###### Abstract  
With the increasing integration of non-linear electronic loads, the diagnosis and classification of power quality are becoming crucial for power grid signal management. This paper presents a novel diagnosis strategy based on unsupervised learning, namely residual denoising convolutional auto-encoder (RDCA), which extracts features from the complex power quality disturbances (PQDs) automatically. Firstly, the time-frequency analysis is applied to isolate frequency domain information. Then, the RDCA with a weight residual structure is utilized to extract the useful features in the contaminated PQD data, where the performance is improved using the residual structure. A single-layer convolutional neural network (SCNN) with an added batch normalization layer is proposed to classify the features. Furthermore, combining with RDCA and SCNN, we further propose a classification framework to classify complex PQDs. To provide a reasonable interpretation of the RDCA, visual analysis is employed to gain insight into the model, leading to a better understanding of the features from different layers. The simulation and experimental tests are conducted to verify the practicability and robustness of the RDCA. automatic extracted features; power quality disturbances; residual denoising convolutional auto-encoder; single-layer convolutional neural network +
Footnote †: journal: Medical Imaging  
10.3390/app11167637  
12 July 2021  
17 August 2021  
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**Copyright:** **© 2021 by the authors. Licensee MDP, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))). **  
**This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))). **  
**Copyright:** **© 2021 by the authors. Licensee MDP, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))). **  
**Copyright:** ** © 2021 by the authors. Licensee MDP, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons. In the feature extraction stage, different statistical features are then extracted and fed to the classifier, such as peak and harmonic amplitude [6]. Different kinds of literature have adopted different quantities of statistical features. For example, the identification and classification method under multiple disturbances was presented in [7], by combining the adaptive linear networks and feedforward neural networks together with two statistical features. It was reported in [8] that multiple PQDs were analyzed along with six manually selected features and nearly 94.19% accuracy was achieved. Furthermore, 62 features were designed for classification in [9], and the model has better performance when only partial features were used. However, the design of these manually selected features lacks reference and may lead to information loss owing to human factors. Hereby, automatic feature extraction is becoming increasingly popular.