Dynamic and Adaptive Maximum Power Point Tracking Using Sequential Monte Carlo Algorithm for Photovoltaic System

To produce maximum output power in a solar collector system, a maximum power point tracker (MPPT) is considered a vital component in system design. Though, due to partial shading effect initiated by dynamic weather conditions, the tracking process becomes more complex when locating the global maximum power point (GMPP). To solve this issue, an innovative and adaptive MPPT based on sequential Monte Carlo is suggested to accurately and efficiently predict the global peak under rapid changing weather conditions. The proposed method adaptively predicts the next best duty cycle value that will generate the maximum output power. The capability of the technique has been tested strongly under standard test conditions (STC) and dynamic weather conditions including random partial shading and changing irradiance and temperature input values. The new recommended technique is compared to the classical perturb and observe algorithm, in addition to the particle swarm optimization, and flower pollination tracking techniques. The simulated results illustrated dominance in accuracy and efficiency under varying environment conditions. The efficiency calculated was found to be as high as 99.89% at STC and as low as 98.58% at dynamic and random partial shading conditions. In addition, the results displayed high tracking speed in predicting the GMPP while maintaining no oscillations at the output power.