access icon free Active power control of a photovoltaic system without energy storage using neural network-based estimator and modified P&O algorithm

© The Institution of Engineering and Technology
Received 01/02/2017, Accepted 16/10/2017, Revised 22/07/2017, Published 17/01/2018

This study proposes a variable step size modified P&O algorithm for active power control (APC) that ensures that a predetermined amount of power, which is less than the maximum power, is extracted from the photovoltaic system without any energy storage. Under varying environmental conditions, the maximum available power varies and care should be taken to ensure that the desired operating point is below and on the right side of the current maximum power point (MPP). This requires real-time estimation of MPP. A neural network (NN)-based MPP estimator is proposed. With NN-based MPP estimator and modified P&O algorithm, a novel APC scheme is shown to perform well under varying environmental and demand conditions. This method of APC has been validated through simulation and experimental results.

Inspec keywords: power control; photovoltaic power systems; power system control; neural nets; maximum power point trackers

Other keywords: neural network-based MPP estimator; active power control; varying environmental conditions; MPP real-time estimation; modified P&O algorithm; photovoltaic system; current maximum power point

Subjects: Neural computing techniques; Power electronics, supply and supervisory circuits; Power system control; DC-DC power convertors; Power and energy control; Control of electric power systems; Solar power stations and photovoltaic power systems

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