A Study and investigation approaches for performance enhancement in renewable energy conversion system

Abstract

Renewable energy resources like solar photovoltaic (PV) power newlinegeneration are widely employed for grid scale power generation because of newlinetheir low environmental effect, sustainability, no fuel cost, low plant newlineoperation costs and pollution free power generation. After obtaining DC from newlinepanels, it is necessary to convert to AC to meet both local and industrial newlinepower essentials. Therefore, inverters are quite significant in this regard. The newlinevarious advantage of multilevel inverter replaces the role of the conventional newlineinverter. Grid integration of renewable energy sources into the utility grid newlineraises technical and economic difficulties in the power quality domain. newlineThe Maximum power monitoring efficiency of PV is crucial to the newlinegird connected distribution system due to non-linearity attributes and newlineenvironmental changes. Therefore, the power generated from the PV panels newlineneeds to be maximized, which makes the PV system to work at its maximum newlinepower point. Many Maximum Power Point Tracking (MPPT) algorithms fail newlineto track the global peak power under partial shading conditions. As a result, newlinean effort was made to design powerful maximum power point tracking newlinealgorithms that are simple to construct and execute while also being cost newlineeffective. newlineThis research proposes an intelligent hybrid algorithm for newlinemaximum power point tracking in the solar photovoltaic system. The newlineintegration of the Extreme Learning machine (ELM) with the BAT algorithm newlinestructures the Hybrid Efficient Reliable BATs (HERBS) algorithm to track newlinethe maximum power efficiently with minimal time. The HERBS algorithm is newlineexperimentally justified using an ARM processor and simulated in a newlineMATLAB environment to check out the full control under partial shading newlineconditions. newline