Kit Guan Lim

Universiti of Malaysia Sabah

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Co-Authors

Kenneth Tze Kin Teo

Universiti of Malaysia Sabah

Min Keng Tan

University of Chinese Academy of Sciences

Helen Sin Ee Chuo

Universiti of Malaysia Sabah

Renee Ka Yin Chin

Universiti of Malaysia Sabah

Chun Hoe Lee

Universiti of Malaysia Sabah

Soo Siang Yang

Universiti of Malaysia Sabah

Bih Lii Chua

Universiti of Malaysia Sabah

Ahmad Razani Haron

Universiti of Malaysia Sabah

Liau Chung Fan

Universiti of Malaysia Sabah

Lorita Angeline

Universiti of Malaysia Sabah

Cooperative Institutions

Universiti of Malaysia Sabah

University of Technology Malaysia

Universiti Sains Malaysia

Information Technology University

Conference Board

Universiti Teknologi MARA

National University of Malaysia

American Society of Safety Professionals

Universiti Putra Malaysia

Universiti Malaysia Pahang Al-Sultan Abdullah

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Adapting Photovoltaic Perturbation Voltage with Particle Swarm Optimization

Min Keng Tan Kah Ming Yap Kit Guan Lim Soo Siang Yang Pungut Ibrahim

This work presents a particle swarm optimization (PSO) based maximum power point tracking (MPPT) system for maximizing the output power of a photovoltaic (PV) system and minimizing the output power fluctuation. Under partially shaded condition (PSC), the P-V characteristics will exhibit multiple peaks. The conventional MPPT approaches, such as perturb and observe (P&O) algorithm will tend to track the local maximum power point (LMPP) instead of the global maximum power point (GMPP). The fix perturbation size of the P&O algorithm will lead to power losses as the output power fluctuates at steady state. Thus, PSO based MPPT algorithm is proposed to track the GMPP and minimize the fluctuation in output power. The proposed algorithm will determine the optimum perturbation size for MPPT controller based on the information of change in voltage and change in power. The simulation results show that the optimized MPPT is able to track the GMPP under PSC accurately, fast and with zero oscillation in comparison with conventional MPPT. Parameter's tuning is important for PSO based MPPT to achieve good transient response, particularly the inertia weight. In conclusion, the PSO based MPPT can ensure optimal and stable power generation from the PV system.

10.1109/scored53546.2021.9652760

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Citations (4)

Advanced Fault Detection in DC Microgrid System using Reinforcement Learning

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) (2021)

Min Keng Tan Kar Leong Lee Kit Guan Lim Ahmad Razani Haron Pungut Ibrahim

As technologies are expanding, the demand for power supply also increases. This causes the demand for power is difficult to be fulfilled as non-renewable sources are reducing. Therefore, the microgrid concept is introduced, where it is constructed with renewable energy sources, energy storage devices and loads. There are two types of microgrid, which are alternating current (AC) microgrid and direct current (DC) microgrid. Various research show that DC microgrid has more advantages over AC microgrid. However, DC microgrid is not widely used due to the lack of studies on it compared to AC microgrid. Besides, DC microgrid has one significant problem not fixed, which is the fault in the DC microgrid. Whenever a fault occurs, the whole DC microgrid will be affected rapidly. Therefore, this project aims to design a fault detector based on artificial intelligence to detect the fault and isolate the fault effectively. A fault detector based artificial intelligence should be implemented into the DC microgrid system to protect it. Two techniques in Artificial Immune System are being compared. The results showed that the improved Negative Selection Algorithm with variable sized detector has better performance than the general Negative Selection Algorithm with constant sized radius in detecting fault in DC microgrid system.

Microgrid

10.1109/iicaiet51634.2021.9573711

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Citations (2)

Bio-based nanomaterials for dye removal

Elsevier eBooks (2025)

Chi Huey Ng Fiona Wang Ming Ling Mohd Syahlan Mohd Syukri Kit Guan Lim Junidah Lamaming

Nanomaterials

10.1016/b978-0-443-21701-2.00016-7

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Citations (0)

Maximizing Power Generation in Variable Speed Micro-Hydro with Power Point Tracking

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) (2022)

Min Keng Tan Norafe Maximo Javinez Kit Guan Lim Ahmad Razani Haron Pungut Ibrahim

Conventional variable speed micro-hydro control systems suffer from non-optimal input control. The controllers estimate the changes in flow rate without anticipating the global maximum power curve. As such, this paper aims to explore and develop a feasible maximum power point tracker (MPPT) with perturb and observe (P&O) and genetic algorithm (GA) in providing optimal power generation for variable speed micro-hydro system. This research first introduces a mathematical model for an experimental variable speed micro-hydro platform and then simulates the micro-hydro in MATLAB. Conventional P&O MPPT algorithm used fixed perturbation size which requires large computation time when the perturbation size is small and suffers from power fluctuation issues when the perturbation size is large. Thus, a GA-based P&O MPPT algorithm with adaptive perturbation size is proposed to provide a large perturbation size during transient response and a small perturbation size at a steady state. The simulation results showed that the proposed GA-based P&O MPPT algorithm was able to track the global maximum power point (MPP).

