| In recent years,power cables have been widely used in urban distribution networks due to their advantages of power supply safety and high reliability.However,during the operation of the medium-voltage distribution cable,its local insulation performance will decrease due to its own defects,environmental impact and external damage,and may cause multiple arc faults with extremely short time and self-recovery in this part.This paper calls such faults as cable latent faults,which will accelerate the deterioration of cable insulation and often eventually develop into permanent ground faults.However,due to its short duration and weak characteristics,it is difficult for relay protection devices to detect such faults,which are often regarded as transient disturbances,which makes it difficult to detect and identify cable latent faults.In this context,the research is carried out around the identification method of latent faults in medium voltage distribution cables,and the main work is as follows.(1)The development mechanism and fault characteristics of cable latent fault are analyzed.A typical 10 kV small resistance grounding cable distribution network model is built in PSCAD/EMTDC platform.Based on Kizilcay arc model,an equivalent model suitable for cable latent fault of small resistance grounding distribution system is established.The waveform of electrical quantity obtained by simulation conforms to the waveform characteristics of arc fault,which verifies the effectiveness of the cable latent fault model built in this paper.The influence of three model parameters on latent fault characteristics is studied by control variable experiments,which establishes the parameter basis for the generation of sample data.A constant impedance grounding fault,capacitor switching and load change disturbance module are built to provide data basis for subsequent fault identification research.(2)A data-driven identification model for cable latent faults based on multi-dimensional multi-domain feature extraction and optimization is proposed.The multi-dimensional feature analysis and extraction of fault current samples in time domain,frequency domain and time-frequency domain are carried out,and the validity verification and principal component analysis of the extracted features are carried out to optimize the fault feature vector.An intelligent identification model of cable latent fault based on Extreme Learning Machine(ELM)is established.The feature vector is used as the model input,and the random initial parameters of ELM algorithm are optimized by particle swarm optimization algorithm.The comparison with other classification models proves the advantages of the model established in this paper in terms of classification accuracy and training time.(3)A cable latent fault identification model based on data-knowledge joint-driven is proposed.The theoretical knowledge of the data-knowledge joint-driven model is introduced,and the empirical knowledge for cable latent fault identification is extracted.Combining it with the data-driven model based on ELM established above,using empirical knowledge to provide guidance for the rule mining of data-driven algorithms,the concept of conflict function is proposed and its calculation rules are defined.Combining it with the loss function to construct a knowledge function,a data-knowledge joint-driven cable latent fault identification model is established.The particle swarm optimization algorithm is used to optimize the model parameters,and the random factor and trust degree parameters are established to adjust the participation of domain experience knowledge in the fusion model,so as to reduce the adverse effect on the joint drive model training when the experience knowledge is wrong.By comparing it with multiple indicators of the pure data-driven model in multiple random tests under two typical operating scenarios,it is proved that the introduction of empirical knowledge can effectively improve the generalization,stability and security of the model. |
PDF Full Text Request