电能质量扰动, 扰动信号, 分类回归树, 主成分分析, 多特征融合, 信号调制

Power quality disturbances cause distortion of voltage and current waveforms, and different forms of distortion result in complex characteristics of power quality signals in both time and frequency domains,increasing the difficulty of signal analysis and processing. Therefore, based on multi feature fusion, a modulation algorithm for signals of power quality disturbance is proposed to make signal detection and recognition easier and more efficient. From all the characteristics of power quality disturbance signals obtained from S-transform and wavelet transform, using classification regression tree and Gini importance, representative time-domain signal features and frequency-domain signal features are selected, and multi feature fusion is completed through principal component analysis. According to the LSTM ( Long Short-Term Memory) based fusion feature, the category of power quality disturbance signal is given, and the modulated power quality disturbance signal is output by the signal generator. The experimental results show that the signal-to-noise ratio of the selected signal features exceeds 90 dB, indicating strong representational ability. The signal modulated by this algorithm has strong recognizability, and both single and complex types can be accurately identified. The frequency deviation fluctuates slightly within the range of ±0. 1 Hz, indicating a significant improvement in power quality.

power quality disturbance, disturbance signal, classification regression tree, principal component analysis, multi feature fusion, signal modulation