A Particle Swarm Optimization-Based Maximum Power Point Tracking Scheme Employing Dynamic Inertia Weight Strategies

Abstract

AbstractPhotovoltaic (PV) systems are one of the most popular forms of Renewable Energy Sources. It is extremely important that these systems be operated at the Maximum Power Point (MPP). Under uniform insolation conditions, we observe a single peak in the P–V characteristics of a PV array. In contrast, under Partial Shading Condition (PSC) the P–V curve is highly non-linear and has multiple peaks that can be classified as Local and Global Peaks. Conventional MPPT Algorithms have failed to deliver satisfactory results under PSC. Hence, nature inspired optimization techniques such as the Particle Swarm Optimization (PSO) algorithm have been applied to MPPT under PSC and have proven to be an effective solution to the problem. In this paper, we employ a set of simple and dynamic Inertia weight strategies which are independent of factors such as maximum number of iterations and can be exploited to increase the speed of tracking of the PSO-based MPPT approach. An inertia weight that is dynamic, simple, and intuitive has also been proposed. The proposed Inertia Weight (IW) is independent of the current iteration number as well as a predetermined value for the maximum number of iterations necessary to converge to the MPP. A significantly lower convergence time and lesser tracking losses are obtained using the proposed IW. The performance of all these techniques has been evaluated using simulations under different shading conditions and validated with hardware implementation.KeywordsMPPTPSCPSOInertia weightPV systemsDynamicSimulated annealing inertia weightGlobal–local best inertia weight