eCite Digital Repository

Improving the performance of time invariant maximum power point tracking methods

Citation

Galligan, H and Lyden, S, Improving the performance of time invariant maximum power point tracking methods, Proceedings from the Australian Universities Power Engineering Conference, 19-22 November 2017, Melbourne, Australia, pp. 1-6. ISBN 9781510846531 (2017) [Refereed Conference Paper]


Preview		PDF (Submitted conference paper)
471Kb		  

Copyright Statement

Copyright 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

DOI: doi:10.1109/AUPEC.2017.8282452

Abstract

This paper presents an improved reinitialisation condition for time invariant maximum power point tracking (MPPT) methods used in photovoltaic (PV) systems experiencing partial shading conditions (PSC). Time invariant (MPPT) methods, such as Particle Swarm Optimisation (PSO), overcome the limitations of existing MPPT by tracking the global maximum power point (GMPP) of a PV system operating under PSC. However, due to the time invariant structure of these MPPT methods, they also require a reinitialisation condition to be defined for when a change in irradiance or temperature occurs. Testing was performed using simulations of a model built in Matlab/ Simulink, where the performance of existing and developed conditions was evaluated using test cases with changes in solar irradiance. Limitations of existing conditions were identified and a more robust reinitialisation condition developed. The developed reinitialisation condition used sentry particles to monitor the PV voltage range for changes in the measured power of any sentry. The developed condition had a 96 % rate of successful detection, as compared to as low as 68 % successful detection for existing methods, demonstrating improved performance and robustness.

Item Details

Item Type:Refereed Conference Paper
Keywords:photovoltaic, maximum power point tracking, particle swarm optimisation, partial shading conditions, reinitialisation condition
Research Division:Engineering
Research Group:Electrical engineering
Research Field:Electrical energy generation (incl. renewables, excl. photovoltaics)
Objective Division:Energy
Objective Group:Renewable energy
Objective Field:Solar-photovoltaic energy
UTAS Author:Galligan, H (Mr Harry Galligan)
UTAS Author:Lyden, S (Dr Sarah Lyden)
ID Code:123945
Year Published:2017
Deposited By:Engineering
Deposited On:2018-02-02
Last Modified:2018-07-31
Downloads:132 View Download Statistics

Repository Staff Only: item control page