Studies on Performance Improvement of Hybrid Power Generation Systems

Abstract

In recent years, the solar energy has been considered as one of principal renewable newlineenergy (RE) source for electric power generation. However, the maximization of newlineextracted power from PV system is a matter of concern as its conversion efficiency is newlinelow. The partial shading and dust accumulation effect on PV array are the major newlineenvironmental issues, which reduces the PV performance. Therefore, a maximum power newlinepoint tracking (MPPT) controller is necessary in a PV system for maximum power newlineextraction. Therefore, the performance of advanced MPPT methods such as fuzzy logic newlinecontroller (FLC) and artificial neural network (ANN) are investigated in this thesis, and newlinecompared with traditional MPPT methods such as constant voltage (CVC), perturb and newlineobserved (PandO) and incremental conductance (IC). Further, the PV system performance newlineis investigated for different irradiation conditions in terms of voltage, current, power. newlineFurthermore, a detailed study and analysis of various shading patterns on different PV newlinearray configurations is carried out to investigate the improvement in performance. Apart newlinefrom existing configurations e.g. series-parallel (SP), bridge link (BL), total cross tied newline(TCT), honey-comb (HC); novel configurations such as hybrid total cross tied-series newlineparallel (TCT-SP), total cross tied- bridge link (TCT-BL), cross tied-honey comb (CTHC) newlineare proposed. All these configurations are analyzed using performance index such newlineas maximum power, power losses and fill factor (FF) etc. which represent the ability to newlinesustain the shading effects. newline