基于优化VMD和RF的矿井小电流接地选线方法

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中图分类号：TD61 文献标志码：A

Line selection method for mine small-current grounding based on optimized VMD and RF

ZHUJun12，LI Jiachengl，ZHAO Guotong1，WANG Xiaodong1，YANG Ming’，WEI Xiangyuan³ （1.SchoolofElectricalEngineeringandAutomation，HenanPolytechnic University，Jaozuo454oo，China;

2.HenanKeyLaboratoryofInteligentDetectionandControlofCoalMineEquipment，Jiaozuo454003，China;

3.XJ Electric Corporation， Xuchang 461000， China）

Abstract： In underground small-current grounding power supply systems， the decomposition performance of the single-phase grounding fault line selection method based on Variational Mode Decomposition （VMD） depends heavilyon the selection ofparameters suchas thepenaltyfactorand thenumber of decomposition modes， whichare diffculttosetuniformlyfordifferentsignals.Toaddressthis problem，afaultlineselection method for mine smal-current grounding based on optimized VMD and Random Forest （RF） was proposed.The Crested Porcupine Optimizer（CPO）was used to adaptively optimize the key parameters of VMD，including the penalty factor and the number of decomposition modes.A simulation model of underground power supply lines was established on the PSCAD/EMTDC platform.Zero-sequence current data under different fault conditions were obtainedbychanging the groundingresistance，initial fault phase angle，fault line，and fault location.The optimized VMD was applied to decompose the fault zero-sequence current signals.The modal components of each line were extracted，and their sample entropy was calculated to construct multidimensional feature vectors that reflected the complexity and nonlinear characteristics of the signals.The feature vectors were then input into the RFclassifier for trainingand identification toachieve accurate determinationofthe fault line.The simulation results showed that the accuracy of the RF classifier was 98.3% ，which was higher than that of Convolutional Neural Network （CNN）， Long Short-Term Memory （LSTM） network， and Extreme Learning Machine （ELM）. The experimental results showed that the proposed method achieved a fault identification accuracy of 97.5% unaffected by factors suchas transitionresistance，initial phase angle，and fault location，demonstrating high accuracy and applicability.

Key words： mine power supply； fault line selection; Variational Mode Decomposition; Random Forest; Crested Porcupine Optimizer; sample entropy

0引言

我国煤矿井下供电网络一般采用小电流接地方式运行，单相接地故障占所有故障的 80% 以上[1]。（剩余13353字）