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CN-121412869-B - Intelligent fault monitoring method and device for battery exchange cabinet and storage medium

CN121412869BCN 121412869 BCN121412869 BCN 121412869BCN-121412869-B

Abstract

The invention relates to the technical field of fault prediction, in particular to an intelligent fault monitoring method, device and storage medium for a power conversion cabinet, wherein the method comprises the steps of collecting port data and shrapnel data of the power conversion cabinet; the method comprises the steps of extracting waveform characteristics of an inserting moment voltage waveform, constructing an arc intensity characteristic according to the waveform characteristics, constructing an elastic piece abnormal index based on elastic piece contact pressure, judging an elastic piece fatigue state, constructing an elastic piece abrasion index according to an inserting moment vibration signal, constructing a thermal shock index according to the change rate of the elastic piece temperature in the initial stage of a charging session, updating the elastic piece fatigue state based on the elastic piece abrasion index and the thermal shock index, carrying out fault early warning according to the arc intensity characteristic and the elastic piece fatigue state in a management period, and adjusting the target elastic piece contact pressure of the next management period according to a fault early warning result and elastic piece deformation data. The invention improves the operation efficiency of the power conversion cabinet.

Inventors

  • LI HAO
  • DENG YANG
  • ZHANG HUIZHOU

Assignees

  • 北京汇森通科技有限公司

Dates

Publication Date
20260512
Application Date
20251029

Claims (9)

