A novel unified maximum power point tracker for controlling a hybrid wind-solar and fuel-cell system

In the districts where solar energy and wind energy are naturally complementary, the combination of wind-solar generation systems can considerably reduce the storage capacity of batteries and the total cost of the system. But the efficient and reliable operation of these hybrid systems depends on 1) their availability at all times, and 2) the control strategies of their controller. We address the topic of a unified controller for maximum power point tracking (MPPT) in distributed hybrid PV, wind and fuel-cell energy systems. The power produced by a PV module depends on the solar irradiance and temperature. The power produced by a wind turbine depends on the wind speed. The power produced by a fuel-cell depends on the level of hydrogen consumption. The maximum power controllers adaptively search and maintain operation at the maximum power point for changing irradiance, wind speed and hydrogen-consumption conditions, thus maximizing the system output power and consequently minimizing the overall system cost. A variety of conventional MPPT algorithms have been created for ideal conditions, not many algorithms were derived to extract true maximum power under abrupt changes in wind velocity, partial shading, and temperature conditions. Under these dynamically changing conditions, the conventional MPPT controllers can't find the true MPP (global MPP) and are often track to a local one. In this work, results are obtained for a tracking algorithm based on Bayesian information fusion combined with swarm intelligence. Compared to state-of-the-art trackers, the system achieves global maximum power tracking and higher efficiency for hybrid systems with different optimal current, caused by continuously changing environmental and load conditions.