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中文摘要：
针对电能质量混合扰动分类问题,提出一种基于时频域多特征量的分类新方法。首先利用聚类经验模型分解方法和改进不完全S变换对扰动信号进行处理,并提取9个时频域特征值;然后,将特征量输入到分块化的自动分类系统中进行扰动识别。该方法充分考虑单一扰动之间的相互干扰,并通过互补的时频域特征量进行有效的抑制。仿真结果表明,在一定的噪声条件下,所提方法可有效分类电压暂降、电压暂升、电压短时中断、脉冲暂态、振荡暂态、谐波和闪变等电能质量扰动及其组合而成的混合扰动。
英文摘要：
A new method for power quality mixed disturbance classification, based on time-frequency domain multiple features, was presented in the paper. Firstly, the disturbance signals were processed with ensemble empirical mode decomposition and modified incomplete S-transformation, and nine time-frequency domain characteristics were extracted. Then, the characteristies were input into the sub-block of the automatic classification system for the disturbance identification. In this method, interferences between the single disturbances were fully considered and effectively suppressed through the complementary amount of time and frequency domain features. The simulation results show that the method can effectively recognize the power quality mixed disturbances with the noise including voltage sag, voltage swell, voltage interruption, impulsive transient, oscillation transient, harmonics, flicker and their mixed ones.
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A New Classification Method for Transient Power Quality Combining Spectral Kurtosis with Neural Netw
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