Wooi Chin Leong
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Preferred name
Wooi Chin Leong
Official Name
Leong, Wooi Chin
Alternative Name
Wooi, Chin-Leong
Wooi, C. L.
Wooi, Chin-leong
Main Affiliation
Scopus Author ID
55912811600
Researcher ID
V-2268-2019

Research Output
Now showing 1 - 4 of 4
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Feasibility, sizing and economic analysis of solar energy system for green Swinburne campus

2020 , Mohamed Ashfaaq Riphque , Hadi Nabipour-Afrouzi , Wooi Chin Leong , SanChuin Liew , Kamyar Mehranzamir , Jubaer Ahmed

In 2011 alone, Malaysia has spent a hefty RM14.5 billion in buying fossil fuels to generate electricity. With the cost of fossil fuels rising each year, the nation is looking to rely on more sustainable energy sources. One way to decrease the reliance on fossil fuel is to minimize electricity consumption or use renewable energy sources. This research tests if it is feasible to run the Swinburne campus with Solar photovoltaic (PV) cells. This study aims to design and implement a PV energy system to power Swinburne University of Technology Sarawak. The design of the PV system is performed with the collection of the solar irradiance in Kuching, load profile of the Campus and the area required to layout the panels. Block-E of Swinburne is chosen for the PV system since all Swinburne blocks together consume very high power and do not have the required area for the panels. A Grid-Tied system is considered for the PV design. MATLAB software is used to size the PV system. Economic analysis such as Levelized Cost of Energy (LCOE), Life Cycle Cost Analysis (LCCA) and payback period is studied for the PV system.

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Publication

Grounding performance of hydrogel, silica gel and charcoal ash as additive material in grounding system

2024-06 , Wooi Chin Leong , Shafirol Nizam Osman , Hadi Nabipour Afrouzi , Syahrun Nizam Md Arshad @ Hashim , Kamyar Mehranzamir , Muzaidi Othman @ Marzuki

Grounding enhancement materials (GEMs) are one of the additive materials which can change the grounding performance without lots of significant costs. The study aimed to assess the performance of laterite and peat soil, copper and galvanized conductors, and determine the effectiveness of additional materials in reducing grounding resistance. Altering soil characteristics can enhance the conductor's contact area, achieving lower grounding resistance without high costs. Hydrogel, silica gel, and charcoal ash were mixed with soil for testing. Grounding resistance values were measured and collected using the Fall-of-Potential Method using Kyoritsu Earth-Tester-Model-4102. The number of GEMs used were 300g and 600g. Hydrogel, silica gel, and charcoal ash added to soil reduced grounding resistance. Among the various Ground Enhancement Materials (GEMs) tested, hydrogel exhibited the most impressive performance, boasting the lowest grounding resistance at just 56% compared to the reference grounding system. Silica gel followed closely as the second-best performer, with an average grounding resistance of 77% relative to the reference system and lastly is charcoal ash with an average grounding resistance of 77% relative to the reference system. These GEMs significantly enhanced soil conductivity. Furthermore, when considering different soil types and conductor materials, it was observed that peat soil combined with galvanized conductors achieved notably lower grounding resistance in comparison to laterite soil and copper conductors, respectively.

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Development of biomass-fueled cogeneration systems for off-grid electrification

2020 , Mohammad Ali Bagherian , Kamyar Mehranzamir , Jubaer Ahmed , Marwan Nafea , Hadi Nabipour-Afrouzi , Wooi Chin Leong

Over the past centuries, discovery and utilization of fossil fuels has revolutionized the world pushing societies towards modernization. This source of energy that is being referred as black gold, has faced extensive consumption which consequently started to underpin the acceptance of the negative effects of our carbon society and its likely demise. Fossil fuels have costed the safety and overall health of our environment, causing serious consequences such as global warming and unsustainability. To this end, in the search for alternative generation, the potential of renewables was realized and hence, exploited in the energy sector. In recent times, development of efficient prime movers and possible utilization of renewable energy resources for both power and heat production introduced the concept of cogeneration to the energy sector. Accordingly, this comparative research elaborates on utilization of the most available and versatile renewable energy, biomass, in combined heat and power technology. There is no doubt that the integration of bioenergy and cogeneration is associated with significant advantages, namely: lower environmental impacts and primary energy savings. In this context, four different biomass-fueled configurations are developed for the purpose of electrifying the Masset village in Canada. Two well-known methods of: Direct Combustion and Gasification are considered in the model development. Further, a comparative conclusion is drawn from economic perspective.

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Artificial neural network application in an implemented lightning locating system

2020 , Kamyar Mehranzamir , Zulkurnain Abdul-Malek , Hadi Nabipour Afrouzi , Saeed Vahabi Mashak , Wooi Chin Leong , Roozbeh Zarei

Time difference of arrival (TDOA) technique is one of many bases to determine lightning strike location employed in a lightning locating system (LLS). In this technique, at least four measurement sensors are required to correctly locate a lightning strike. Usage of fewer number of sensors will result in non-unique solutions to the generated hyperbolas, and hence wrong lightning strike point. This research aims to correctly determine the strike point even if only three measuring sensors are utilized. An artificial neural network (ANN) based algorithm was developed for a 400 km2 coverage area in Southern Malaysia using time of arrival data collected at the three measuring stations over a certain period. The Levenberg–Marquardt algorithm is demonstrated to correctly identify the lightning strike coordinates with an average error of 350 m. The algorithm has helped the three-station TDOA-based LLS to successfully locate the lightning strike point with a remarkable accuracy comparable to that of commercial systems.

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