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Subject: Electrical Engineering × Author: Joshi, Puneet × End date: 2019 × Start date: 2016 × Type: Thesis × Thesis Type: Ph.D ×
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    ThesisItemOpen Access
    Development and performance investigation of a novel meta-heuristic based MPPT for photovoltaic systems
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2017-07) Joshi, Puneet; Arora, Sudha
    Solar energy is transformed into Electrical energy through PV systems consisting of PV panels, power conditioners & controllers and a load. Generally, there is a shift in the maximum power point with the change in the environmental conditions, a load change or due to partial shading. Hence, Maximum Power Point Tracking (MPPT) controller is required that ensures the maximum power extraction under all conditions. Many MPPT techniques for PV systems have been developed that are broadly classified as conventional methods and the modern approaches. These techniques vary in many aspects such as PV array dependency, application domain, complexity, convergence speed, periodic tuning, efficiency, simplicity, digital or analog implementation, sensors required, and cost. This work presents a novel hybridization of the Particle Swarm Optimization and the Coral reef Optimization approaches for MPPT for PV systems. In order to study the performance of the proposed method, ten widely-adopted MPPT algorithms; viz., Perturb and Observe (P&O) method, Adaptive step-size P&O method, Drift-free P&O method, Incremental Conductance (INC) Method, Adaptive step-size INC method, Incremental Resistance (INR) Method, Adaptive step-size INR method and three meta-heuristics based approaches, viz., Particle Swarm Optimization (PSO) based method, Gravitational Search Algorithm (GSA) based method, hybrid PSO-GSA method are compared with it using the Matlab/Simulink software. Firstly, the behavior of the conventional techniques was studied in presence of solar irradiation variations, imitating the natural variations occurring in the irradiance throughout the year, under constant temperature (250C). Thereafter, the methods were ranked based on the power obtained under each test conditions. In addition, the superiority of the methods was highlighted by simulation results. It was concluded that both INC method and the conventional P&O method showed the best overall efficiency ( ≈ 97%) under all test patterns. However, the meta-heuristics based approaches displayed even better results for the MPPT application. These methods, in general, exhibited very high efficiency ( ≈ 98.8%) and convergence rate under various test conditions. Amongst the selected methods, the proposed PSO-CRO method displayed highest efficiency ( ≈ 99.6%) and fastest convergence rate. Additionally, statistical analysis and two-sampled T-test were performed to determine the ordering of the methods. In addition, the meta-heuristics based approaches were tested under four partial shading conditions as well. All the methods were able to converge to the global maximum power again very quickly with an excellent efficacy. Simulation results show that the proposed PSO-CRO method can rapidly track the true MPP under different conditions with reduced steady state oscillations (to practically zero) once it is located. Furthermore, the proposed method has the ability to track the MPP for the extreme environmental condition, e.g., large fluctuations of insolation and partial shading condition. The outcome indicates the proposed method has obvious advantages, especially the performance being superior to the conventional methods. Additionally, the algorithm is simple to program and can be computed very rapidly using state-of-the art hardware technologies.