Sparse LMS algorithm for two‐level DSTATCOM

Abstract Sparse least mean square algorithm is proposed for the DSTATCOM as an optimal current harmonic extractor to cope with the intermittent nature of loadings. Sparse least mean square is the improved version of adaptive least mean square learning mechanism with regards to sparsity. This innovat...

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Main Authors: Mrutyunjaya Mangaraj, Anup Kumar Panda
Format: Article
Language:English
Published: Wiley 2021-01-01
Series:IET Generation, Transmission & Distribution
Online Access:https://doi.org/10.1049/gtd2.12014
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spelling doaj-1c4abe413bd54ebea3ca48d9286babb72021-07-14T13:25:42ZengWileyIET Generation, Transmission & Distribution1751-86871751-86952021-01-01151869610.1049/gtd2.12014Sparse LMS algorithm for two‐level DSTATCOMMrutyunjaya Mangaraj0Anup Kumar Panda1Lendi Institute of Engineering and Technology Vizianagaram Andhra Pradesh 535005 IndiaNational Institute of Technology Rourkela Odisha 769008 IndiaAbstract Sparse least mean square algorithm is proposed for the DSTATCOM as an optimal current harmonic extractor to cope with the intermittent nature of loadings. Sparse least mean square is the improved version of adaptive least mean square learning mechanism with regards to sparsity. This innovative approach is utilized for better parameter estimation due to its algorithmic simplicity and parallel computing nature. Hence, sparse least mean square is expected to reduce the computation and storage requirements significantly. This suggested controller consists of six subnets. Three subnets for active and another three for reactive weight component are used to extract the fundamental component of the load current. Several factors like previous weight, normalizing weight and learning rate are involved in the sparse least mean square based weight‐updating rule to have better dynamic performance, reduced computational burden and better estimation speed etc. The detailed control algorithm is formulated using MATLAB/Simulink, and validated using experimental analysis. Among these two algorithms, the sparse least mean square offers better voltage regulation, voltage balancing, source current harmonic reduction and power factor correction under various loading scenarios.https://doi.org/10.1049/gtd2.12014
collection DOAJ
language English
format Article
sources DOAJ
author Mrutyunjaya Mangaraj
Anup Kumar Panda
spellingShingle Mrutyunjaya Mangaraj
Anup Kumar Panda
Sparse LMS algorithm for two‐level DSTATCOM
IET Generation, Transmission & Distribution
author_facet Mrutyunjaya Mangaraj
Anup Kumar Panda
author_sort Mrutyunjaya Mangaraj
title Sparse LMS algorithm for two‐level DSTATCOM
title_short Sparse LMS algorithm for two‐level DSTATCOM
title_full Sparse LMS algorithm for two‐level DSTATCOM
title_fullStr Sparse LMS algorithm for two‐level DSTATCOM
title_full_unstemmed Sparse LMS algorithm for two‐level DSTATCOM
title_sort sparse lms algorithm for two‐level dstatcom
publisher Wiley
series IET Generation, Transmission & Distribution
issn 1751-8687
1751-8695
publishDate 2021-01-01
description Abstract Sparse least mean square algorithm is proposed for the DSTATCOM as an optimal current harmonic extractor to cope with the intermittent nature of loadings. Sparse least mean square is the improved version of adaptive least mean square learning mechanism with regards to sparsity. This innovative approach is utilized for better parameter estimation due to its algorithmic simplicity and parallel computing nature. Hence, sparse least mean square is expected to reduce the computation and storage requirements significantly. This suggested controller consists of six subnets. Three subnets for active and another three for reactive weight component are used to extract the fundamental component of the load current. Several factors like previous weight, normalizing weight and learning rate are involved in the sparse least mean square based weight‐updating rule to have better dynamic performance, reduced computational burden and better estimation speed etc. The detailed control algorithm is formulated using MATLAB/Simulink, and validated using experimental analysis. Among these two algorithms, the sparse least mean square offers better voltage regulation, voltage balancing, source current harmonic reduction and power factor correction under various loading scenarios.
url https://doi.org/10.1049/gtd2.12014
work_keys_str_mv AT mrutyunjayamangaraj sparselmsalgorithmfortwoleveldstatcom
AT anupkumarpanda sparselmsalgorithmfortwoleveldstatcom
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