A Novel Deep Learning-Based Diagnosis Method Applied to Power Quality Disturbances

Artvin-Darien Gonzalez-Abreu, Miguel Delgado-Prieto, Roque-Alfredo Osornio-Rios, Juan-Jose Saucedo-Dorantes and Rene- de-Jesus Romero-Troncoso
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Artvin-Darien Gonzalez-Abreu: HSPdigital CA-Mecatronica, Engineering Faculty, Autonomous University of Queretaro, San Juan del Rio 76807, Queretaro, Mexico
Miguel Delgado-Prieto: MCIA Research Center, Department of Electronic Engineering, Technical University of Catalonia (UPC), 08034 Barcelona, Spain
Roque-Alfredo Osornio-Rios: HSPdigital CA-Mecatronica, Engineering Faculty, Autonomous University of Queretaro, San Juan del Rio 76807, Queretaro, Mexico
Juan-Jose Saucedo-Dorantes: HSPdigital CA-Mecatronica, Engineering Faculty, Autonomous University of Queretaro, San Juan del Rio 76807, Queretaro, Mexico
Rene- de-Jesus Romero-Troncoso: HSPdigital CA-Mecatronica, Engineering Faculty, Autonomous University of Queretaro, San Juan del Rio 76807, Queretaro, Mexico

Energies, 2021, vol. 14, issue 10, 1-17

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.

Keywords: autoencoder; deep learning; power quality disturbances; power quality monitoring (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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