1. Nature-Inspired Algorithms for Maximum Power Point Tracking in Photovoltaic Systems Under Partially Shaded Conditions
- Author
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C. K. Aravind and V. Vignesh Kumar
- Subjects
Nonlinear system ,Computer science ,Control theory ,business.industry ,Photovoltaic system ,Genetic algorithm ,Particle swarm optimization ,Firefly algorithm ,Modular design ,business ,Maximum power point tracking ,Power (physics) - Abstract
Solar photovoltaic (PV) power is a promising alternative to fossil fuel-based power because of its attributes such as omnipresence, abundant availability, absence of rotating parts, modular and low maintenance cost. Due to the low conversion efficiency and higher initial investments on PV systems, maximum power point tracking (MPPT) is an essential requirement in grid-connected PV systems. MPPT in PV systems is a challenging task due to the nonlinear current–voltage (I–V) and power–voltage (P–V) characteristics. MPPT under partially shaded conditions (PSC) is further complicated due to the existence of multiple power peaks in the P–V curve. One of the prominent but cost-effective and feasible method of MPPT is the application of particle swarm optimization (PSO), and this scheme has been extensively reported in the literature. Recently, few more nature-inspired optimization techniques such as genetic algorithm (GA), firefly algorithm (FA) and artificial bee colony (ABC) are also implemented for MPPT. This chapter develops and analyses the performance of few popular nature-inspired optimization techniques towards MPPT. The procedure for implementation of each method is lucidly explained and computed MPPT curves along with qualitative and numeric data are presented. Experimental implementation is also included for completeness.
- Published
- 2020
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