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Research Article Open access CC BY 4.0

Optimal Control of 3.5 MW Solar Hybrid Power Plant of Joseph Sarwuan Tarka University Makurdi, Benue State, Nigeria

Emmanuel A. Nyiekaa, Kumator M. Iortim, Jonathan U. Agber

Journal of Energy Research and Reviews · pp. 42–56 · Published 18 Oct 2022

10.9734/jenrr/2022/v12i3242

Abstract

The operation of power-generating plants becomes complex when two or more sources of power are combined. The decision of which source should take priority of load demand at various intervals requires sensitive control approaches. The Joseph Sarwuan Tarka University Makurdi (JOSTUM) 3.5 Mega Watts Solar Hybrid Power Plant (SHPP) has one source of renewable system; photovoltaic (PV) array, one source of energy storage system; battery banks, and one source of backup system; diesel generators (DGs), power conversion devices; Photovoltaic inverters (PVIs), power conditioning systems (PCSs) and switchgears were modelled and simulated. The   emphasis of the research is to obtain a control technique, which when applied to the PVI would improve the power produced from the PV array system. For a PV module to harvest the maximum amount of solar energy and simultaneously attain higher efficiency, PV systems must be operated at their maximum power point (MPP) under partially shaded conditions (PSC), varying irradiances and temperature. Maximum power point tracking (MPPT) methods are capable of guaranteeing MPP under varying climatic conditions. A control scheme is proposed using Whale Optimization Algorithm (WOA)-based MPPT control technique in order to improve the output power of the PV array. The output of the existing system from the PV array is about 1,800 kW, but when the WOA-based MPPT control technique is applied, an output of about 2,800 kW is produced. To validate the choice of the proposed technique, perturb and observe (P and O) algorithm for MPPT was tested on the system, which generated a mean power of 2,300 kW. The results proved that the proposed technique has an efficiency of 55.56% greater than the existing and P and O MPPT- based algorithm, which has 27.78%. The simulation was accomplished via MATLAB/Simulink.

Photovoltaic inverter power conditioning system maximum power point tracking perturb and observe whale optimization algorithm

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