Deep learning and signal processing based algorithm for autorecognition of harmonic loads

Issue title: Digital transformation through advances in artificial intelligence and machine learning

Guest editors: Hasmat Malik, Gopal Chaudhary and Smriti Srivastava

Article type: Research Article

Authors: Srikanth, Pullabhatla^{a; *} | Koley, Chiranjib^b

Affiliations: [a] EED, NIT Durgapur, West Bengal & CCE (R&D) South, DRDO Secunderabad, India | [b] National Institute of Technology, Durgapur, West Bengal, India

Correspondence: [*] Corresponding author. Pullabhatla Srikanth, EED, NIT Durgapur, West Bengal –713 209 & CCE (R&D) South, DRDO Secunderabad –500 009, India. E-mail: [email protected].

Abstract: A convolution neural network (CNN) based deep learning method has been proposed for automatic classification and localization of nonlinear loads present in an interconnected power system. The identification of nonlinear loads has been previously dealt with the use of Nonlinear Auto Regression neural network with eXogenous inputs (NARX), Backpropagation Neural Network (BPNN), Probabilistic Neural Network (PNN), Artificial Neural Networks (ANN) and Fuzzy Logic (FL). However, these techniques had not explored the area of classification of industrial and domestic nonlinear loads in an interconnected power system. Also, a Deep learning-based solution for identification of the type of nonlinear load has not been reported in the literature to date. Hence, to address these shortcomings, an IEEE-9 Bus system with industrial nonlinear loads has been used to obtain various current waveforms with distortions. The recorded current waveforms are transformed into a time-frequency (TF) domain plane, and the obtained images are then fed to the deep learning algorithm. The colored images of the TF plots of each type of nonlinear load in Red-Green-Blue (RGB) index provide the best visual features for extraction. The TF domain signatures of individual events are scaled to a standard size before feeding to the algorithm. Through these TF signatures, unique features were extracted with the deep learning algorithm, and then passed on to different stages of convolution and max-pooling with fully connected layers. The softmax classifier at the end classifies the input data into the type of nonlinear present in the power system. The algorithm, when run at different buses, also identifies the location of the nonlinear load. The proposed methodology avoids the usage of any additional fusion layer for obtaining unique features, reduces the training time and maintains the highest accuracy of 100%.

Keywords: Nonlinear loads, localization, identification, deep learning, time-frequency representation

DOI: 10.3233/JIFS-189780

Journal: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 1171-1184, 2022