Guerrero-Sánchez, A.E.; Rivas-Araiza, E.A.; Garduño-Aparicio, M.; Tovar-Arriaga, S.; Rodriguez-Resendiz, J.; Toledano-Ayala, M. A Novel Methodology for Classifying Electrical Disturbances Using Deep Neural Networks. Technologies2023, 11, 82.
Guerrero-Sánchez, A.E.; Rivas-Araiza, E.A.; Garduño-Aparicio, M.; Tovar-Arriaga, S.; Rodriguez-Resendiz, J.; Toledano-Ayala, M. A Novel Methodology for Classifying Electrical Disturbances Using Deep Neural Networks. Technologies 2023, 11, 82.
Guerrero-Sánchez, A.E.; Rivas-Araiza, E.A.; Garduño-Aparicio, M.; Tovar-Arriaga, S.; Rodriguez-Resendiz, J.; Toledano-Ayala, M. A Novel Methodology for Classifying Electrical Disturbances Using Deep Neural Networks. Technologies2023, 11, 82.
Guerrero-Sánchez, A.E.; Rivas-Araiza, E.A.; Garduño-Aparicio, M.; Tovar-Arriaga, S.; Rodriguez-Resendiz, J.; Toledano-Ayala, M. A Novel Methodology for Classifying Electrical Disturbances Using Deep Neural Networks. Technologies 2023, 11, 82.
Abstract
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, and non-linear loads are used for different industrial processes. As a result of this, 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 Mul-titasking Deep Neural Network (MDL) for the Classification and Analysis of Multiple Electrical Disturbances. The characteristics are extracted with a specialized and adaptive methodology for non-stationary signals, Empirical Mode Decomposition (EMD). The methodology’s design, devel-opment, and various performance tests are carried out with 28 different difficulty levels, such as severity, disturbance duration time, and noise in the 20 dB to 60 dB signal range. MDL was devel-oped 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, an average accuracy percentage of 90% in analyzing important signal aspects for studying electrical power quality such as crest factor, Per Unit voltage analysis, Short Term Flicker Perceptibility (Pst), and Total Harmonic Distortion (THD), among others.
Keywords
Artificial intelligence; Neural Networks; Deep learning; Multitasking learning; Solar photovoltaic; Smart grids; Multiple Electrical Disturbances; Power quality
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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