A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network

Shouxiang Wang and Haiwen Chen

Applied Energy, 2019, vol. 235, issue C, 1126-1140

Abstract: With the integration of multiple energy systems, there are more and more deterioration risks of power quality in different energy production, transformation, delivery and consumption stages. Automatic classification of power quality disturbances is the foundation to deal with power quality problem. From the traditional point of view, the identification process of power quality disturbances should be divided into three independent stages: signal analysis, feature selection and classification. However, there are some inherent defects in signal analysis and the procedure of manual feature selection is tedious and imprecise, leading to a low classification accuracy of multiple disturbances and a poor noise immunity. This paper proposes a novel full closed-loop approach to detect and classify power quality disturbances based on a deep convolutional neural network. Considering the characteristics of power quality disturbances problem, a unit construction which consists of 1-D convolutional, pooling, and batch-normalization layers is designed to capture multi-scale features and reduce overfitting. In the proposed deep convolutional neural network, multiple units are stacked to extract features from massive disturbance samples automatically. Comparisons with other state-of-the-art deep neural networks and traditional methods proves that the proposed method can overcome defects of traditional signal process and artificial feature selection. Considering microgrid is an important development form of multi-energy system and an essential part of smart grid, a typical simulation system is constructed to analyze the causes of power quality problems in microgrid and the field data from a multi-microgrid system are used to further prove the validity of the proposed method.

Keywords: Power quality disturbances; Distributed energy; Feature extraction; Deep neural networks; Deep convolutional neural networks (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (30)

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DOI: 10.1016/j.apenergy.2018.09.160

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