An Intelligent MPPT controller based on direct neural control for partially shaded PV system

• Intelligent MPPT controller improves the performance in transient operations.
• On-line learning gradient descent algorithm on direct neural control is utilized.
• Off-line Big Bang–Big Crunch optimization method is employed.
• The intelligent controller is tested under partial shading conditions.
• The MPPT controller can be easily implemented.

The development of an effective maximum power point tracking (MPPT) algorithm is important in order to achieve maximum power operation in a photovoltaic system (PV). In this study, a direct neural control (DNC) scheme is developed. The intelligent MPPT controller consists of a hybrid learning mechanism; an on-line learning rule based on gradient decent method and an off-line learning rule based on Big Bang–Big Crunch (BB–BC) algorithm. The effectiveness of the proposed system is tested under partial shading conditions by applying the cascaded converter topology. The feasibility of the DNC is evaluated by the simulation results and compared to the conventional perturbation and observation (P&O) method.

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