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首页> 外文期刊>Electric power systems research >Power Quality Disturbances Recognition Using Modified S Transform and Parallel Stack Sparse Auto-encoder
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Power Quality Disturbances Recognition Using Modified S Transform and Parallel Stack Sparse Auto-encoder

机译：电源质量干扰使用修改的S变换和并联堆栈稀疏自动编码器识别

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摘要

The effective automatic recognition and classification of power quality (PQ) disturbance is of significance to the control of power grid pollution before any reasonable solution is taken. In this paper, a novel method to PQ disturbances recognition is proposed based on the modified S transform (MST) and parallel stacked sparse auto encoder (PSSAE). A Kaiser window is used in MST for a better energy concentration in time-frequency matrix. Thereafter, not only the time-frequency matrix but also the Fourier transform spectrum is utilized to automatically extract features, as input of the two sub-model in PSSAE. Furthermore, the dimensionality reduction and visual analysis of features are achieved as an example. The recognition of PQ disturbances is then identified with the softmax classifier. The effectiveness and robustness of the proposed algorithm is validated by conducting a series of experiments with different types of single and combined signals.
机译：在采用任何合理解决方案之前，有效的自动识别和功率质量（PQ）干扰对电网污染的控制具有重要意义。本文基于修改的S变换（MST）和并联堆积稀疏自动编码器（PSSAE）提出了一种对PQ扰动识别的新方法。在MST中使用Kaiser窗口以更好的时频矩阵能量浓度。此后，不仅是时频矩阵，而且使用傅里叶变换频谱来自动提取特征，作为PSSAE中的两个子模型的输入。此外，作为示例实现了特征的维度降低和视觉分析。然后使用SoftMax分类器识别PQ干扰的识别。通过用不同类型的单一和组合信号进行一系列实验来验证所提出的算法的有效性和鲁棒性。

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