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Deep power: Deep learning architectures for power quality disturbances classification

Publication Type : Conference Paper

Publisher : IEEE

Source : In 2017 International Conference on Technological Advancements in Power and Energy (TAP ENERGY) (pp. 1–6). IEEE.

Keywords : classification, Computer architecture, Convolution, convolutional neural network-long short-term memory, Deep learning, deep learning algorithms, deep learning architecture, deep learning mechanisms, deep power, Distributed generation system, feature engineering, Feature extraction, identity-recurrent neural network, learning (artificial intelligence), Logic gates, Machine learning, modern smart grid, optimal deep learning architecture, optimal features, Pattern classification, power engineering computing, Power quality, power quality disturbance classification schemes, power quality disturbances, power quality disturbances classification, power supply quality, power system faults, power system quality, PQ signals, real-time characterization, real-time PQ events, recurrent neural nets, Recurrent neural networks, reliability problems, Smart grid, Smart power grids, synthetic single combined PQ disturbances, time-consuming feature engineering

Campus : Amritapuri, Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking, Electronics Communication and Instrumentation Forum (ECIF)

Department : Electronics and Communication

Verified : Yes

Year : 2017

Abstract : The transformation of the conventional electric power grid to modern smart grid are subjected to power system quality and reliability problems. In order to ensure reliable, secure and quality supply of power, it is important to characterize and classify the power quality disturbances. Power quality (PQ) disturbance classification schemes implicitly relies o n feature engineering to extract unique and accurate features such as statistical information, spatio-temporal characteristics, stationary and non-stationary behavior of PQ signals. This paper explores the potentiality of deep learning algorithms to characterize and classify various PQ disturbances in smart grid. Deep learning algorithms have the inherent capability to automatically learn optimal features from raw input data and thus to avoid time-consuming feature engineering. To understand the effectiveness of various deep learning mechanisms, different architectures namely convolution neural network (CNN), recurrent neural network (RNN), identity-recurrent neural network (I-RNN), long short-term memory (LSTM), gated recurrent units (GRU) and convolutional neural network-long short-term memory (CNN-LSTM) are studied in this paper. Several experiments are conducted to propose an optimal deep learning architecture with specific network parameters and topologies. The performance of the proposed deep learning architecture is evaluated on a set of synthetic single and combined PQ disturbances and real-time PQ events. The proposed architecture is found to be accurate for real-time characterization and classification of power quality disturbances in smart grid.

Cite this Research Publication : Mohan, Neethu, Soman, K., & Vinayakumar, R. (2017). Deep power: Deep learning architectures for power quality disturbances classification. In 2017 International Conference on Technological Advancements in Power and Energy (TAP ENERGY) (pp. 1–6). IEEE.

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