Charles Darwin University

CDU eSpace
Institutional Repository

 
CDU Staff and Student only
 

An off-grid hybrid PV/diesel model as a planning and design tool, incorporating dynamic and ANN modeling techniques

Yap, Wai Kean and Karri, Vishy (2015). An off-grid hybrid PV/diesel model as a planning and design tool, incorporating dynamic and ANN modeling techniques<br />. Renewable Energy,78(June):42-50.

Document type: Journal Article
Citation counts:
Google Scholar Search Google Scholar

IRMA ID 75039815xPUB811
Title An off-grid hybrid PV/diesel model as a planning and design tool, incorporating dynamic and ANN modeling techniques
Author Yap, Wai Kean
Karri, Vishy
Journal Name Renewable Energy
Publication Date 2015
Volume Number 78
Issue Number June
ISSN 0960-1481   (check CDU catalogue  open catalogue search in new window)
eISSN 1879-0682
Scopus ID 2-s2.0-84921340621
Start Page 42
End Page 50
Total Pages 9
Place of Publication United Kingdom
Publisher Pergamon Press
Language English
Field of Research ENVIRONMENTAL SCIENCES
HERDC Category C1 - Journal Article (DIISR)
Abstract Remote towns and communities are normally without access to the main electrical grid and electricity is normally generated through diesel generators. Diesel fuel costs represent a significant portion of the utilities' expenditure. Solar photovoltaic (PV) integration is an attractive solution reduces fossil fuel dependency for such communities. This study presents an off-grid hybrid PV/diesel model developed using dynamic modelling and artificial neural network (ANN) techniques. Dynamic subsystem models were developed in Simulink and ANN methods were employed for predictive modelling. Utilizing simple climate data (humidity, rain fall, ambient temperature and wind speed) and load profile as model inputs, generator and PV output powers and fuel consumption can be accurately predicted. Experimental data were used for ANN training and model validation. A comparative analysis was conducted between the Simulink model and an existing industrial design tool for a remote community in the Northern Australia. Simulation results showed that the developed model is a viable planning and analytical tool for aiding future off-grid PV-to-diesel system integration applications, with R2 values ranging from 0.92 to 0.99 and mean relative errors below 5%. Lastly, the incorporation of both dynamic and ANN modelling techniques in a single model reduces modelling complexity whilst maintaining its accuracy and ease-of-use.
Keywords Hybrid solar-diesel systems
Artificial neural network
Predictive modelling
Photovoltaic modelling
DOI http://dx.doi.org/10.1016/j.renene.2014.12.065   (check subscription with CDU E-Gateway service for CDU Staff and Students  check subscription with CDU E-Gateway in new window)
 
Versions
Version Filter Type
Access Statistics: 40 Abstract Views  -  Detailed Statistics
Created: Mon, 12 Oct 2015, 16:48:01 CST by Kean Yap