Power Quality Analysis Based on Machine Learning Methods for Low-Voltage Electrical Distribution Lines

Carlos Alberto Iturrino Garcia, Marco Bindi, Fabio Corti (), Antonio Luchetta, Francesco Grasso, Libero Paolucci, Maria Cristina Piccirilli and Igor Aizenberg
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Carlos Alberto Iturrino Garcia: Department of Information Engineering, University of Florence, 50139 Firenze, Italy
Marco Bindi: Department of Information Engineering, University of Florence, 50139 Firenze, Italy
Fabio Corti: Department of Information Engineering, University of Florence, 50139 Firenze, Italy
Antonio Luchetta: Department of Information Engineering, University of Florence, 50139 Firenze, Italy
Francesco Grasso: Department of Information Engineering, University of Florence, 50139 Firenze, Italy
Libero Paolucci: Department of Information Engineering, University of Florence, 50139 Firenze, Italy
Maria Cristina Piccirilli: Department of Information Engineering, University of Florence, 50139 Firenze, Italy
Igor Aizenberg: Department of Computer Science, Manhattan College, Riverdale, New York, NY 10471, USA

Energies, 2023, vol. 16, issue 9, 1-28

Abstract: The main objective of this paper is to propose two innovative monitoring methods for electrical disturbances in low-voltage networks. The two approaches present a focus on the classification of voltage signals in the frequency domain using machine learning techniques. The first technique proposed here uses the Fourier transform (FT) of the voltage waveform and classifies the corresponding complex coefficients through a multilayered neural network with multivalued neurons (MLMVN). In this case, the classifier structure has three layers and a small number of neurons in the hidden layer. This allows complex-valued inputs to be processed without the need for pre-coding, thus reducing computational cost and keeping training time short. The second technique involves the use of the short-time Fourier transform (STFT) and a convolutional neural network (CNN) with 2D convolutions in each layer for feature extraction and dimensionality reduction. The voltage waveform perturbations taken into consideration are: voltage sag, voltage swell, harmonic pollution, voltage notch, and interruption. The comparison between the two proposed techniques is developed in two phases: initially, the simulated data used during the training phase are considered and, subsequently, various experimental measurements are processed, obtained both through an artificial disturbance generator and through a variable load. The two techniques represent an innovative approach to this problem and guarantee excellent classification results.

Keywords: convolutional neural networks; electrical disturbances; short-time Fourier transform; multilayer neural networks with multivalued neurons; power quality (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: 2023
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