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CN-122000257-A - Self-diagnosis high-voltage fuse and running state online monitoring method thereof

CN122000257ACN 122000257 ACN122000257 ACN 122000257ACN-122000257-A

Abstract

The invention discloses a self-diagnosis high-voltage fuse and an on-line monitoring method of the running state thereof, belonging to the technical field of high-voltage power equipment, the method comprises the following steps that S1, the running parameters and the environmental parameters of a fuse body are collected, and the collected data are corrected in real time based on a calibration factor; the method comprises the steps of S2, filtering and denoising calibrated data to extract core characteristic parameters, S3, calculating a state evaluation index based on a multi-parameter coupling state evaluation formula, constructing a dynamic threshold self-optimization model by combining a digital twin model simulation result, adjusting a state judgment threshold in real time, and adaptively matching a typical fault mode, S4, calculating the residual life of a fuse body through a dynamic aging prediction formula, quantifying a fault risk level, and S5, carrying out grading response and self-diagnosis feedback based on the calculation result. The invention solves the technical defects of single monitoring parameter, low diagnosis precision and lack of self-repairing linkage in the prior art, and improves the operation safety and reliability of the power system.

Inventors

  • JIN YINGXIN

Assignees

  • 河北名琅电力科技有限公司

Dates

Publication Date
20260508
Application Date
20260126

Claims (10)

