永磁真空断路器同步控制关键技术的研究  

Study on Key Technology of Synchronous Control for Permanent Magnet Vacuum Circuit Breakers

作　　者：王玉梅[1] 李国锴 WANG Yu-mei;LI Guo-kai(School of Electrical Engineering and Automation,Henan Polytechnic University,Jiaozuo 454000,China)

机构地区：[1]河南理工大学电气工程与自动化学院,河南焦作454000

出　　处：《控制工程》2021年第7期1451-1459,共9页Control Engineering of China

基　　金：河南省产学研项目(132107000027);河南省科技攻关项目(112102210004)。

摘　　要：为了提高相控开关投切精度,更好抑制产生的过电压和涌流,针对断路器同步控制关键技术中的短路电流过零点预测和操动机构动作时间预测进行了研究。根据短路故障电流的一般表达式,利用自适应的最小均方算法求得其特征参数,预测电流过零点时刻,预测误差0.4ms,满足同步分断的精度要求且实时性好。提出云遗传算法优化BP神经网络,云理论优化遗传算法的交叉和变异概率,云遗传算法优化网络的初始权值和阈值,在保证全局寻优的高可靠性并加速收敛的基础上,建立不同环境温度和控制电压下预测机构动作时间的数学模型。经仿真分析对比,各项误差均有减少,该方法可行。In order to improve the switching accuracy of phase-controlled switches and better suppress the overvoltage and inrush current generated,the prediction of the zero-crossing point of short-circuit current and the action time of the operating mechanism in key synchronous control technologies for circuit breakers are studied.On the basis of the general expression of short-circuit fault current,the adaptive least mean square algorithm is used to obtain the characteristic parameters and predict the zero-crossing time of the current.The prediction error is 0.4 ms,which meets the accuracy requirement of synchronous interruption and has good real-time performance.The cloud genetic algorithm for optimizing a BP neural network,the cloud genetic algorithm for optimizing the initial weights and thresholds of the network,and the cloud theory for optimizing the crossover and mutation probability of the genetic algorithm are proposed in this paper.Mathematical models for predicting the action time of the mechanism under different ambient temperatures and control voltages are established on the basis of guaranteeing the high reliability of global optimization and accelerating convergence.The simulation analysis and comparison show that all errors are reduced,and this method is feasible.

关 键 词：永磁机构 同步控制 过零点 云遗传算法 BP神经网络 

分 类 号：TM561[电气工程—电器]