  1. 1. The intelligent fault monitoring method for the power exchange cabinet is characterized by comprising the following steps of: collecting port data and spring plate data of the battery changing cabinet; Extracting waveform characteristics of the waveform of the inserted instant voltage, and constructing arc intensity characteristics according to the waveform characteristics; constructing an elastic piece abnormality index based on the elastic piece contact pressure, and judging the fatigue state of the elastic piece; Constructing a dome wear index based on the vibration signal at the insertion instant in the management period: Calculating the root mean square value of the vibration signal acceleration at each insertion moment in the management period to be RMSa, and constructing the shrapnel abrasion index according to the reference value RMSb of the root mean square of the vibration signal acceleration at the shrapnel insertion moment: Setting the spring wear index to 0 when RMSa/RMSb is less than or equal to 1, otherwise setting the spring wear index to [1-exp (1-RMSa/RMSb) ], constructing a thermal shock index according to the change rate of the spring temperature at the initial stage of the charging session, and updating the spring fatigue state based on the spring wear index and the thermal shock index; and carrying out fault early warning according to the arc intensity characteristics and the spring fatigue state in the management period, and adjusting the target spring contact pressure of the next management period according to the fault early warning result and the spring deformation data.
  2. 2. The intelligent fault monitoring method for a battery-changing cabinet according to claim 1, wherein a voltage minimum value Vmi of an i-th insertion instant voltage waveform in a management period is extracted, and a voltage instant drop peak value Δvi is calculated, Δvi=vs-Vmi, vs being a rated voltage; Starting from a trigger point, searching backwards, namely, marking as Tsi when the voltage is lower than Vs x 95% for the first time, starting searching backwards after the Tsi point, marking as Tei when the voltage is first entered and maintained within 98% -x Vs-102% -x Vs for more than 10 milliseconds, and marking as Tdi when the time difference between Tei and Tsi is used as voltage drop duration; constructing an arc intensity index ASi based on a voltage instantaneous drop peak value DeltaVi and a voltage drop duration Tdi, setting asi=x1×DeltaVi/Vs+x2×tanh (Tdi/Ty), sequencing all the arc intensity indexes in a management period, taking the maximum value AS an arc intensity characteristic of the current management period, and recording AS AS; Where Ty is a preset duration, x1 is a drop weight, x2 is a duration weight, x1+x2=1.
  3. 3. The intelligent fault monitoring method of a power exchange cabinet according to claim 2, wherein an average value of contact pressure of the shrapnel in a management period is calculated and recorded as Pa, an shrapnel abnormality index SF is constructed, and sf=max (0, 1-Pa/Pe) is set; when SF is smaller than or equal to a preset fatigue index s0, judging that the spring plate fatigue state in the current management period is a normal state, otherwise, judging that the spring plate fatigue state in the current management period is an abnormal state; Wherein Pe is the rated contact pressure of the spring plate.
  4. 4. The intelligent fault monitoring method of a battery cabinet according to claim 3, wherein the thermal shock index is constructed according to the change rate of the initial shrapnel temperature of each charging session in the management period: Extracting the maximum value of the change rate of the initial shrapnel temperature of the jth charging session in the management period to be Tmj, comparing the maximum value with the preset temperature change rate t, judging that the change rate of the initial shrapnel temperature of the charging session is normal when Tmj is smaller than or equal to t, otherwise, judging that the change rate of the initial shrapnel temperature of the charging session is abnormal, counting the number of charging sessions with abnormal change rate of the shrapnel temperature in the management period to be m1, counting the number of charging sessions in the management period to be m2, and taking the ratio of m1 to m2 as a thermal shock index.
  5. 5. The intelligent fault monitoring method for a battery exchange cabinet according to claim 4, wherein an anomaly factor is determined according to the wear index and the thermal shock index of the shrapnel, and the fatigue state of the shrapnel is updated according to the anomaly factor: the expression of the anomaly factor is u=w1×shrapnel wear index+w2×thermal shock index, w1 is wear weight, w2 is thermal shock weight, w1+w2=1; The preset fatigue index is updated based on the abnormality factor, and the updated preset fatigue index is set to s1.
  6. 6. The intelligent fault monitoring method for the battery exchange cabinet according to claim 5, wherein a risk coefficient is constructed according to the arc intensity characteristics and the spring fatigue state in the management period, and fault early warning is performed according to the risk coefficient: setting a risk factor as F1 when the spring fatigue state is normal, and setting f1=u1×min (1, as/a 0); When the spring fatigue state is abnormal, setting a risk coefficient as F2, and setting F2=u1×min (1, AS/a 0) +u2×lg [3× (spring abnormality index-preset fatigue index) +1]/lg4; Wherein u1 is an arc weight, u2 is an elastic piece fatigue weight, u1+u2=1, and a0 is a preset arc intensity; and carrying out fault risk early warning on the user when the risk coefficient is greater than or equal to the preset risk coefficient, otherwise, not carrying out fault risk early warning on the user.
  7. 7. The intelligent fault monitoring method of a battery exchange cabinet according to claim 6, wherein an average value mu epsilon of the elastic piece deformation data of all successful insertion events in a management period is calculated, a standard deviation sigma epsilon of the elastic piece deformation data of all successful insertion events in the management period is calculated, and a structural health index SH is constructed; When carrying out fault risk early warning on a user in a current management period, if the structural health index SH is smaller than or equal to a preset structural health index, adjusting the contact pressure of a target shrapnel in a next management period to be P1, setting P1=p0X (1+beta), wherein P0 is the contact pressure of the target shrapnel in the current management period, and beta is a preset correction coefficient; and when the fault risk early warning is not carried out on the user in the current management period, the contact pressure of the target elastic sheet in the next management period is not adjusted.
  8. 8. An intelligent fault monitoring device for a power conversion cabinet, which is applied to the intelligent fault monitoring method for the power conversion cabinet according to any one of claims 1-7, and is characterized by comprising the following steps: the data acquisition unit is used for acquiring port data of the battery changing cabinet and spring piece data; The arc characteristic construction unit is used for extracting waveform characteristics of the waveform of the inserted instant voltage and constructing arc intensity characteristics according to the waveform characteristics; the elastic piece fatigue analysis unit is used for constructing an elastic piece abnormality index based on the elastic piece contact pressure and judging an elastic piece fatigue state; The updating unit is used for constructing an elastic piece abrasion index according to the vibration signal at the moment of insertion, constructing a thermal shock index according to the change rate of the elastic piece temperature at the initial stage of the charging session, and updating the elastic piece fatigue state based on the elastic piece abrasion index and the thermal shock index; The fault monitoring unit is used for carrying out fault early warning according to the arc intensity characteristics and the spring fatigue state in the management period, and adjusting the target spring contact pressure of the next management period according to the fault early warning result and the spring deformation data.
  9. 9. A computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is used to control an electronic device where the computer readable storage medium is located to execute the intelligent fault monitoring method of the power conversion cabinet according to any one of claims 1-7 when running.

