A Novel Methodology for Classifying Electrical Disturbances Using Deep Neural Networks

• Published in: Technologies (Basel) Vol. 11; no. 4; p. 82
• Main Authors: Guerrero-Sánchez, Alma E., Rivas-Araiza, Edgar A., Garduño-Aparicio, Mariano, Tovar-Arriaga, Saul, Rodriguez-Resendiz, Juvenal, Toledano-Ayala, Manuel
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2023
• Subjects: Algorithms; artificial intelligence; Artificial neural networks; Classification; deep learning; Disturbances; Electric power; Electrical engineering; Electricity distribution; Empirical analysis; Energy; Fourier transforms; Harmonic distortion; Identification and classification; Mathematical functions; Methodology; Methods; Multitasking; multitasking learning; Neural networks; Noise levels; Performance tests; Power failure; smart grids; solar photovoltaic; System reliability; Taxonomy; Wavelet transforms; Mexico
• DOI: 10.3390/technologies11040082
• ISSN: 2227-7080

Electrical power quality is one of the main elements in power generation systems. At the same time, it is one of the most significant challenges regarding stability and reliability. Due to different switching devices in this type of architecture, different kinds of power generators as well as non-linear loads are used for different industrial processes. A result of this is the need to classify and analyze Power Quality Disturbance (PQD) to prevent and analyze the degradation of the system reliability affected by the non-linear and non-stationary oscillatory nature. This paper presents a novel Multitasking Deep Neural Network (MDL) for the classification and analysis of multiple electrical disturbances. The characteristics are extracted using a specialized and adaptive methodology for non-stationary signals, namely, Empirical Mode Decomposition (EMD). The methodology’s design, development, and various performance tests are carried out with 28 different difficulties levels, such as severity, disturbance duration time, and noise in the 20 dB to 60 dB signal range. MDL was developed with a diverse data set in difficulty and noise, with a quantity of 4500 records of different samples of multiple electrical disturbances. The analysis and classification methodology has an average accuracy percentage of 95% with multiple disturbances. In addition, it has an average accuracy percentage of 90% in analyzing important signal aspects for studying electrical power quality such as the crest factor, per unit voltage analysis, Short-term Flicker Perceptibility (Pst), and Total Harmonic Distortion (THD), among others.