值大于等于第二浓度阈值且小于第三浓度阈值时，进一步确定微水值的第一增长速度，在第一增长速度大于增长速度阈值时，确定检测结果为第十异常状态；在微水值大于等于第三浓度阈值时，进一步确定微水值的第二增长速度，在第二增长速度大于增长速度阈值时，确定检测结果为第十一异常状态。
可选地，在本申请的一个实施例中，检测模块200还用于：当状态量为SF6分解物时，检测H2S含量和SO2的含量；在H2S浓度值或者SO2浓度值大于第四浓度阈值且小于第五浓度阈值时，确定检测结果为第十二异常状态；在H2S浓度值或者SO2浓度值大于等于第五浓度阈值时，确定检测结果为第十三异常状态。
可选地，在本申请的一个实施例中，检测模块200还用于：当状态量为局部放电时，检测局部放电时的特高频信号和超声波信号；将特高频信号与预设的放电检测图谱进行匹配，在特高频信号与预设的放电检测图谱不匹配时，确定检测结果为第十四异常状态；在特高频信号存在目标局部放电的检测图谱时，确定检测结果为第十五异常状态；在超声波 信号存在目标局部放电的检测图谱时，进一步获取超声波信号的测量值，若测量值大于预设分贝值，则确定检测结果为第十六异常状态。
需要说明的是，前述对火电储能系统电流互感器状态监测方法的实施例的解释说明也适用于该实施例的系统，此处不再赘述
综上所述，本申请实施例的火电储能电流互感器状态监测系统，从整体和具体细节两个角度对电流互感器状态量进行检测，通过训练完成的状态量评价模型可以更加快速、便捷的生成电流互感器的整体检测结果，并汇总每个状态量的检测结果，提高电流互感器状态量检测的准确性、全面性和检测效率，并减少检测所需的人工成本，便于及时对电流互感器进行维护，减少电流互感器在运行过程中存在的潜在风险，有利于保障GIS的正常运行。
为了实现上述实施例，本申请还提出了一种电子设备，该电子设备包括存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时，实现如上述实施例中任一项所述的火电储能电流互感器状态监测方法。
在本说明书的描述中，参考术语“一个实施例”、“一些实施例”、“示例”、“具体示例”、或“一些示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含于本申请的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不必须针对的是相同的实施例或示例。而且，描述的具体特征、结构、材料或者特点可以在任一个或多个实施例或示例中以合适的方式结合。此外，在不相互矛盾的情况下，本领域的技术人员可以将本说明书中描述的不同实施例或示例以及不同实施例或示例的特征进行结合和组合。
此外，术语“第一”、“第二”仅用于描述目的，而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”的特征可以明示或者隐含地包括至少一个该特征。在本申请的描述中，“多个”的含义是至少两个，例如两个，三个等，除非另有明确具体的限定。
流程图中或在此以其他方式描述的任何过程或方法描述可以被理解为，表示包括一个或更多个用于实现定制逻辑功能或过程的步骤的可执行指令的代码的模块、片段或部分，并且本申请的优选实施方式的范围包括另外的实现，其中可以不按所示出或讨论的顺序，包括根据所涉及的功能按基本同时的方式或按相反的顺序，来执行功能，这应被本申请的实施例所属技术领域的技术人员所理解。
在流程图中表示或在此以其他方式描述的逻辑和/或步骤，例如，可以被认为是用于实现逻辑功能的可执行指令的定序列表，可以具体实现在任何计算机可读介质中，以供指令执行系统、装置或设备(如基于计算机的系统、包括处理器的系统或其他可以从指令执行系统、装置或设备取指令并执行指令的系统)使用，或结合这些指令执行系统、装置或设备而使用。就本说明书而言，"计算机可读介质"可以是任何可以包含、存储、通信、传播或传输程序以 供指令执行系统、装置或设备或结合这些指令执行系统、装置或设备而使用的装置。计算机可读介质的更具体的示例(非穷尽性列表)包括以下：具有一个或多个布线的电连接部(电子装置)，便携式计算机盘盒(磁装置)，随机存取存储器(RAM)，只读存储器(ROM)，可擦除可编辑只读存储器(EPROM或闪速存储器)，光纤装置，以及便携式光盘只读存储器(CDROM)。另外，计算机可读介质甚至可以是可在其上打印所述程序的纸或其他合适的介质，因为可以例如通过对纸或其他介质进行光学扫描，接着进行编辑、解译或必要时以其他合适方式进行处理来以电子方式获得所述程序，然后将其存储在计算机存储器中。
