The increase in energy needs and the decrease in conventional energy sources have placed greater attention on renewable energy sources in general and solar energy in particular. Generating electricity from solar energy requires the application of photovoltaic (PV) principles. Therefore, photovoltaic cells have become the most important component of solar power systems. In developing countries, rooftop solar systems are gaining importance as they cater to both off-grid and on-grid applications. However, shading is an unavoidable phenomenon in rooftop systems that significantly affects output and performance [Ref]. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essay The partially shaded condition introduces a lot of dynamics into the system in terms of changes in power and voltage provided by the PV array. This results in the occurrence of multiple peaks that cannot be tracked with conventional maximum power point tracking (MPPT) methods. Therefore, the development of a suitable algorithm to track the global peak becomes necessary. Optimization techniques such as Flashing Fireflies, Particle Swarm Optimization (PSO), and improved PSO have been proposed as generalized MPPT algorithms with the objective function of solar panel power output. The random search method (RSM) is generally used to find the global maximum in any optimization problem. Incorporating artificial intelligence into MPPT algorithms is said to increase processing speed. Differential evolution based optimization of the MPPT algorithm is discussed and compared with conventional techniques. Peak power prediction of PV arrays under different irradiance and temperature conditions for series-parallel, bridged, and “total cross configurations” configurations is predicted and validated with 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 such as energy recovery (ER), distributed MPPT and incremental conductance (IC) have also been discussed by various researchers. A hybrid optimization method combining the vector dynamics of “Differential Evolution” (DE) and “Particle Swarm Optimization” (PSO) called DEPSO is simulated and validated with the hardware implementation. The method is said to improve the reliability, operational independence and accuracy of MPP identification. The MPPT algorithm based on Cuckoo Search algorithm is proposed and the performance is compared with algorithms such as P&O and PSO for different conditions such as rapid, smooth and gradual change of temperature and irradiance. Cuckoo Search outperforms both the PSO method and the Perturb and Observe methods. You can find a detailed review of the various methods in. Please note: this is just an example. Get a custom paper from our expert writers now. Get a Custom Essay All the above techniques are unique in nature and focus only on GMMP monitoring.