The partial shading of PV modules due to clouds or blocking objects such as buildings or tree leaves is a common problem for photovoltaic systems. To address this, Maximum Power Point Tracking (MPPT) is implemented to find the Global Maximum Power Point (GMPP). In this thesis, a new hybrid MPPT is proposed that combines a modified Grasshopper Optimization Algorithm (GOA) with Incremental Conductance (IC). In the first stage, the proposed modified GOA is implemented to find a suitable tracking area where GMPP is located. Then the system moves to the second stage by implementing IC to get the correct GMPP. IC is a fast-performing and reliable algorithm. By combining GOA and IC, the proposed method can find GMPP accurately with short tracking time. Various experimental results show that the proposed method yields the highest tracking efficiency and lowest tracking time compared to state of the art MPPT algorithms such as particle swarm and modified firefly optimizations.

The partial shading of PV modules due to clouds or blocking objects such as buildings or tree leaves is a common problem for photovoltaic systems. To address this, Maximum Power Point Tracking (MPPT) is implemented to find the Global Maximum Power Point (GMPP). In this thesis, a new hybrid MPPT is proposed that combines a modified Grasshopper Optimization Algorithm (GOA) with Incremental Conductance (IC). In the first stage, the proposed modified GOA is implemented to find a suitable tracking area where GMPP is located. Then the system moves to the second stage by implementing IC to get the correct GMPP. IC is a fast-performing and reliable algorithm. By combining GOA and IC, the proposed method can find GMPP accurately with short tracking time. Various experimental results show that the proposed method yields the highest tracking efficiency and lowest tracking time compared to state of the art MPPT algorithms such as particle swarm and modified firefly optimizations.

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