An improved maximum power point tracking for PV system

Abstract

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.