摘要
将一种新的非平稳信号的处理方法—Hilbert-Huang变换(HHT)应用于同步电机的参数辨识中。提出了基于HHT方法的短路电流处理新方法,该方法以经验模态分解(empiricalmodedecomposition,EMD)为基础,构成一种新型的时空滤波方法,从强噪声背景下的短路电流数据中有效地提取出了基波分量和直流分量。克服了传统处理方法精度低的缺点,并且不存在小波基选取问题,具有处理精度高、自适应性强的特点。还提出了基于稳健回归算法的直流分量辨识算法,以及基于Hilbert变换和非线性变量优化(NLO)的基波分量辨识算法,实现了同步电机瞬态和超瞬态参数的精确辨识。用加入滤波环节的F-EMD,降低了电流分量分离过程中的计算量。仿真分析和试验数据分析验证了该方法的有效性。
Introduces a novel technique-HHT to the electromagnetic parameter identification of synchronous machine. First of all, a new analysis method based on HI-IT is proposed to deal with short-circuit current data. It devises a time-space filtering using EMD and extracts the DC and fundamental component from the transient short-circuit current. This way overcomes the low-accuracy shortcoming of traditional method as well as the difficulty in choosing Wavelet, so it has high accuracy and strong self-adaptability. In addition, this article presents a technique using robust regression algorithm to analysis the DC current component, as well as a new algorithm based on I-filbert and NLO for the analysis of fundamental current component , consequently, realizes accurate identification of transient and subtransient parameters. Besides, to decrease the computation during the current component isolating, an alternative approach based on low-pass filtering and EMD is stated. The simulation and experimental results demonstrate the effectiveness of the proposed technique.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2006年第8期153-158,共6页
Proceedings of the CSEE
关键词
参数辨识
同步电机
希尔伯特-黄变换
时空滤波
稳健回归
非线性优化
parameter identification
synchronous machine
hilbert-huang transform
time-space filtering
robust regression algorithm
non-linear optimization