应当理解，本申请的各部分可以用硬件、软件、固件或它们的组合来实现。在上述实施方式中，多个步骤或方法可以用存储在存储器中且由合适的指令执行系统执行的软件或固件来实现。如，如果用硬件来实现和在另一实施方式中一样，可用本领域公知的下列技术中的任一项或他们的组合来实现：具有用于对数据信号实现逻辑功能的逻辑门电路的离散逻辑电路，具有合适的组合逻辑门电路的专用集成电路，可编程门阵列(PGA)，现场可编程门阵列(FPGA)等。
本技术领域的普通技术人员可以理解实现上述实施例方法携带的全部或部分步骤是可以通过程序来指令相关的硬件完成，所述的程序可以存储于一种计算机可读存储介质中，该程序在执行时，包括方法实施例的步骤之一或其组合。
此外，在本申请各个实施例中的各功能单元可以集成在一个处理模块中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个模块中。上述集成的模块既可以采用硬件的形式实现，也可以采用软件功能模块的形式实现。所述集成的模块如果以软件功能模块的形式实现并作为独立的产品销售或使用时，也可以存储在一个计算机可读取存储介质中。
上述提到的存储介质可以是只读存储器，磁盘或光盘等。尽管上面已经示出和描述了本申请的实施例，可以理解的是，上述实施例是示例性的，不能理解为对本申请的限制，本领域的普通技术人员在本申请的范围内可以对上述实施例进行变化、修改、替换和变型。

Claims (12)

  1. 一种火电储能电流互感器状态监测方法，包括以下步骤：
    获取GIS中电流互感器的多个状态量，并获取每个所述状态量对应的预设判断条件；
    根据所述预设判断条件对对应的状态量进行检测，获得每个所述状态量对应的检测结果；
    根据所述检测结果计算对应的状态量的扣分值，将所述多个状态量对应的检测结果和所述多个状态量对应的扣分值作为训练数据，对预先设置的基于人工智能的状态量评价模型进行训练，获得训练完成的状态量评价模型；
    将待检测的电流互感器的状态量检测数据输入至所述训练完成的状态量评价模型，生成所述待检测电流互感器的目标检测结果。
  2. 根据权利要求1所述的监测方法，其中所述检测结果包括所述状态量是否异常、所述状态量对应的异常状态和所述状态量对应的劣化程度，其中所述根据所述检测结果计算对应的状态量的扣分值，包括：
    根据所述劣化程度确定所述检测结果对应的基础扣分值，并获取所述状态量对应的影响因子，将所述基础扣分值乘以所述影响因子得到所述扣分值。
  3. 根据权利要求1或2所述的监测方法，其中在所述获得每个所述状态量对应的检测结果之后，所述监测方法还包括：
    对于预先标定的状态量，根据所述预先标定的状态量的检测结果确定是否执行局部放电检测或红外检漏。
  4. 根据权利要求1至3中任一项所述的监测方法，其中所述根据所述预设判断条件对对应的状态量进行检测，获得每个所述状态量对应的检测结果，包括：
    当所述状态量为振动和异常声响时，若检测出所述电流互感器运行中内部出现振动和异常声响，则确定所述检测结果为异常；
    当所述状态量为放电声时，若检测出所述电流互感器运行中内部出现放电声，则确定所述检测结果为异常；
    当所述状态量为设备标牌状态时，若未识别出所述设备标牌上的设备标识，则确定所述检测结果为异常；
    当所述状态量为二次绕组电阻值时，若检测出所述二次绕组电阻值与出厂电阻值的偏差在预设的误差范围之外，则确定所述检测结果为异常；
    当所述状态量为二次绕组绝缘电阻值时，若检测出所述为二次绕组绝缘电阻值不大于预设的阻值阈值，则确定所述检测结果为异常。
  5. 根据权利要求1至4中任一项所述的监测方法，其中所述根据所述预设判断条件对 对应的状态量进行检测，获得每个所述状态量对应的检测结果，还包括：
    当所述状态量为SF6压力表状态时，检测所述SF6压力表的外观和指示的压力值；
    在检测出所述SF6压力表的外观破损或存在渗漏油时，确定所述检测结果为第一异常状态；
    在检测出所述SF6压力表指示的压力值在预设的压力范围之外，确定所述检测结果为第二异常状态。
