Abstract
The Maximum Power Point Tracking controller (MPPT) is a key element in Photovoltaic systems (PV) since it allows maintaining the PV operating point at its maximum under different temperatures and sunlight irradiations. Metaheuristic algorithms such as the ant colony optimization (ACO) are adopted and have shown their superiority to many other techniques. The perturb and observe (P&O) algorithm is a simple and efficient technique, and is one of the most commonly employed MPPT schemes for PV power generation systems. Which executed by manipulating direct duty ratio of the boost converter. P&O method miserably fails to recognize various MPPT controllers. This paper proposes ACO technique to solve real-life problems.
Highlights
As technology advances, scientists and researchers are finding new alternative efficient ways to generate electricity in our homes, offices, schools, hospitals and vehicles
Maximum Power Point Tracking controller (MPPT) based on soft computing methods (SCM) have attracted huge interest from research communities, due to their ability to solve the problem of the nonlinear I-V or P-V functions
The model of the proposed photovoltaic system is depicted in Fig.8.Where the Photovoltaic systems (PV) arrays, boost converter, and the perturb and observe (P&O) MPPT Controller have been modeled in Simulink
Summary
Scientists and researchers are finding new alternative efficient ways to generate electricity in our homes, offices, schools, hospitals and vehicles. In the class of CM, we mention the well-established and widely used Perturb and Observe, the incremental conductance and the Hill Climbing MPPT controllers These CM are simple, easy to implement and capable of tracking the MPP efficiently in normal conditions. MPPT based on soft computing methods (SCM) have attracted huge interest from research communities, due to their ability to solve the problem of the nonlinear I-V or P-V functions Among these methods, we mention the latest proposed methods as follow: Ant Colony Algorithm (ACO) , Particle Swarm Optimization (PSO) , Artificial Bee Colony (ABC) , Cuckoo Search Algorithm (CSA) , Firefly Algorithm (FA) , Bat Algorithm (BA) , Evolutionary Algorithms, Flower Pollination Algorithm (FPA) , Glowworm Swarm Optimization (GSO) , Gray Wolf Optimization (GWO) , Teaching Learning Based Optimization (TLBO) , Generalized Pattern Search (GPS) , Bacteria Foraging (BF) , Chaos Optimization Search (COS), and Shuffled Frog Leaping Algorithm (SFLA). The basic equation that describes the I-V characteristic of the model is given by [2]:
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