Self-Tuning Parameters of the Proposed Fuzzy Immune Integral Controller to Improve the Performance of the Standard P&O-MPPT Strategy

This paper proposes the automatic parameter tuning of the proposed fuzzy immune controller. It is cascaded with the control employing the perturb and observe (P&O) algorithm, resulting in a new maximum power point tracking (MPPT) strategy, called fuzzy immune P&O-MPPT. This strategy overcomes the ripple problems that occur in the PV output power response when the renewable energy conversion system (RECS) is controlled using the standard P&O-MPPT strategies. The design of the proposed controller consists of separate steps. Firstly, a nonlinear model perfectly describing the internal functioning of the solar PV generator is designed where its nonlinear current-voltage characteristic is linearized around standard test conditions (STC). The resulting RECS model is then used in a synthesis method that combines an immune control mechanism with fuzzy logic, where the parameters of the resulting controller are optimized using the Genetic Algorithm GA. It enables to track the optimal reference PV voltage, previously established using the modified P&O algorithm. Indeed, the past value of the duty cycle is accurately updated to provide a ripple-free PV power output response. Since this response is compared with those provided by other standard P&O-MPPT strategies in STC, the findings, based on experimental measurements, confirm not only the validity of the global model RECS, but also the effectiveness of the proposed controller. The superiority of the proposed control strategy over the standard ones can be seen in the trade-off between steady-state MPP tracking and the speed of convergence of the MPP in transient conditions, regardless of sudden variations in climatic conditions.