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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | IET Generation, Transmission & Distribution |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/gtd2.12014 |
| _version_ | 1818065179843231744 |
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| author | Mrutyunjaya Mangaraj Anup Kumar Panda |
| author_facet | Mrutyunjaya Mangaraj Anup Kumar Panda |
| author_sort | Mrutyunjaya Mangaraj |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-10T14:47:47Z |
| format | Article |
| id | doaj.art-1c4abe413bd54ebea3ca48d9286babb7 |
| institution | Directory Open Access Journal |
| issn | 1751-8687 1751-8695 |
| language | English |
| last_indexed | 2024-12-10T14:47:47Z |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Generation, Transmission & Distribution |
| spelling | doaj.art-1c4abe413bd54ebea3ca48d9286babb72022-12-22T01:44:31ZengWileyIET 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/gtd2.12014Optimisation techniquesVoltage controlControl of electric power systemsInterpolation and function approximation (numerical analysis)Other topics in statisticsOptimisation techniques |
| spellingShingle | Mrutyunjaya Mangaraj Anup Kumar Panda Sparse LMS algorithm for two‐level DSTATCOM IET Generation, Transmission & Distribution Optimisation techniques Voltage control Control of electric power systems Interpolation and function approximation (numerical analysis) Other topics in statistics Optimisation techniques |
| title | 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_short | Sparse LMS algorithm for two‐level DSTATCOM |
| title_sort | sparse lms algorithm for two level dstatcom |
| topic | Optimisation techniques Voltage control Control of electric power systems Interpolation and function approximation (numerical analysis) Other topics in statistics Optimisation techniques |
| url | https://doi.org/10.1049/gtd2.12014 |
| work_keys_str_mv | AT mrutyunjayamangaraj sparselmsalgorithmfortwoleveldstatcom AT anupkumarpanda sparselmsalgorithmfortwoleveldstatcom |