CN-121076699-B - Intelligent algorithm-based MCB and RCD cooperative distribution box control method and system

CN121076699BCN 121076699 BCN121076699 BCN 121076699BCN-121076699-B

Abstract

The invention relates to an MCB and RCD cooperative distribution box control method and system based on an intelligent algorithm, which belong to the technical field of low-voltage distribution protection and intelligent fault diagnosis and comprise the steps of S1, acquiring a discrete time current sequence; S2, processing by applying continuous wavelet transformation based on a discrete time current sequence to generate a time-frequency coefficient matrix, S3, calculating high-frequency energy integration according to the time-frequency coefficient matrix, S4, calculating time-frequency distribution entropy according to the time-frequency coefficient matrix, S5, calculating a waveform randomness factor according to the discrete time current sequence, S6, combining the high-frequency energy integration, the time-frequency distribution entropy and the waveform randomness factor, and generating an electrical disturbance characteristic signature through a nonlinear fusion model.

Inventors

HUANG XIEYUN
ZHANG QIANG
ZHOU WEN
GONG MINGCAI
ZHENG WEIJIAN
JIANG SONG

Assignees

泰姆电气(杭州)股份有限公司

Dates

Publication Date: 20260505
Application Date: 20251104

Claims (6)

1. The intelligent algorithm-based MCB and RCD cooperative distribution box control method is characterized by comprising the following steps of: S1, acquiring a discrete time current sequence; S2, processing by applying continuous wavelet transformation based on a discrete time current sequence to generate a time-frequency coefficient matrix; s3, calculating high-frequency energy integration according to the time-frequency coefficient matrix; S4, calculating a time-frequency distribution entropy according to the time-frequency coefficient matrix; S5, calculating a waveform randomness factor according to the discrete time current sequence; S6, combining high-frequency energy integration, time-frequency distribution entropy and waveform randomness factors, and generating an electric disturbance characteristic signature through a nonlinear fusion model; s7, dynamically generating an adaptive tolerance threshold based on a discrete time current sequence; s8, if the state duration time of the electrical disturbance characteristic signature which is larger than the self-adaptive tolerance threshold exceeds a preset confirmation window, outputting a synchronous tripping instruction to the MCB and the RCD, and if the electrical disturbance characteristic signature is smaller than or equal to the self-adaptive tolerance threshold, maintaining the current running state; Generating an electrical disturbance signature by a nonlinear fusion model, comprising: The method comprises the steps of adopting a maximum and minimum normalization function to respectively process high-frequency energy integration, time-frequency distribution entropy and waveform randomness factor to generate normalized characteristic parameters, fusing the normalized time-frequency distribution entropy and the waveform randomness factor through a nonlinear multiplication gating model, and multiplying the normalized high-frequency energy integration serving as the gating factor to generate a final characteristic signature; the boundary value required by the maximum and minimum normalization functions is determined by statistical analysis of a pre-marked electrical event waveform database, and the weight coefficient is determined by optimizing training on the electrical event waveform database by adopting a supervised machine learning algorithm.
2. The intelligent algorithm-based control method for the MCB and RCD collaborative distribution box according to claim 1, wherein the time-frequency distribution entropy is obtained by calculating shannon entropy of time-frequency energy distribution.
3. The intelligent algorithm-based MCB and RCD coordinated box control method according to claim 1, wherein the waveform randomness factor is obtained by calculating the variance of the residual signal formed by the difference between the discrete-time current sequence and the power frequency fundamental component.
4. The intelligent algorithm-based MCB and RCD coordinated block terminal control method of claim 1, wherein dynamically generating the adaptive tolerance threshold comprises: Setting a basic protection threshold value, calculating a short-term index moving average and a long-term index moving average of a circuit current effective value in parallel, calculating a normalized difference value of the short-term index moving average and the long-term index moving average, combining the basic protection threshold value and the normalized difference value, and introducing an exponential decay factor to generate an adaptive tolerance threshold value.
5. The intelligent algorithm-based MCB and RCD coordinated block terminal control method of claim 4, wherein an exponential decay factor is introduced for restoring the adaptive tolerance threshold from a boost value to a base protection threshold over time after a load change event is triggered; the triggering condition of the load change event is that the normalized difference value is larger than a preset event triggering threshold value.
6. An MCB and RCD cooperative distribution box control system based on an intelligent algorithm, which is applied to the MCB and RCD cooperative distribution box control method based on an intelligent algorithm as set forth in any one of claims 1 to 5, and is characterized by comprising: the data acquisition module is used for acquiring a discrete time current sequence; the characteristic calculation module is used for generating a time-frequency coefficient matrix based on the discrete time current sequence and calculating high-frequency energy integration, time-frequency distribution entropy and waveform randomness factors according to the time-frequency coefficient matrix and the discrete time current sequence; The characteristic signature generation module is used for generating an electric disturbance characteristic signature through a nonlinear fusion model by combining the high-frequency energy integration, the time-frequency distribution entropy and the waveform randomness factor; A dynamic threshold generation module for dynamically generating an adaptive tolerance threshold based on the discrete-time current sequence; And the cooperative protection decision module is used for outputting a synchronous tripping instruction if the state duration time of the electric disturbance characteristic signature larger than the self-adaptive tolerance threshold exceeds a preset confirmation window, and maintaining the current running state of the control system if the electric disturbance characteristic signature is smaller than or equal to the self-adaptive tolerance threshold.

