Maximum Power Point Tracking Techniques for Photovoltaic Systems

Increasing energy needs and decreasing conventional energy sources put more focus on the renewable energy sources in general and solar energy in particular. Generation of electrical energy from solar energy needs the application of photovoltaic (PV) principles. Hence, PV cells have become most important component of solar energy plants. In developing countries, roof top solar systems are gaining prominence as they cater both off-grid and on-grid applications. However, shading is inevitable phenomenon in roof top systems that affects the output and performance significantly [Ref].

The partial shaded condition introduces lot of dynamics into the system in terms of the power and voltage variations delivered from the PV array. This results in occurrence of multiple peaks which cannot be tracked by conventional maximum power point tracking (MPPT) methods. Therefore, development of suitable algorithm for tracking global peak becomes necessary. Optimization techniques like the Flashing Fireflies, Particle Swarm Optimization (PSO) and improved PSO have been proposed as generalized MPPT algorithms with the objective function of power delivered from the solar arrays.

Random Search Method (RSM) is generally used to find the global maximum in any optimization problem. Incorporation of artificial intelligence in the MPPT algorithms is reported to increase the speed of processing. Differential Evolution based optimization of MPPT algorithm is discussed and compared with the conventional techniques. The power peak prediction of the PV arrays under different irradiance condition and temperature for series-parallel, bridge-linked and “total-cross-tied configurations” are predicted and validated with the commercial PV modules. Efforts are also made to compare RSM optimization techniques with PSO based predictions and Perturb and Observe (P & O) methods. The other methods like Energy Recovery (ER), Distributed MPPT, Incremental Conductance (IC) have also been discussed by various researchers. A hybrid optimization method which combines the vector dynamics of “Differential Evolution” (DE) and “Particle Swarm Optimization” (PSO) called the DEPSO is simulated and validated with hardware implementation. The method is said to improve the reliability, independence operation and accuracy of identifying MPP. Cuckoo Search Algorithm based MPPT algorithm is proposed and the performance is compared with the algorithms like P & O and PSO for different conditions like rapid, step and gradual change in temperature and irradiance. Cuckoo Search outperforms both the PSO and the Perturb and Observe methods. Detailed review of various methods can be found in.

All the above techniques are single objective in nature and focus on tracking GMMP only.