In this paper, an efficient adaptive neuro-fuzzy inference system (ANFIS)-based PI controller for maximum power point tracking (MPPT) of photovoltaic (PV) systems is proposed. The proposed ANFIS-based MPPT controller has the capacity to track the optimum point under the rapidly changing irradiation conditions with less fluctuations in steady state. The training data of the proposed controller are extracted from a precise PV model developed. The performance of the proposed controller is compared with the conventional incremental conductance method. Finally, the proposed ANFIS-based MPPT controller has been implemented experimentally using real-time digital simulator (RTDS) to simulate a PV system in real time, while the proposed ANFIS-based controller is implemented on dSPACE 1104 controller. Simulation and experimental results show that the proposed ANFIS-based MPPT controller has fast and accurate dynamic response with less fluctuations in steady state. In addition, its performance is superior as compared to the conventional methods.
Bibliographical notePublisher Copyright:
© 2015, King Fahd University of Petroleum & Minerals.
- Adaptive network-based fuzzy inference system (ANFIS)
- Maximum power point tracking (MPPT)
- Photovoltaic (PV)
- Real-time digital simulator (RTDS)
- dSPACE controller
ASJC Scopus subject areas