基于EMD-TEO及信号能量分析法的主导低频振荡模式识别

提出了一种识别电力系统主导低频振荡模式的新方法。该方法综合了经验模态分解(EMD)、Teager能量算子(TEO)及信号能量分析法,借助经验模态分解处理非平稳信号,不需考虑定阶问题,扩展了信号能量分析法的应用范围;利用Teager能量算子的快速响应能力及健壮性,提高了频率辨识精度;根据二阶模型下的信号能量分析法,提出了一种阻尼比的简化算法。分别在WEPRI-36系统和实际电网中进行了验证,并与Q-R特征值分析法及Prony算法进行了准确性比较。结果表明,新方法对非线性系统的适应性比Prony算法强,可用于低频振荡主导模式的有效识别。

Identification of the Dominant Inertial Mode Based on EMD-TEO and Signal Energy Method

A new method about dominant inertial mode identification for power systems was proposed.The method integrated empirical mode decomposition(EMD),Teager energy operator(TEO) and signal energy method.EMD expands the applied scene of signal energy method without considering the ordering issues.The fast-response ability and robustness of TEO leads to a higher accuracy for frequency identification.Based on the second-order model signal energy method,the algorithm for dumping ratio was simplified.The method was verified in WEPRI-36 and actual grid by Q-R eigenvalue analysis and Prony algorithm.Its fitting-curve adaption to unlinear system is better than Prony algorithm,and it can be effectively used to detect the dominant inertial mode.