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An improved maximum power point tracking for PV system
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.Working very far from maximum power point diminishes the created power from photovoltaic (PV) system. It is therefore vital, in order to ensure ideal operating conditions, to constantly track the Maximum Power Point (MPP) of the PV panel array. However, this is not easy to identify, due to considerable changes in external influences and the nonlinear relationship of the electrical attributes of PV panels. Therefore, Maximum Power Point Tracking (MPPT) methods can be used to uphold the PV panel operating at its MPP. To date, a number of MPPT methods have been developed, ranging from the simple to the more complex, depending on the weather conditions and the control strategies employed. This current study offers a novel approach to augment the MPPT method for the PV system, based on the Lagrange Interpolation (LI) formula and the Particle Swarm Optimisation (PSO) method. The LI method is used initially to determine the optimum value of the duty cycle in the case of the MPP, according to the operating point. Starting from that point, the PSO method can then be used to search for the true Global Peak (GP). The proposed MPPT controller essentially initialises the particles surrounding the MPP, thereby providing the initial swarm with information concerning the most effective position. This has the ability to improve PSO efficiency and lead to a more rapid convergence, with zero steady-state oscillations. Additionally, there is no need to restrict particle velocity, as the initial values are closer to MPP. Thus, the proposed technique aims to increase efficiency without adding additional complexity, thereby substantially enhancing potential tracking speeds, while also reducing the steady-state oscillation (i.e. to practically zero) once the MPP is located. This offers a number of significant improvements over the conventional PSO method, in which new operating points are at too great a distance from MPP, and thus require additional iterations. The algorithm put forward in this work is verified with an OPAL-RT real time simulator and Matlab Simulink tool. A number of simulations are undertaken and compared to: (1) the Perturb and Observe (P&O) method; (2) the Incremental Conductance (IncCond) method; and (3) the PSO based algorithm. The simulation results indicate that the proposed algorithm can effectively enhance stability and fast tracking capability under fast-changing non-uniform insolation conditions
Comparison between the Conventional Methods and PSO Based MPPT Algorithm for Photovoltaic Systems
Since the output characteristics of Photovoltaic (PV) system depends on the ambient temperature, solar radiation and load impedance, its maximum Power Point (MPP) is not constant. Under each condition PV module has a point at which it can produce its MPP. Therefore, a Maximum Power Point Tracking (MPPT) method is needed to uphold the PV panel operating at its MPP. This paper presents comparative study between the conventional MPPT methods used in (PV) system: Perturb and Observe (P&O), Incremental Conductance (IncCond), and Particle Swarm Optimization (PSO) algorithm for (MPPT) of (PV) system. To evaluate the study, the proposed PSO MPPT is implemented on a DC-DC converter and has been compared with P&O and INcond methods in terms of their tracking speed, accuracy and performance by using the Matlab tool Simulink. The simulation result shows that the proposed algorithm is simple, and is superior to the P&O and IncCond methods
A Novel MPPT Algorithm Based on Particle Swarm Optimisation for Photovoltaic Systems
This paper describes a new maximum-power-point-tracking method for a photovoltaic system based on the Lagrange Interpolation Formula and proposes the particle swarm optimization method. The proposed control scheme eliminates the problems of conventional methods by using only a simple numerical calculation to initialize the particles around the global maximum power point. Hence, the suggested control scheme will utilize less iterations to reach the maximum power point. Simulation study is carried out using MATLAB/SIMULINK and compared with the Perturb and Observe method, the Incremental Conductance method, and the conventional Particle Swarm Optimization algorithm. The proposed algorithm is verified with the OPAL-RT real-time simulator. The simulation results confirm that the proposed algorithm can effectively enhance the stability and the fast tracking capability under abnormal insolation conditions