Theoretical and experimental analysis of genetic algorithms based MPPT for PV systems

This paper presents a theoretical and experimental analysis of Maximum Power Point Tracking (MPPT) method for photovoltaic (PV) systems based on Genetic Algorithms (GAs). The proposed algorithm is based on Genetic Algorithms (GAs) and it can estimate the current (Imp) and voltage (Vmp) at maximum power point by measuring the open circuit voltage (Voc) and the short circuit current (Isc) without knowing the irradiance and the cell temperature. The principle of GAs is searching for the maximum of fitness function and not for the minimum of power derivation; this gives more stability and minimize oscillation of output power around the maximum power point (MPP).We expose the method with a few tests; then a comparison with the famous Perturb and Observe (P&O) and Incremental Conductance (Inc-Cond) is given. We tested stability (power oscillation) with real panels. To compare response time (rapidity) we used a PV emulator (realized by Kadri et al.), so we can inject the same irradiance profile and see output PV power evolution. The response time of P&O and Inc-Cond, and the PV power oscillation varies with the duty cycle increment step; with a small step, we get less power oscillation but this needs an important time response, we can improve system rapidity with a bigger duty increment step but important power oscillation will result. With GAs based MPPT we can get more stability with rapid response time. The results obtained show better stability and less oscillation around the MPP with the new method.