An improved P&O algorithm integrated with artificial bee colony for photovoltaic systems under partial shading conditions

Abstract For an efficient Photovoltaic (PV) system, tracking of true maximum power point (MPP) is essential. Therefore the maximum power point tracking (MPPT) controller is mandatory for harvesting maximum power from the solar panel. Perturb and Observe (P&O) MPPT is the simplest and most widely used low-cost MPPT method for tracking MPP. The major drawback of P&O is steady state oscillations around MPP and tracking of local MPP (LMPP) instead of global MPP (GMPP) under partial shading conditions (PSC). Thus, this paper proposes a modified P&O MPPT that can be used under PSC effectively, by integrating Artificial Bee Colony (ABC) algorithm in the first stage and P&O algorithm in the second stage. In the proposed method GMPP is first tracked by calling ABC algorithm followed by the P&O algorithm for LMPP. Thus the local search ability of P&O and global search ability of ABC are effectively combined to produce optimum duty cycle for the boost converter in a fast and efficient way. In this paper, the proposed ABC-PO algorithm is implemented in MATLAB/Simulink model and it is compared with different MPPT algorithms such as P&O, Incremental conductance (INC) and ABC. The simulation results clearly depicted that the proposed ABC-PO algorithm gives more than 99.5% efficiency under PSC.