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

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Design and performance evaluation of a novel time measurement calibration device for electric power systems

2025-06 , Wooi Chin Leong , Yan Huang , Hadi Nabipour Afrouzi , Hieng Tiong Su , Ismat Hijazin

In order to solve the difficulty of digital signal calibration of electric power equipment, such as low precision, inability to test the full range, and complicated configuration, and further promote the development of power system, a proposed time measurement calibration device is designed, and its performance is verified in this paper. This paper points out the main drawbacks of the existing calibration system, carries on the design innovation of the key technologies based on FPGA (Field Programmable Gate Array), puts forward the optimization method of the software and hardware, and verifies the accuracy of the input and output signal by experiments. The accuracy of input and output SV, GOOSE, and contact signal of the proposed calibration device in this paper can be better than 10μs, which is a meaningful improvement in accuracy and efficiency for time measurement calibration.

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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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Optimization of Distribution System Reliability Using Dandelion Optimizer

2024-02 , Tang Jia Wen , Wooi Chin Leong , Wen-Shan Tan , Hadi Nabipour Afrouzi , Syahrun Nizam Md Arshad @ Hashim , Yee Von Thien

This study proposes the Dandelion Optimizer (DO), a novel nature-inspired algorithm based on dandelion plant life cycles, for optimizing lightning protection system (LPS) placement in radial distribution networks with 81 buses. The objective is to minimize System Average Interruption Frequency Index (SAIFI) and Momentary Average Interruption Frequency Index (MAIFI). The DO offers a user-friendly approach to determining optimal solutions and excels in pinpointing optimal LPS deployment locations within distribution systems, concurrently fastening the calculation of SAIFI and MAIFI. Validation involves comprehensive numerical simulations, considering lightning flashover rate, distribution feeder attributes, and LPS configuration. Comparative analysis with Grey Wolf Optimizer (GWO) and Dragonfly Algorithm (DA) affirms DO's superior performance. Notably, DO showcases better capability in finding local optima and faster convergence than GWO and DA. Applying DO to optimise the optimal placements of LPSs yields a commendable 6.16% reduction in reliability indices, surpassing GWO and DA's 6.14% and 5.14% reductions. DO also exhibits heightened convergence speed over GWO and DA.

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Optimized allocation of lightning protection system using PSOGSA

2023 , Jia Wen Tang , Wooi Chin Leong , Wen-Shan Tan , Hadi Nabipour Afrouzi , Syahrun Nizam bin Md Arshad Hashim , Muzaidi Othman @ Marzuki

In this paper, the hybrid PSOGSA, which is a combined algorithm of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA), is proposed to find the optimum locations for the lightning protection system on the 81- bus radial distribution system. Moreover, the System Average Interruption Frequency Index (SAIFI) is considered as the objective function and will be minimized. The main advantage of this work is the simplicity and convenience of finding an optimal solution using the proposed PSOGSA algorithm. Additionally, PSOGSA is also capable of finding the optimal locations for applying a lightning protection system (LPS) in a distribution network, while minimizing SAIFI and maintaining computational efficiency. To validate the effectiveness of the proposed algorithm, numerical simulations are carried out considering the interdependency between lightning phenomena and the distribution feeder characteristics, namely, the flashover rates due to direct and induced lightning. In addition, a comparison between PSO, GSA, and PSOGSA is made to compare and validate the performance of the algorithms. The results show that the latter is better at escaping from local optima and has a faster convergence than the standard PSO and GSA. PSOGSA also managed to achieve a higher reduction of 12.10% SAIFI after applying LPS on the optimal feeders, as compared to the 10.79% and 11.77% reduction of SAIFI by GSA and PSO, respectively. PSOGSA also has a faster convergence speed than PSO.

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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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