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Improved solar cell models and control methods using synergies of soft-computing techniques are used to demonstrate increased energy efficiencies of photovoltaic (PV) power plants connected to the electricity grid via space-vector-modulated three-phase inverters. The models and control strategies are combined to form two new model-based controllers that are more accurate and resilient than existing solutions resulting in increased power production. A radial-basis-function-network (RBFN) model with a neuro-fuzzy regulator applied to a plant well characterized by the conventional solar cell model provided an estimated 1.5% increase in power production over an existing conventional model proportional integral (PI)-regulator combination. A neuro-fuzzy model with a neuro-fuzzy controller applied to a plant poorly characterized by the conventional solar cell model gave an 8.6% increase in power. An analysis of the net contributions to the increased efficiencies shows that the improved models had the most effect on power gains.
Varnham et al. (Tue,) studied this question.