  1. 1. A self-diagnosis high-voltage fuse is characterized by comprising a fuse body, a fuse body and a fuse body, wherein the fuse body is made of a novel self-repairing alloy material, and a micro-nano conductive repairing medium is embedded in the fuse body; the self-diagnosis integrated module is integrated in end caps at two ends of the fuse body and comprises a physical field sensor unit, a data processing unit, a digital twin virtual modeling unit, a self-calibration unit, a wireless communication unit and a self-repair triggering unit; the auxiliary execution module is electrically connected with the self-diagnosis integrated module and comprises a miniature heat radiation module, an arc suppression device and a state indication unit.
  2. 2. The self-diagnosis high-voltage fuse of claim 1, wherein the novel self-repair alloy material adopted by the fuse body is prepared from copper-silver alloy serving as a base material and added with a nano aluminum oxide reinforcing phase, the micro-nano conductive repair medium is a micro-nano bismuth-tin alloy medium, the melting point is 120-150 ℃, and the fuse body is in a solid insulation state below 80 ℃.
  3. 3. The self-diagnostic high voltage fuse of claim 1 wherein said physical field sensor unit comprises a Rogowski coil current sensor, a fiber bragg grating temperature sensor, a micro strain sensor, a voltage sensor, and an environmental sensor.
  4. 4. A method for on-line monitoring of the operation state of a self-diagnosing high voltage fuse as recited in any one of claims 1 to 3, comprising the steps of: S1, multidimensional data acquisition and synchronous calibration, namely acquiring operation parameters and environment parameters of a fuse body according to sampling frequency through a physical field sensor unit, and correcting acquired data in real time based on a calibration factor K by a self-calibration unit; S2, data preprocessing and multi-factor feature extraction, namely filtering and denoising the calibrated data, and extracting a current change rate, a temperature change rate, a voltage harmonic component, an equivalent resistance and a strain accumulation amount; s3, self-adaptive running state diagnosis, namely calculating a state evaluation index S based on a multi-parameter coupling state evaluation formula, combining a digital twin model simulation result, introducing full life cycle data of a fuse body to construct a dynamic threshold self-optimization model, adjusting a state judgment threshold in real time, and self-adaptively matching a typical fault mode to perform accurate state diagnosis and fault type identification; S4, dynamically predicting the residual life, namely calculating the residual life of the fuse body through a dynamic aging prediction formula and quantifying the fault risk level; and S5, grading response and self-diagnosis feedback, namely triggering corresponding early warning, self-repair pretreatment or fault isolation actions according to the state diagnosis result and the residual life prediction value, and feeding back the diagnosis result to the power system monitoring platform.
  5. 5. The on-line monitoring method of operation state of self-diagnosis high voltage fuse in claim 4, wherein in S1, the collected data is corrected in real time by adopting an adaptive calibration algorithm, and the calculation formula of the calibration factor K is as follows: ; Wherein K 0 is a standard calibration coefficient, and the value is 0.98-1.02; As a temperature influence coefficient of the temperature, As the coefficient of influence of the humidity, The temperature of the fuse link body is T, T 0 is standard calibration temperature, H is environment relative humidity, D is environment dust concentration, calibrated data X cal =X raw ×K,X raw is sensor original acquisition data, and X cal is calibrated data.
  6. 6. The on-line monitoring method of the operation state of a self-diagnostic high voltage fuse as recited in claim 5, wherein in S3, the multi-parameter coupling state evaluation formula is: ; Wherein S is a state evaluation index, Are all dynamic weight coefficients, satisfy I is real-time running current, I N is rated current of the fuse, T is temperature of the body of the fuse link, T env is ambient temperature, T max is allowable highest working temperature of the fuse, R eq is equivalent resistance of the body of the fuse link, and R 0 is initial resistance of the body of the fuse link; In order to provide a cumulative amount of strain, Maximum strain is allowed for the fuse body, U h is the total effective value of voltage harmonics, and U N is the rated voltage of the fuse.
  7. 7. The on-line monitoring method of the operation state of the self-diagnosis high voltage fuse of claim 6, wherein the optimization formula of the dynamic threshold self-optimization model is: ; Where T std is a standard threshold, k life is a life decay influence coefficient, In order to accumulate the duty cycle, k work is the working condition severity coefficient, Scoring the working condition severity; Finally calculating an optimized normal threshold T opt1 , an optimized potential fault threshold T opt2 and an optimized failure threshold T opt3 based on the dynamic threshold self-optimization model; The dynamic threshold judgment rule is that S is less than or equal to T opt1 and is in a normal state, T opt1 <S≤T opt2 is in a potential fault state, T opt2 <S≤T opt3 is in a critical fusing state, S > T opt3 is in a failure state, and meanwhile fault types are adaptively matched based on a fault mode library.
  8. 8. The on-line monitoring method of the operation state of the self-diagnosis high voltage fuse as claimed in claim 7, wherein in S4, a dynamic aging prediction formula is: ; Wherein L r is the residual life, L 0 is the rated service life of the fuse, K 1 is a current aging coefficient, k 2 is a temperature aging coefficient, k 3 is a self-repairing number influence coefficient, t is accumulated running time, n is a self-repairing triggering number, In order to provide a cumulative effect of the current overload, Is a cumulative effect of temperature rise.
  9. 9. The on-line monitoring method of the operation state of the self-diagnosis high voltage fuse of claim 8, wherein the state evaluation index S introduced into S3 and the digital twin model simulation life L sim are subjected to error correction, and the correction formula is as follows: ; Wherein L is the final remaining life after correction.
  10. 10. The on-line monitoring method for operating conditions of a self-diagnosing high voltage fuse as recited in claim 9, wherein the determining rule of the failure risk level in S5 is: S is less than or equal to 0.3 or L is more than 2000h, and is judged to be in a normal state, wherein the self-diagnosis module enters a low-power consumption monitoring mode, the sampling frequency is reduced to 100Hz, state data is uploaded to the monitoring platform through the wireless communication unit every 30min, and the auxiliary execution module is in a standby state; Triggering a first-level early warning, enabling a state indication unit LED lamp to flash yellow, starting a miniature heat dissipation module to reduce the temperature of a fuse body, increasing the sampling frequency to 500Hz, uploading state data once every 5min, and tracking the change trend of parameters in real time; triggering a secondary early warning, enabling a state indication unit LED lamp to flash red, starting an arc suppression device to prevent arc diffusion, synchronously sending fault early warning information to a monitoring platform, increasing the sampling frequency to 1kHz, uploading state data once every 1min, and reminding operation and maintenance personnel of timely treatment; S >0.9 or L is less than or equal to 500h, namely triggering three-stage early warning, enabling a state indication unit LED lamp to be red and normally bright, enabling a self-repairing triggering unit to start self-repairing, enabling micro-nano conductive repairing medium to be melted through heating wires, filling fusing gaps to achieve temporary conductive repairing, simultaneously sending an emergency tripping request to a monitoring platform, and achieving fault isolation in cooperation with a power system.

