文章以线圈电流波形的时间和电流值为特征量,断路器5 种典型故障为输出量,采用改进黑猩猩算法(Improved Chimp Optimization Algorithm,ICOA)对长短时记忆(Long Short Term Memory,LSTM)神经网络的三个关键参数进行优化,构建了基于ICOA-LSTM的高压断路器故障诊断模型.采用断路器故障数据进行仿真,并与现有断路器故障诊断模型进行对比分析.对比测试结果表明,ICOA-LSTM模型的诊断精度更高,计算时间更短,验证了ICOA-LSTM模型的优越性和有效性.
Fault Diagnosis of High-Voltage Circuit Breakers Based on Optimized ICOA-LSTM
This paper takes the time and current value of the coil current waveform as characteristic varia-bles,and five typical faults of circuit breakers as output variables.The Improved Chimp Optimization Algo-rithm(ICOA)is used to optimize the three key parameters of long short-term memory(LSTM)neural net-work,and a high-voltage circuit breaker fault diagnosis model based on ICOA-LSTM is constructed.Simu-lations were conducted using circuit breaker fault data and compared with existing circuit breaker fault di-agnosis models.The comparative test results show that the ICOA-LSTM model has higher diagnostic accura-cy and shorter calculation time,verifying the superiority and effectiveness of the ICOA-LSTM model.
万方数据
维普
