Bayesian fusion for maximum power output in hybrid wind-solar systems

We address the topic of a unified controller for maximum power point tracking (MPPT) in distributed hybrid PV and wind 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 maximum power controllers adaptively search and maintain operation at the maximum power point for changing irradiance and wind speed conditions, thus maximizing the system output power and consequently minimizing the overall system cost. Various conventional MPPT algorithms have been proposed for ideal conditions, few algorithms were derived to extract true maximum power under abrupt changes in wind speed and partial shading conditions. Very few algorithms have addressed the problem of very fast changes in wind speed and continuously varying shading. 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 wind speed and uneven insolation.