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Home List of Titles A FLANN-based controller for maximum power point tracking in PV systems under rapidly changing conditions
Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/237047
- A FLANN-based controller for maximum power point tracking in PV systems under rapidly changing conditions
- Jiang, Lian Lian; Maskell, Douglas L.; Patra, Jagdish C.
- In order to increase the efficiency of the Photovoltaic (PV) system, the PV system should be operated at the Maximum Power Point (MPP). The MPP Tracking (MPPT) is an essential part in achieving this improvement. Some of the existing techniques such as Perturb-and-Observe (P&O) and Incremental Conductance (INC) are relatively simpler to implement, but under rapidly changing irradiance and temperature conditions, they fail to track the MPP. Although methods such as Multilayer Perceptron (MLP) and Fuzzy Logic (FL) are efficient in tracking the MPP, their implementation increases the system complexity. In this paper, we propose a novel artificial intelligence based controller for MPPT, which can efficiently track the MPP, while keeping the computational complexity within the limits. Our technique uses Functional Link Artificial Neural Network (FLANN) to predict the PV output voltage at the MPP. Since there is no hidden layer, FLANN is computationally inexpensive. Simulation results verify that the proposed FLANN controller is computationally less intensive and exhibits higher efficiency under rapidly changing weather conditions.
- Publication type
- Conference paper
- Research centre
- Swinburne University of Technology. Faculty of Engineering and Industrial Sciences
- Proceedings of the 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2012), Kyoto, Japan, 25-30 March 2012, pp. 2141-2144
- Publication year
- Computational complexity; FLANN; Functional Link Artificial Neural Network; Maximum Power Point; MPP Tracking; MPPT; Photovoltaic system; PV system; Weather conditions
- 1520-6149 (series ISSN)
- 9781467300469, 1467300462
- Publisher URL
- Copyright © 2012 IEEE.
- Peer reviewed