An effective power quality prediction for regional power grid can provide valuable references and contribute to the discovering and solving of power quality problems. So a predicting model for power quality steady sta...An effective power quality prediction for regional power grid can provide valuable references and contribute to the discovering and solving of power quality problems. So a predicting model for power quality steady state index based on chaotic theory and least squares support vector machine (LSSVM) is proposed in this paper. At first, the phase space reconstruction of original power quality data is performed to form a new data space containing the attractor. The new data space is used as training samples for the LSSVM. Then in order to predict power quality steady state index accurately, the particle swarm algorithm is adopted to optimize parameters of the LSSVM model. According to the simulation results based on power quality data measured in a certain distribution network, the model applies to several indexes with higher forecasting accuracy and strong practicability.展开更多
针对永磁同步直线电机(p e r m a n e n tm a g n e t synchronouslinearmotor,PMSLM)局部退磁故障问题,采用粒子群优化最小二乘支持向量机(particleswarm optimization-leastsquaressupportvectormachine,PSO-LSSVM)分类模型,实现对PM...针对永磁同步直线电机(p e r m a n e n tm a g n e t synchronouslinearmotor,PMSLM)局部退磁故障问题,采用粒子群优化最小二乘支持向量机(particleswarm optimization-leastsquaressupportvectormachine,PSO-LSSVM)分类模型,实现对PMSLM局部退磁故障的准确识别。首先,基于等效磁化强度法,建立PMSLM局部退磁故障解析模型,引出包含故障特征信息的多峰图;其次,通过对比分析不同故障类型的多峰图,提取峰个数、峰起始位置、峰值比、退磁程度等参数,构造故障特征量;最后,采用PSO-LSSVM算法,建立用于识别PMSLM局部退磁故障的分类模型。有限元仿真实验和样机实验表明,该方法能够准确离线识别PMSLM局部退磁故障,识别率达到100%。展开更多
文摘An effective power quality prediction for regional power grid can provide valuable references and contribute to the discovering and solving of power quality problems. So a predicting model for power quality steady state index based on chaotic theory and least squares support vector machine (LSSVM) is proposed in this paper. At first, the phase space reconstruction of original power quality data is performed to form a new data space containing the attractor. The new data space is used as training samples for the LSSVM. Then in order to predict power quality steady state index accurately, the particle swarm algorithm is adopted to optimize parameters of the LSSVM model. According to the simulation results based on power quality data measured in a certain distribution network, the model applies to several indexes with higher forecasting accuracy and strong practicability.
文摘针对永磁同步直线电机(p e r m a n e n tm a g n e t synchronouslinearmotor,PMSLM)局部退磁故障问题,采用粒子群优化最小二乘支持向量机(particleswarm optimization-leastsquaressupportvectormachine,PSO-LSSVM)分类模型,实现对PMSLM局部退磁故障的准确识别。首先,基于等效磁化强度法,建立PMSLM局部退磁故障解析模型,引出包含故障特征信息的多峰图;其次,通过对比分析不同故障类型的多峰图,提取峰个数、峰起始位置、峰值比、退磁程度等参数,构造故障特征量;最后,采用PSO-LSSVM算法,建立用于识别PMSLM局部退磁故障的分类模型。有限元仿真实验和样机实验表明,该方法能够准确离线识别PMSLM局部退磁故障,识别率达到100%。
