Maximum power point tracking in a solarPVsystem: Current trends towards nature‐inspired optimization techniques

Abstract

This paper discusses and analyses the work done in developing a Natureinspired optimization algorithm in a solar PV system for tracking the global maximum power point (GMPP) in partial shaded condition (PSC). Partial shading is a situation where PV panels connected in series do not receive the same solar radiation, thus exhibiting different I-V characteristics for the individual panel. Under PSC, solar PV system output exhibits multiple local maximum power point (MPP) and a unique global MPP on the Power-Voltage curve. Conventional maximum power point tracking (MPPT) techniques such as Perturb and Observe (P&O), incremental conductance (IC), and Hill Climbing (HC) method can only track MPP under uniform insolation conditions. It may fail to track GMPP in case of PSC and get trapped in local MPP, thereby causing loss of power that could have been tapped if the operation would have been at GMPP. To overcome the problem posed by PSC, researchers are applying the nature-inspired optimization algorithm for tracking GMPP under partial shading conditions with fairly good results. Nature-inspired optimization algorithm offers the advantage of solving multivariable nonlinear objective functions with constraints by exploiting the search space and can converge quickly to GMPP. Some of the most recent and famous nature-inspired algorithms have been discussed and compared here. Simulation and hardware results of some of them are also taken in order to compare them effectively. Simulation results show that the Most Valuable Player Algorithm (MVPA) takes the least time to track the MPP in all the cases, and the MPP tracked is also comparable List of Symbols and Abbreviations: X k , Position corresponding to the kth iteration; U k , Voltage corresponding to the kth iteration; P pv , Output power of the solar PV; k, Iteration number; d, Duty cycle; v k , Velocity of the particle corresponding to the kth iteration; ω, Inertia weight; c 1 , cognitive coefficient; c 2 , Social coefficient; r 1 and r 2 , Uniformly distributed random variables between [0,1]; p best,k , Best position of the kth particle; g best , Best position amongst all particles; X p , Prey position; A, C, and D, Coefficient vectors; HIL, Hardware-in-the-loop.

Enhancing Photovoltaic Conversion Efficiency With Model Predictive Control-Based Sensor-Reduced Maximum Power Point Tracking in Modified SEPIC Converters

“…This results in less energy being lost as heat, improving overall system efficiency; 3. Greater Flexibility: The use of a dc/dc converter with MPPT control allows for greater flexibility in system design, as it enables PV panels to be connected in series or parallel to achieve the desired voltage and current levels [10]. The maximum power point (MPP) of a PV panel is the point at which it generates the most power for a given set of conditions, such as temperature and irradiance.…”