*WINNER* Adaptive Step Size Incremental Conductance Based Maximum Power Point Tracking

Abstract

Extracting maximum power available from a photovoltaic array requires operating at maximum power point (MPP), which changes with multiple environmental factors, mainly temperature and irradiance. Because any practical PV system would be under a condition in which these factors change, the operating point of a PV array needs to be constantly updated to a new MPP. The process to identify the new MPP is called maximum power point tracking (MPPT). Traditionally, incremental conductance MPPT algorithm with fixed step size is used. This algorithm, however, suffers from a trade-off between convergence speed and accuracy. I propose an incremental conductance MPPT algorithm with variable step size, which adaptively changes step size after each iteration based on how far away the current operating point is from a new MPP. This is done by updating step size to a product of derivative of power with respect voltage and an exponential decaying function, which effectively increases step size as the operating point gets further from the MPP, and decreases step size as the operating point approaches the MPP. With this algorithm, the aforementioned trade-off can be dramatically mitigated as faster convergence speed can be achieved with little to no power loss. A series of simulations involving variation of temperature and irradiance were performed using MATLAB, and convergence speed and accuracy were compared with the traditional fixed step size algorithm.