Fundamental active current adaptive linear neural networks for photovoltaic shunt active power filters

Zainuri, M.A.A.M. and Radzi, M.A.M. and Soh, A.C. and Mariun, N. and Rahim, N.A. and Hajighorbani, S. (2016) Fundamental active current adaptive linear neural networks for photovoltaic shunt active power filters. Energies, 9 (6). p. 397. ISSN 1996-1073

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Official URL: https://doi.org/10.3390/en9060397

Abstract

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.

Item Type: Article
Uncontrolled Keywords: Shunt active power filter (SAPF); Photovoltaic (PV); Current harmonic; Artificial neural network (ANN); Total harmonic distortion (THD); Digital signal processor (DSP); Simulink/MATLAB
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 16 May 2018 06:26
Last Modified: 16 May 2018 06:26
URI: http://eprints.um.edu.my/id/eprint/18698

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