摘要
准确地辨识同步电机参数,是分析电力系统运行和控制系统设计的前提。利用最小二乘或卡尔曼滤波等方法辨识电机三相同时突然短路试验数据,是传统电机参数辨识的主要方式。受同步电机三相同时突然短路的可操作性和电机参数模型的强非线性等因素的影响,电机瞬态参数难以准确测量。利用改进的小波变换和人工神经元网络辨识参数的新方法,结合容易实现的同步电机线对线突然短路试验,辨识得到了三相同步电机的瞬态参数,有效提高了电机参数的辨识精度。仿真试验表明,该方法是有效的。
An accurate identification of the electromagnetic parameters of synchronous electric machine is the precondition for analyzing power system running and controlling system design. It is a tradi- tional way to identify the parameters of sudden three-phase short circuit with least-square and Kalman filtering. Influenced by the maneuverability of the synchronous machine sudden three-phase short circuit and the nonlinear of the machine parameter model, the machine parameters are difficult to measure. By combining the new method of improved wave transform and artificial neuron network with the synchronous machine sudden line-to-line short circuit test in identifying the parameters, the transient parameters of three phase synchronous machine were identified. The identifying precision of the parameters was improved. The simulation test proves that the method is effective.
出处
《海军工程大学学报》
CAS
北大核心
2009年第5期18-21,共4页
Journal of Naval University of Engineering
基金
国家自然科学基金委员会创新研究群体科学基金资助项目(50721063)
关键词
电机参数
线对线短路
小波变换
machine parameters
line-to-line short circuit
wave transform
