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

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...

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Published inEnergies (Basel) Vol. 14; no. 10; p. 2839
Main Authors Gonzalez-Abreu, Artvin-Darien, Delgado-Prieto, Miguel, Osornio-Rios, Roque-Alfredo, Saucedo-Dorantes, Juan-Jose, Romero-Troncoso, Rene-de-Jesus
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.05.2021
Subjects
Algorithms
Artificial intelligence
autoencoder
Classification
Deep learning
Energy
Neural networks
power quality disturbances
power quality monitoring
Power supply
Principal components analysis
Signal processing
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Summary: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.
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content type line 14
ISSN:1996-1073
1996-1073
DOI:10.3390/en14102839