Description

Intelligent algorithm-based MCB and RCD cooperative distribution box control method and system Technical Field The invention relates to the technical field of low-voltage power distribution protection and intelligent fault diagnosis, in particular to an MCB and RCD collaborative distribution box control method and system based on an intelligent algorithm. Background In low voltage distribution systems, conventional line protection devices, such as miniature circuit breakers MCB and residual current operated protectors RCD, whose protection logic relies primarily on monitoring a single physical quantity, either the current rating or the residual current, which are typically determined using fixed, preset operating thresholds. The conventional protection mode has inherent limitation, and on one hand, the amplitude of surge current generated during motor starting, frequency converter operation or switching of high-power equipment can exceed a preset threshold value in a short time. These inrush currents are benign, transient operating disturbances in nature, but conventional protection devices may experience unwanted tripping, i.e., false protection, due to their inability to effectively identify transient characteristics, which severely impacts the continuity and reliability of the power supply. On the other hand, for series or parallel arc faults caused by ageing of the line insulation, loosening of the connection, etc., especially in the early stages thereof, the increase of the effective value of the fault current may not be significant enough to trigger the action threshold of the conventional protection device, however, such faults have a great fire hazard. The traditional method only pays attention to the current amplitude, but ignores the unique physical characteristics of arc faults in the dimensions of high frequency, randomness and the like, thereby causing the risk of leakage protection and seriously threatening the safety of life and property. The root of the limitation is that the traditional protection technology lacks the capability of carrying out deep analysis on the current signal, and cannot comprehensively reveal the intrinsic physical essence of the electrical event from multiple dimensions such as energy, time-frequency distribution form, waveform randomness and the like, so that dangerous malignant faults and benign operation disturbance are difficult to accurately distinguish in a complex electricity utilization environment. The above information disclosed in the above background section is only for enhancement of understanding of the background of the disclosure and therefore it may include information that does not form the prior art that is already known to those of ordinary skill in the art. Disclosure of Invention The invention aims to provide an MCB and RCD cooperative distribution box control method and system based on an intelligent algorithm, so as to solve the problems in the background art. S1, acquiring a discrete time current sequence; S2, processing by applying continuous wavelet transformation based on a discrete time current sequence to generate a time-frequency coefficient matrix; s3, calculating high-frequency energy integration according to the time-frequency coefficient matrix; S4, calculating a time-frequency distribution entropy according to the time-frequency coefficient matrix; S5, calculating a waveform randomness factor according to the discrete time current sequence; S6, combining high-frequency energy integration, time-frequency distribution entropy and waveform randomness factors, and generating an electric disturbance characteristic signature through a nonlinear fusion model; s7, dynamically generating an adaptive tolerance threshold based on a discrete time current sequence; S8, if the state duration time of the electrical disturbance characteristic signature larger than the self-adaptive tolerance threshold exceeds a preset confirmation window, outputting a synchronous tripping instruction to the MCB and the RCD, and if the electrical disturbance characteristic signature is smaller than or equal to the self-adaptive tolerance threshold, maintaining the current running state. Preferably, generating the electrical disturbance signature by a nonlinear fusion model includes: the method comprises the steps of adopting a maximum and minimum normalization function to respectively process high-frequency energy integration, time-frequency distribution entropy and waveform randomness factor to generate normalized characteristic parameters, fusing the normalized time-frequency distribution entropy and the waveform randomness factor through a nonlinear multiplication gating model, carrying out weighted summation on the normalized time-frequency distribution entropy and the waveform randomness factor, and multiplying the normalized high-frequency energy integration serving as the gating factor to generate a final characteristic signature. Preferably, the boundary val