Indian Journal of Science and Technology

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Artificial Neural Network based Harmonics Estimator for a Power Electronics Converter


Affiliations
1 Department of EEE, NIT Puducherry, Karaikal - 609609, Puducherry, India
2 Department of EEE, MVIT, Kalitheerthalkuppam - 605107, Puducherry, India
3 Department of EEE, SRM University, Chennai - 603203, Tamil Nadu, India
 

Objectives: This paper presents harmonics estimation using Artificial Neural Network (ANN) for a 2 pulse Un-controlled power electronics converter. Methods/Analysis: Feed-forward architecture is chosen to model ANN-based Harmonics Estimator. The Feedforward architecture trained with various Learning algorithms is investigated. The suitable ANN model is identified. The performance of ANN based harmonics estimator is compared with conventional Fourier series method. Findings: The feed-forward architecture trained with LM algorithm is identified to be suitable for harmonics estimation in 2-pulse uncontrolled rectifier. Novelty/Improvement: The suitability of feed-forward architecture with different learning algorithms is investigated which is novel in this paper.

Keywords

Artificial Neural Networks, Estimator, Feed-Forward Neural Architectures, Harmonics, Learning Algorithms, Power Electronics Converters, 2-Pulse Controlled Rectifier.
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  • Artificial Neural Network based Harmonics Estimator for a Power Electronics Converter

Abstract Views: 224  |  PDF Views: 0

Authors

A. Venkadesan
Department of EEE, NIT Puducherry, Karaikal - 609609, Puducherry, India
K. Sedhu Raman
Department of EEE, MVIT, Kalitheerthalkuppam - 605107, Puducherry, India
K. Chandrasekaran
Department of EEE, NIT Puducherry, Karaikal - 609609, Puducherry, India
C. S. Boopathi
Department of EEE, SRM University, Chennai - 603203, Tamil Nadu, India

Abstract


Objectives: This paper presents harmonics estimation using Artificial Neural Network (ANN) for a 2 pulse Un-controlled power electronics converter. Methods/Analysis: Feed-forward architecture is chosen to model ANN-based Harmonics Estimator. The Feedforward architecture trained with various Learning algorithms is investigated. The suitable ANN model is identified. The performance of ANN based harmonics estimator is compared with conventional Fourier series method. Findings: The feed-forward architecture trained with LM algorithm is identified to be suitable for harmonics estimation in 2-pulse uncontrolled rectifier. Novelty/Improvement: The suitability of feed-forward architecture with different learning algorithms is investigated which is novel in this paper.

Keywords


Artificial Neural Networks, Estimator, Feed-Forward Neural Architectures, Harmonics, Learning Algorithms, Power Electronics Converters, 2-Pulse Controlled Rectifier.



DOI: https://doi.org/10.17485/ijst%2F2016%2Fv9i42%2F123986