基于灰狼算法优化SVM的油浸式变压器电力故障自动化监测系统

Automatic Monitoring System for Power Failure of Oil-immersed Transformer Based on Grey Wolf Algorithm Optimization SVM

针对现有系统存在的电力故障监测精度低、控制性能差等问题,提出基于灰狼算法优化SVM的油浸式变压器电力故障自动化监测系统。选取与配置传感器设备、监控与显示设备,提取与融合油浸式变压器监测数据特征,引入灰狼算法优化SVM参数,制定变压器电力故障自动化监测程序,设置合理的安全阈值,在电力故障发生前对其进行报警及显示,实现对电力故障的自动化监测与控制。实验结果显示,应用设计系统获得的机械振动信号特征提取结果与实际机械振动信号特征相同,电力故障监测结果与模拟工况一致。

Aiming at the problems of low monitoring accuracy and poor control performance in the existing system,an automatic monitoring system for oil-immersed transformer power failure based on SVM optimized by grey wolf algorithm is proposed.Select and configure sensor equipment,monitoring and display equipment,extract and fuse oil-immersed transformer monitoring data features,introduce gray wolf algorithm to optimize SVM parameters,develop transformer power fault automatic monitoring procedures,set a reasonable safety threshold,alarm and display power faults before they occur.Thus,the automatic monitoring and control of power failure is realized.The experimental results show that the mechanical vibration signal feature extraction results obtained by the design system are the same as the actual mechanical vibration signal feature,the power fault monitoring results are consistent with the simulated condition.