Description

Self-diagnosis high-voltage fuse and running state online monitoring method thereof Technical Field The invention relates to the technical field of high-voltage power equipment, in particular to a self-diagnosis high-voltage fuse and an on-line monitoring method for the running state of the self-diagnosis high-voltage fuse. Background The high-voltage fuse is a key short-circuit protection and overload protection device in a power system, and the running state of the high-voltage fuse directly affects the safety and stability of the power system. The existing high-voltage fuses are mostly of traditional structures, lack of effective on-line monitoring and self-diagnosis functions, and can only find faults through manual inspection after fusing, so that the problems of lag in fault identification, high maintenance cost, large unplanned power failure risk and the like exist. At present, although a part of improved high-voltage fuses introduce simple monitoring functions, such as monitoring running current through a current transformer and monitoring surface temperature through a temperature sensor, the following technical defects exist: Firstly, the monitoring parameters are single, only electric or thermal single dimension parameters are concerned, the comprehensive influence of multiple factors such as strain, harmonic waves, environment and the like on the operation state of the fuse is ignored, the diagnosis precision is low, and misjudgment and missed judgment are easy to occur; secondly, the sensor is not provided with a self-adaptive calibration mechanism, the accuracy of the sensor is easily influenced by environmental factors, and the reliability of monitoring data is reduced after long-term operation; thirdly, the linkage of monitoring and self-repairing is not realized, only the passive alarm is realized, and the active pretreatment of potential faults cannot be carried out; and fourthly, the conventional state evaluation formula is mainly judged by adopting fixed weights or single parameters, cannot adapt to complex and changeable operation conditions, and is difficult to accurately quantify the aging degree and the residual life of the fuse. Therefore, developing a high-voltage fuse and an online monitoring method integrating multidimensional monitoring, self-adaptive self-diagnosis, dynamic life prediction and self-repairing linkage functions becomes an urgent need in the current power equipment field. Disclosure of Invention The invention aims to provide a self-diagnosis high-voltage fuse and an on-line monitoring method for the running state of the self-diagnosis high-voltage fuse, which can realize synchronous monitoring, self-adaption accurate diagnosis, dynamic residual life prediction and grading response of multidimensional parameters of the high-voltage fuse, so as to solve the technical defects of single monitoring parameters, low diagnosis precision and lack of self-repair linkage in the prior art and improve the running safety and reliability of a power system. The invention provides a self-diagnosis high-voltage fuse which comprises a fuse body, a fuse body and a fuse cover, wherein the fuse body is made of a novel self-repairing alloy material, and a micro-nano conductive repairing medium is embedded in the fuse body; The self-diagnosis integrated module is integrated in end caps at two ends of the fuse body and comprises a physical field sensor unit, a data processing unit, a digital twin virtual modeling unit, a self-calibration unit, a wireless communication unit and a self-repair triggering unit, wherein the physical field sensor unit synchronously acquires the voltage at two ends of the real-time running current of the fuse body, the body temperature, the surface strain and the environmental parameters; the auxiliary execution module is electrically connected with the self-diagnosis integrated module and comprises a miniature heat radiation module, an arc suppression device and a state indication unit. Preferably, the novel self-repairing alloy material adopted by the fuse body takes copper-silver alloy as a base material and is added with a nano alumina reinforcing phase, the micro-nano conductive repairing medium is micro-nano bismuth-tin alloy medium, the melting point is 120-150 ℃, and the fuse body is in a solid insulation state below 80 ℃. Preferably, the physical field sensor unit comprises a rogowski coil current sensor, a fiber bragg grating temperature sensor, a miniature strain sensor, a voltage sensor and an environment sensor. The invention also provides an on-line monitoring method for the running state of the self-diagnosis high-voltage fuse, which comprises the following steps: S1, multidimensional data acquisition and synchronous calibration, namely acquiring operation parameters and environment parameters of a fuse body according to sampling frequency through a physical field sensor unit, and correcting acquired data in real time based on a calibration factor