Description

Intelligent fault monitoring method and device for battery exchange cabinet and storage medium Technical Field The invention relates to the technical field of fault prediction, in particular to an intelligent fault monitoring method and device for a power conversion cabinet and a storage medium. Background Currently, an effective on-line monitoring and early warning mechanism is generally lacking in a port of a battery changing cabinet, particularly in a spring plate system of a battery pack plugging interface. The traditional maintenance mode mainly depends on regular inspection and post-maintenance, and is difficult to cope with progressive faults under the coupling action of multiple factors such as mechanical abrasion, contact pressure attenuation, arc erosion, thermal shock and the like caused by frequent plugging. The existing scheme is limited to monitoring of single parameters (such as on-off state), potential risks in the electric-mechanical-thermal composite working state of the connector cannot be comprehensively captured, so that failure prediction accuracy is insufficient, maintenance cost is high, and safety concerns caused by connection degradation exist. Disclosure of Invention The invention aims to provide an intelligent fault monitoring method and device for a battery exchange cabinet and a storage medium, so as to solve at least one of the problems in the prior art. In order to achieve the above purpose, the invention adopts the following technical scheme: An intelligent fault monitoring method for a battery exchange cabinet comprises the following steps: collecting port data and spring plate data of the battery changing cabinet; Extracting waveform characteristics of the waveform of the inserted instant voltage, and constructing arc intensity characteristics according to the waveform characteristics; constructing an elastic piece abnormality index based on the elastic piece contact pressure, and judging the fatigue state of the elastic piece; constructing an elastic piece abrasion index according to the vibration signal at the moment of insertion, constructing a thermal shock index according to the change rate of the elastic piece temperature at the initial stage of a charging session, and updating the elastic piece fatigue state based on the elastic piece abrasion index and the thermal shock index; and carrying out fault early warning according to the arc intensity characteristics and the spring fatigue state in the management period, and adjusting the target spring contact pressure of the next management period according to the fault early warning result and the spring deformation data. Optionally, extracting the voltage minimum value Vmi of the ith inserted transient voltage waveform in the management period, and calculating the voltage transient dip peak value Δvi, Δvi=vs-Vmi, wherein Vs is the rated voltage; Starting from a trigger point, searching backwards, namely, marking as Tsi when the voltage is lower than Vs x 95% for the first time, starting searching backwards after the Tsi point, marking as Tei when the voltage is first entered and maintained within 98% -x Vs-102% -x Vs for more than 10 milliseconds, and marking as Tdi when the time difference between Tei and Tsi is used as voltage drop duration; constructing an arc intensity index ASi based on a voltage instantaneous drop peak value DeltaVi and a voltage drop duration Tdi, setting asi=x1×DeltaVi/Vs+x2×tanh (Tdi/Ty), sequencing all the arc intensity indexes in a management period, taking the maximum value AS an arc intensity characteristic of the current management period, and recording AS AS; Where Ty is a preset duration, x1 is a drop weight, x2 is a duration weight, x1+x2=1. Optionally, calculating an average value of the contact pressure of the shrapnel in the management period, recording as Pa, constructing an abnormality index SF of the shrapnel, and setting SF=max (0, 1-Pa/Pe); when SF is smaller than or equal to a preset fatigue index s0, judging that the spring plate fatigue state in the current management period is a normal state, otherwise, judging that the spring plate fatigue state in the current management period is an abnormal state; Wherein Pe is the rated contact pressure of the spring plate. Optionally, the dome wear index is constructed based on the vibration signal at the insertion instant in the management period: Calculating the root mean square value of the vibration signal acceleration at each insertion moment in the management period to be RMSa, and constructing the shrapnel abrasion index according to the reference value RMSb of the root mean square of the vibration signal acceleration at the shrapnel insertion moment: When RMSa/RMSb is less than or equal to 1, the spring wear index is set to 0, whereas the spring wear index is set to [1-exp (1-RMSa/RMSb) ]. Optionally, the thermal shock index is constructed according to the rate of change of the initial dome temperature of each charging session i