Intelligent Photovoltaic Maximum Power Point Tracking Controller for Energy Enhancement in Renewable Energy System. (20th March 2013)
- Record Type:
- Journal Article
- Title:
- Intelligent Photovoltaic Maximum Power Point Tracking Controller for Energy Enhancement in Renewable Energy System. (20th March 2013)
- Main Title:
- Intelligent Photovoltaic Maximum Power Point Tracking Controller for Energy Enhancement in Renewable Energy System
- Authors:
- Subiyanto,
Mohamed, Azah
Hannan, M. A. - Other Names:
- Ozgener Onder Academic Editor.
- Abstract:
- Abstract : Photovoltaic (PV) system is one of the promising renewable energy technologies. Although the energy conversion efficiency of the system is still low, but it has the advantage that the operating cost is free, very low maintenance and pollution-free. Maximum power point tracking (MPPT) is a significant part of PV systems. This paper presents a novel intelligent MPPT controller for PV systems. For the MPPT algorithm, an optimized fuzzy logic controller (FLC) using the Hopfield neural network is proposed. It utilizes an automatically tuned FLC membership function instead of the trial-and-error approach. The MPPT algorithm is implemented in a new variant of coupled inductor soft switching boost converter with high voltage gain to increase the converter output from the PV panel. The applied switching technique, which includes passive and active regenerative snubber circuits, reduces the insulated gate bipolar transistor switching losses. The proposed MPPT algorithm is implemented using the dSPACE DS1104 platform software on a DS1104 board controller. The prototype MPPT controller is tested using an agilent solar array simulator together with a 3 kW real PV panel. Experimental test results show that the proposed boost converter produces higher output voltages and gives better efficiency (90%) than the conventional boost converter with an RCD snubber, which gives 81% efficiency. The prototype MPPT controller is also found to be capable of tracking power from the 3 kW PVAbstract : Photovoltaic (PV) system is one of the promising renewable energy technologies. Although the energy conversion efficiency of the system is still low, but it has the advantage that the operating cost is free, very low maintenance and pollution-free. Maximum power point tracking (MPPT) is a significant part of PV systems. This paper presents a novel intelligent MPPT controller for PV systems. For the MPPT algorithm, an optimized fuzzy logic controller (FLC) using the Hopfield neural network is proposed. It utilizes an automatically tuned FLC membership function instead of the trial-and-error approach. The MPPT algorithm is implemented in a new variant of coupled inductor soft switching boost converter with high voltage gain to increase the converter output from the PV panel. The applied switching technique, which includes passive and active regenerative snubber circuits, reduces the insulated gate bipolar transistor switching losses. The proposed MPPT algorithm is implemented using the dSPACE DS1104 platform software on a DS1104 board controller. The prototype MPPT controller is tested using an agilent solar array simulator together with a 3 kW real PV panel. Experimental test results show that the proposed boost converter produces higher output voltages and gives better efficiency (90%) than the conventional boost converter with an RCD snubber, which gives 81% efficiency. The prototype MPPT controller is also found to be capable of tracking power from the 3 kW PV array about 2.4 times more than that without using the MPPT controller. … (more)
- Is Part Of:
- Journal of renewable energy. Volume 2013(2013)
- Journal:
- Journal of renewable energy
- Issue:
- Volume 2013(2013)
- Issue Display:
- Volume 2013, Issue 2013 (2013)
- Year:
- 2013
- Volume:
- 2013
- Issue:
- 2013
- Issue Sort Value:
- 2013-2013-2013-0000
- Page Start:
- Page End:
- Publication Date:
- 2013-03-20
- Subjects:
- Renewable energy sources -- Periodicals
Renewable energy sources
Electronic journals
Periodicals
Electronic journals
621.042 - Journal URLs:
- https://www.hindawi.com/journals/jre/ ↗
- DOI:
- 10.1155/2013/901962 ↗
- Languages:
- English
- ISSNs:
- 2314-4386
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD Digital store
- Ingest File:
- 17167.xml