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Applasamy V.,Quest International University Perak
ISBEIA 2011 - 2011 IEEE Symposium on Business, Engineering and Industrial Applications | Year: 2011

This article reviews several methods of calculating solar radiation from satellite derived earth atmospheric reflectivity from the visible channel. Most models calculate global and direct beam solar radiation on daily and hourly basis. Statistical models do not require precise information on atmospheric parameters whereas physical models apply these atmospheric parameters. These later evolved where authors developed hybrid models combining both. Despite a considerable number of publications which use satellite data to derive solar radiation, many models were modified and improved from existing models which were considered popular models. These popular models are briefly reviewed in this article. Most models were developed for the North American or European climate except for the physical model of Janjai et al 2005, which considered the tropical climate and the Brazillian Solar Radiation model. The models estimate hourly global solar irradiation with a RMSE between 6.8% and 25.6% while the daily global solar irradiation RMSE is between 12.9% and 18.13%. © 2011 IEEE.

Applasamy V.,Quest International University Perak
ISBEIA 2011 - 2011 IEEE Symposium on Business, Engineering and Industrial Applications | Year: 2011

This article reviews the cost to generate per kilowatt hour of electricity applying life cycle costing (LCC) analysis for a typical household of four using stand-alone photovoltaic technology. RETScreen was used to determine the cost of developing the stand-alone photovoltaic system and finally deriving to the cost per kilowatt hour for the various photovoltaic technologies taking into consideration all major components involved in the construction. Life cycle costing is employed over a period of 25 years subject to annual interest rates and inflation to accurately ascertain the cost per kilowatt hour for each photovoltaic module. A price comparison was undertaken between the national utility company and the stand-alone system. Ultimately, results show that albeit having several advantages from the environmental perspective compared to current power generating sources, the major drawback is the high cost of between RM 1.17 - RM 1.21 per kilowatt hour which is more than five times the current cost of electricity for residential household. © 2011 IEEE.

Akram N.A.,University of Nottingham Malaysia Campus | Isa D.,University of Nottingham Malaysia Campus | Rajkumar R.,University of Nottingham Malaysia Campus | Lee L.H.,Quest International University Perak
Ultrasonics | Year: 2014

This work proposes a long range ultrasonic transducers technique in conjunction with an active incremental Support Vector Machine (SVM) classification approach that is used for real-time pipeline defects prediction and condition monitoring. Oil and gas pipeline defects are detected using various techniques. One of the most prevalent techniques is the use of "smart pigs" to travel along the pipeline and detect defects using various types of sensors such as magnetic sensors and eddy-current sensors. A critical short coming of "smart pigs" is the inability to monitor continuously and predict the onset of defects. The emergence of permanently installed long range ultrasonics transducers systems enable continuous monitoring to be achieved. The needs for and the challenges of the proposed technique are presented. The experimental results show that the proposed technique achieves comparable classification accuracy as when batch training is used, while the computational time is decreased, using 56 feature data points acquired from a lab-scale pipeline defect generating experimental rig. © 2014 Elsevier B.V. All rights reserved.

Chia Y.Y.,University of Nottingham Malaysia Campus | Lee L.H.,Quest International University Perak | Shafiabady N.,University of Nottingham Malaysia Campus | Isa D.,University of Nottingham Malaysia Campus
Applied Energy | Year: 2015

This paper presents the use of a Support Vector Machine load predictive energy management system to control the energy flow between a solar energy source, a supercapacitor-battery hybrid energy storage combination and the load. The supercapacitor-battery hybrid energy storage system is deployed in a solar energy system to improve the reliability of delivered power. The combination of batteries and supercapacitors makes use of complementary characteristic that allow the overlapping of a battery's high energy density with a supercapacitors' high power density. This hybrid system produces a straightforward benefit over either individual system, by taking advantage of each characteristic. When the supercapacitor caters for the instantaneous peak power which prolongs the battery lifespan, it also minimizes the system cost and ensures a greener system by reducing the number of batteries. The resulting performance is highly dependent on the energy controls implemented in the system to exploit the strengths of the energy storage devices and minimize its weaknesses. It is crucial to use energy from the supercapacitor and therefore minimize jeopardizing the power system reliability especially when there is a sudden peak power demand. This study has been divided into two stages. The first stage is to obtain the optimum SVM load prediction model, and the second stage carries out the performance comparison of the proposed SVM-load predictive energy management system with conventional sequential programming control (if-else condition). An optimized load prediction classification model is investigated and implemented. This C-Support Vector Classification yields classification accuracy of 100% using 17 support vectors in 0.004866. s of training time. The Polynomial kernel is the optimum kernel in our experiments where the C and g values are 2 and 0.25 respectively. However, for the load profile regression model which was implemented in the K-step ahead of load prediction, the radial basis function (RBF) kernel was chosen due to the highest squared correlation coefficient and the lowest mean squared error. Results obtained shows that the proposed SVM load predictive energy management system accurately identifies and predicts the load demand. This has been justified by the supercapacitor charging and leading the peak current demand by 200. ms for different load profiles with different optimized regression models. This methodology optimizes the cost of the system by reducing the amount of power electronics within the hybrid energy storage system, and also prolongs the batteries' lifespan as previously mentioned. © 2014 Elsevier Ltd.

Yagasena,Quest International University Perak
ARPN Journal of Engineering and Applied Sciences | Year: 2016

Two years of rain rate and attenuation measurement in Malaysia is presented. The analysis shows that the attenuation follows the power law and linear law for rain rates below and above a certain cut-off rain rate respectively. A simple Two-Part model is proposed for consideration in the tropical regions. © 2006-2016 Asian Research Publishing Network (ARPN).

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