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International Conference on Mechanical Engineering and Technology (ICMET-London 2011)

Garry Lee
Garry Lee
Information Engineering Research Institute
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Due to the nonlinearity of the voltage-current characteristic in photovoltaic (PV) systems, maximum power point tracking (MPPT) techniques are employed to maximize the PV cells output power which depends on solar irradiance and temperature. The fixed-step perturbation and observation (P&O) MPPT algorithm is widely used as its easy implementation, but it is difficult to handle both dynamic response and steady-state precision. In this paper, a novel variable step size P&O algorithm based on adaptive neuro-fuzzy inference is proposed, the step size is automatically tuned according to the operating point to track the maximum power point. The theoretical analysis and the design principle of the proposed algorithm are provided and its feasibility is also verified by simulation and experiment results. Compared with the conventional fixed step P&O algorithm, the proposed approach can greatly improve the MPPT response speed and accuracy at steady state simultaneously.

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