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A novel deep learning-based diagnosis method applied to power quality disturbances

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energies-14-02839-v2.pdf (1,427Mb)
 
10.3390/en14102839
 
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hdl:2117/355836

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González Abreu, Artvin Darién
Delgado Prieto, MiquelMés informacióMés informacióMés informació
Osornio Rios, Roque A.
Saucedo Dorantes, Juan Jose
Romero Troncoso, René de Jesús
Document typeArticle
Defense date2021-05-02
Rights accessOpen Access
Attribution 3.0 Spain
This work is protected by the corresponding intellectual and industrial property rights. Except where otherwise noted, its contents are licensed under a Creative Commons license : Attribution 3.0 Spain
Abstract
Monitoring electrical power quality has become a priority in the industrial sector background: avoiding unwanted effects that affect the whole performance at industrial facilities is an aim. The lack of commercial equipment capable of detecting them is a proven fact. Studies and research related to these types of grid behaviors are still a subject for which contributions are required. Although research has been conducted for disturbance detection, most methodologies consider only a few standardized disturbance combinations. This paper proposes an innovative deep learning-based diagnosis method to be applied on power quality disturbances, and it is based on three stages. Firstly, a domain fusion approach is considered in a feature extraction stage to characterize the electrical power grid. Secondly, an adaptive pattern characterization is carried out by considering a stacked autoencoder. Finally, a neural network structure is applied to identify disturbances. The proposed approach relies on the training and validation of the diagnosis system with synthetic data: single, double and triple disturbances combinations and different noise levels, also validated with available experimental measurements provided by IEEE 1159.2 Working Group. The proposed method achieves nearly a 100% hit rate allowing a far more practical application due to its capability of pattern characterization.
CitationGonzález, A.D. [et al.]. A novel deep learning-based diagnosis method applied to power quality disturbances. "Energies", 2 Maig 2021, vol. 14, núm. 10, p. 2839:1-2839:17. 
URIhttp://hdl.handle.net/2117/355836
DOI10.3390/en14102839
ISSN1996-1073
Publisher versionhttps://www.mdpi.com/1996-1073/14/10/2839
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  • Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial - Articles de revista [1.540]
  • MCIA - Motion Control and Industrial Applications Research Group - Articles de revista [260]
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