Fundamental active current adaptive linear neural networks for photovoltaic shunt active power filters
This paper presents improvement of a harmonics extraction algorithm, known as the fundamental active current (FAC) adaptive linear element (ADALINE) neural network with the integration of photovoltaic (PV) to shunt active power filters (SAPFs) as active current source. Active PV injection in SAPFs s...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI
2016
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| Online Access: | http://psasir.upm.edu.my/id/eprint/54359/ http://psasir.upm.edu.my/id/eprint/54359/1/Fundamental%20active%20current%20adaptive%20linear%20neural%20networks%20for%20photovoltaic%20shunt%20active%20power%20filters.pdf |
| _version_ | 1848852523614470144 |
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| author | Mohd Zainuri, Muhammad Ammirrul Atiqi Mohd Radzi, Mohd Amran Che Soh, Azura Mariun, Norman Abd Rahim, Nasrudin Hajighorbani, Shahrooz |
| author_facet | Mohd Zainuri, Muhammad Ammirrul Atiqi Mohd Radzi, Mohd Amran Che Soh, Azura Mariun, Norman Abd Rahim, Nasrudin Hajighorbani, Shahrooz |
| author_sort | Mohd Zainuri, Muhammad Ammirrul Atiqi |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | This paper presents improvement of a harmonics extraction algorithm, known as the fundamental active current (FAC) adaptive linear element (ADALINE) neural network with the integration of photovoltaic (PV) to shunt active power filters (SAPFs) as active current source. Active PV injection in SAPFs should reduce dependency on grid supply current to supply the system. In addition, with a better and faster harmonics extraction algorithm, the SAPF should perform well, especially under dynamic PV and load conditions. The role of the actual injection current from SAPF after connecting PVs will be evaluated, and the better effect of using FAC ADALINE will be confirmed. The proposed SAPF was simulated and evaluated in MATLAB/Simulink first. Then, an experimental laboratory prototype was also developed to be tested with a PV simulator (CHROMA 62100H-600S), and the algorithm was implemented using a TMS320F28335 Digital Signal Processor (DSP). From simulation and experimental results, significant improvements in terms of total harmonic distortion (THD), time response and reduction of source power from grid have successfully been verified and achieved. |
| first_indexed | 2025-11-15T10:39:26Z |
| format | Article |
| id | upm-54359 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T10:39:26Z |
| publishDate | 2016 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-543592019-06-27T03:41:20Z http://psasir.upm.edu.my/id/eprint/54359/ Fundamental active current adaptive linear neural networks for photovoltaic shunt active power filters Mohd Zainuri, Muhammad Ammirrul Atiqi Mohd Radzi, Mohd Amran Che Soh, Azura Mariun, Norman Abd Rahim, Nasrudin Hajighorbani, Shahrooz This paper presents improvement of a harmonics extraction algorithm, known as the fundamental active current (FAC) adaptive linear element (ADALINE) neural network with the integration of photovoltaic (PV) to shunt active power filters (SAPFs) as active current source. Active PV injection in SAPFs should reduce dependency on grid supply current to supply the system. In addition, with a better and faster harmonics extraction algorithm, the SAPF should perform well, especially under dynamic PV and load conditions. The role of the actual injection current from SAPF after connecting PVs will be evaluated, and the better effect of using FAC ADALINE will be confirmed. The proposed SAPF was simulated and evaluated in MATLAB/Simulink first. Then, an experimental laboratory prototype was also developed to be tested with a PV simulator (CHROMA 62100H-600S), and the algorithm was implemented using a TMS320F28335 Digital Signal Processor (DSP). From simulation and experimental results, significant improvements in terms of total harmonic distortion (THD), time response and reduction of source power from grid have successfully been verified and achieved. MDPI 2016 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/54359/1/Fundamental%20active%20current%20adaptive%20linear%20neural%20networks%20for%20photovoltaic%20shunt%20active%20power%20filters.pdf Mohd Zainuri, Muhammad Ammirrul Atiqi and Mohd Radzi, Mohd Amran and Che Soh, Azura and Mariun, Norman and Abd Rahim, Nasrudin and Hajighorbani, Shahrooz (2016) Fundamental active current adaptive linear neural networks for photovoltaic shunt active power filters. Energies, 9 (6). art. no. 397. pp. 1-20. ISSN 1996-1073 http://www.mdpi.com/1996-1073/9/6/397 10.3390/en9060397 |
| spellingShingle | Mohd Zainuri, Muhammad Ammirrul Atiqi Mohd Radzi, Mohd Amran Che Soh, Azura Mariun, Norman Abd Rahim, Nasrudin Hajighorbani, Shahrooz Fundamental active current adaptive linear neural networks for photovoltaic shunt active power filters |
| title | Fundamental active current adaptive linear neural networks for photovoltaic shunt active power filters |
| title_full | Fundamental active current adaptive linear neural networks for photovoltaic shunt active power filters |
| title_fullStr | Fundamental active current adaptive linear neural networks for photovoltaic shunt active power filters |
| title_full_unstemmed | Fundamental active current adaptive linear neural networks for photovoltaic shunt active power filters |
| title_short | Fundamental active current adaptive linear neural networks for photovoltaic shunt active power filters |
| title_sort | fundamental active current adaptive linear neural networks for photovoltaic shunt active power filters |
| url | http://psasir.upm.edu.my/id/eprint/54359/ http://psasir.upm.edu.my/id/eprint/54359/ http://psasir.upm.edu.my/id/eprint/54359/ http://psasir.upm.edu.my/id/eprint/54359/1/Fundamental%20active%20current%20adaptive%20linear%20neural%20networks%20for%20photovoltaic%20shunt%20active%20power%20filters.pdf |