###### Abstract
In the classification stage, various classifiers for PQDs, including support vector machine (SVM), random forests (RF) decision tree (DT), etc., have been also developed [10]. Combined with time-frequency analysis and feature extraction, these classifiers can identify the manually selected features and have a good classification performance. For instance, a practical arcing fault identification method proposed in [11] combines the GT and ANN, but the time-frequency domain window width is fixed, which restricts the analysis ability for signals with rich frequency domain components. Researchers in [12; 13] utilized WT and SVM to conduct power quality events classification. Additionally, low-pass scaling and high-pass scaling parameters were used to accommodate signal resolution [12], while the performance of WT degraded in noisy conditions. Then, ST with DT in [14] was developed to evaluate and detect the power quality, nevertheless, the high computational demand makes it difficult to meet the real-time requirement. To balance the trade-off between speed and accuracy, a modified ST with RF and double resolution ST (DRST) with directed acyclic graph SVM in [15; 16] were proposed respectively, where the key frequency point selection techniques were used to reduce the computational burden. However, there is still the limitation for the classifiers to deal with ever-changing automatic feature. The conventional models have been effectively applied to the field of PQD diagnosis since one problem further prevented the development of these methods, namely the feature extraction process [17]. So far, all the features of power quality have been manually extracted from original signal or time-frequency information. It is found that these manually selected features are various for different researchers. Not only that, the process of manually selected feature extraction is time-consuming, complex, and may not be able to completely distinguish disturbance signals, especially for composite signals [18]. Recently, the deep neural network, an automatic feature extraction method, has attracted attention for image analysis and speech recognition [19]. For instance, the convolutional neural network (CNN) has been successfully used in feature learning for big data [20; 21]. Unlike most previous models, which mainly focused on classification problems, a novel method is introduced for the purpose of automatic feature extraction. Our contributions are stated as follows:  
1. The time-frequency information of PQDs is initially separated by DRST. To refine the feature extraction capability, a noise reduction, and weight residual structure referred as residual denoising convolutional auto-encoder (RDCA) is proposed to automatically extract features from time-frequency information. 2. In terms of classification, the single-layer CNN (SCNN) classifier is used to fulfill the classification task. The batch normalization (BN) layer and dynamic learning rate are integrated to speed up convergence. 3. We also propose a classification framework based on RDCA and SCNN. Especially, we perform two visual verification methods to investigate how RDCA could benefit from the diagnosis of PQDs. Features extracted from different layers of RDCA are visualized and analyzed to verify the meaning of the extracted features. 4. To validate the framework, the simulation and experimental results on both single and combined PQDs data sets are conducted under different noise conditions. The rest of the paper is structured as follows. In Section 2, the proposed RDCA and SCNN are introduced. The PQD diagnostic framework is presented in Section 3. Section 4 explores the interpretability of extracted features by RDCA. Thereafter, Section 5 elucidates the effectiveness of the proposed method through a large number of experiments. Finally, Section 6 concludes this paper.