  6. 根据权利要求1至5中任一项所述的监测方法，其中所述根据所述预设判断条件对对应的状态量进行检测，获得每个所述状态量对应的检测结果，还包括：
    当所述状态量为接地连接状态时，检测所述接地连接的锈蚀状况和松动状况；
    在检测出所述接地连接存在锈蚀或油漆剥落时，确定所述检测结果为第三异常状态；
    在检测出接地引下线发生松动时，确定所述检测结果为第四异常状态；
    在检测出接地线脱落时，确定所述检测结果为第五异常状态。
  7. 根据权利要求1至6中任一项所述的监测方法，其中所述根据所述预设判断条件对对应的状态量进行检测，获得每个所述状态量对应的检测结果，还包括：
    当所述状态量为SF6气体密度时，检测相邻两次补气操作之间的补气间隔时间；
    在所述补气间隔时间小于第一时间阈值且大于等于第二时间阈值时，确定所述检测结果为第六异常状态；
    在所述补气间隔时间小于所述第二时间阈值且大于等于第三时间阈值时，确定所述检测结果为第七异常状态；
    在所述补气间隔时间小于所述第三时间阈值时，确定所述检测结果为第八异常状态。
  8. 根据权利要求1至7中任一项所述的监测方法，其中所述根据所述预设判断条件对对应的状态量进行检测，获得每个所述状态量对应的检测结果，还包括：
    当所述状态量为SF6气体湿度时，检测所述电流互感器运行中的微水值；
    在所述微水值大于等于第一浓度阈值且小于第二浓度阈值时，确定所述检测结果为第九异常状态；
    在所述微水值大于等于所述第二浓度阈值且小于第三浓度阈值时，进一步确定所述微水值的第一增长速度，在所述第一增长速度大于增长速度阈值时，确定所述检测结果为第十异常状态；
    在所述微水值大于等于所述第三浓度阈值时，进一步确定所述微水值的第二增长速度，在所述第二增长速度大于所述增长速度阈值时，确定所述检测结果为第十一异常状态。
  9. 根据权利要求1至8中任一项所述的监测方法，其中所述根据所述预设判断条件对 对应的状态量进行检测，获得每个所述状态量对应的检测结果，还包括：
    当所述状态量为SF6分解物时，检测H2S含量和SO2的含量；
    在H2S浓度值或者SO2浓度值大于第四浓度阈值且小于第五浓度阈值时，确定所述检测结果为第十二异常状态；
    在所述H2S浓度值或者所述SO2浓度值大于等于所述第五浓度阈值时，确定所述检测结果为第十三异常状态。
  10. 根据权利要求1至9中任一项所述的监测方法，其中所述根据所述预设判断条件对对应的状态量进行检测，获得每个所述状态量对应的检测结果，还包括：
    当所述状态量为局部放电时，检测所述局部放电时的特高频信号和超声波信号；
    将所述特高频信号与预设的放电检测图谱进行匹配，在所述特高频信号与所述预设的放电检测图谱不匹配时，确定所述检测结果为第十四异常状态；
    在所述特高频信号存在目标局部放电的检测图谱时，确定所述检测结果为第十五异常状态；
    在所述超声波信号存在所述目标局部放电的检测图谱时，进一步获取所述超声波信号的测量值，若所述测量值大于预设分贝值，则确定所述检测结果为第十六异常状态。
  11. 一种火电储能电流互感器状态监测系统，包括：
    获取模块，用于获取电流互感器的多个状态量，并获取每个所述状态量对应的预设判断条件；
    检测模块，用于根据所述预设判断条件对对应的状态量进行检测，获得每个所述状态量对应的检测结果；
    训练模块，用于根据所述检测结果计算对应的状态量的扣分值，将所述多个状态量对应的检测结果和所述多个状态量对应的扣分值作为训练数据，对预先设置的基于人工智能的状态量检测模型进行训练，获得训练完成的状态量检测模型；
    生成模块，用于将待检测的电流互感器的状态量检测数据输入至所述训练完成的状态量评价模型，生成所述待检测电流互感器的目标检测结果。
  12. 一种电子设备，包括存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时，实现如权利要求1-10中任一项所述的火电储能电流互感器状态监测方法。
PCT/CN2023/098351 2022-07-14 2023-06-05 火电储能电流互感器状态监测系统 Ceased WO2024012091A1 (zh)

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