Maximum power principle

10.1109/iicaiet55139.2022.9936859

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Employing Portable Vehicle-To-Infrastructure Transceiver with Network Coding

Kit Guan Lim Zhen Hua Chu Helen Sin Ee Chuo Min Keng Tan Herwansyah bin Lago

In Vehicle-to-Infrastructure (V2I) technology, transmission between vehicles and infrastructures could be a major challenge due to the moving environment of vehicle, as well as the changing distance and speed of vehicle during the communication with infrastructure. This project starts with the review of past and present researches that are related to V2I communication. Prototype of wireless data transceivers are developed to identify the optimum transmission range. Store-and-forward (SF) algorithm is a process of storing and forwarding data from one data transceiver to another data transceiver. This technique is used to overcome the limitation of transmission range by continuously passing information from one point to another. However, during the transmission, the process taken for data exchanging will affect the entire transmission time. Thus, an algorithm named Network coding (NC) is introduced to reduce the transmission while maintaining the stable connection. NC is a process of combining two or more sources of data to reduce number of transmissions, which is very useful in data exchanging. These algorithms are computed in MATLAB and simulated for performance analysis. Apart from that, SF and NC are also programmed into wireless data transceivers to carry out real time experiment. Simulation results showed that SF uses 0.1664 s to exchange 256 bits data while NC spends 0.1266 s to complete the same mission. Hence NC is showing better performance than SF.

Transceiver

Linear network coding

10.1109/scored53546.2021.9652733

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Dynamic Acceleration Coefficient of Particle Swarm Optimization in Robotics Motion Coordination

International Journal of Simulation Systems Science & Technology (2020)

Tang Nyiak Tien Kit Guan Lim Min Keng Tan Soo Siang Yang Kenneth Tze Kin Teo

Swarm Robotics

10.5013/ijssst.a.21.03.06

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Impact of Genetic Operators on Energy-Efficient Wireless Sensor Network

2019 IEEE 6th International Conference on Engineering Technologies and Applied Sciences (ICETAS) (2019)

Vincent Chung Norah Tuah Kit Guan Lim Min Keng Tan Hui Huang

The metaheuristic genetic algorithm is an evolutionary algorithm which means that it will always evolve to get an optimum solution. Due to this intrinsic characteristic, the conventional genetic algorithm might be trapped at local optimum when dealing with a global optimization problem that consists of several maximum points. As such, this paper aims to explore the potential of improving the genetic algorithm by manipulating its operators. With different procedures in selection and replacement operators, the genetic algorithm is be able to compute more efficiently. This mechanism is introduced to prevent the proposed algorithm to be trapped at the local optimum point with shorter computation time. The robustness of the proposed algorithm is tested in optimizing a wireless sensor network (WSN) because the WSN will exhibit multiple peaks with different network configuration. The existence of multiple peaks will lead to additional difficulties for the conventional routing protocol algorithm in tracking the global optimum network configuration or known as global optima. The simulation results show the effect of proposed genetic algorithm with different combinations of operators.

Robustness

Local optimum

10.1109/icetas48360.2019.9117419

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Citations (1)

Metaheuristic Multi-Hop Clustering Optimization for Energy-Efficient Wireless Sensor Network

Vincent Chung Norah Tuah Kit Guan Lim Min Keng Tan Ismail Saad

Energy-efficient optimization algorithm in wireless sensor network (WSN) is often based on solely cluster routing or multi-hop routing. The cluster optimization algorithm will form a cluster network by dividing the sensor nodes into few clusters where each cluster has a cluster head (CH) for data collection. On the other hand, multi-hop optimization algorithm will form a multi-hop network by transmitting data to base station (BS) through data multi-hopping between sensor nodes. However, cluster optimization algorithm suffers from the overburdens of CH nodes, while multi-hop optimization algorithm suffers from the overburdens of nodes which are near to the BS. Therefore, Genetic Algorithm-Cuckoo Search (GACS) is proposed and developed based on the multi-hop clustering model in this paper. GACS optimizes both intra-cluster and inter-cluster communications to enhance energy efficiency in WSN, extending the network lifetime. Based on the performance evaluation, GACS outperforms both Genetic Algorithm (GA)-based cluster optimization algorithm and Cuckoo Search (CS)-based multi-hop optimization algorithm.

Cuckoo search

10.1109/iicaiet49801.2020.9257871

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Citations (5)

Research on Risk Detection of Autonomous Vehicle Based on Rapidly-Exploring Random Tree

Computation (2023)

Yincong Ma Kit Guan Lim Min Keng Tan Helen Sin Ee Chuo Ali Farzamnia

There is no doubt that the autonomous vehicle is an important developing direction of the auto industry, and, thus, more and more scholars are paying attention to doing more research in this field. Since path planning plays a key role in the operation of autonomous vehicles, scholars attach great importance to this field. Although it has been applied in many fields, there are still some problems, such as low efficiency of path planning and collision risk during driving. In order to solve these problems, an automotive vehicle-based rapid exploration random tree (AV-RRT)-based non-particle path planning method for autonomous vehicles is proposed. On the premise of ensuring safety and meeting the requirements of the vehicle’s kinematic constraints through the expansion of obstacles, the dynamic step size is used for random tree growth. A non-particle collision detection (NPCD) collision detection algorithm and path modification (PM) path modification strategy are proposed for the collision risk in the turning process, and geometric constraints are used to represent the possible security threats, so as to improve the efficiency and safety of vehicle global path driving and to provide reference for the research of driverless vehicles.

Random tree

Tree (set theory)

10.3390/computation11030061

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Citations (5)

Local Traffic Network Formulation and Signalisation via Benchmark Webster Model

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) (2022)

Helen Sin Ee Chuo Min Keng Tan Kit Guan Lim Lorita Angeline Tienlei Wang

Traffic models have been widely studied for general traffic understanding and transportation variable relations, but unreadily exert for real time transportation decision and planning. With the present advancement in computer technology, the used-to-be-lengthy and complicated modelling technique is now more computable and executable than ever for putting into good use. This paper discusses extensively on the formulation of local traffic network in Kota Kinabalu, Malaysia and the signal settings as referred to the Jabatan Kerja Raya (JKR) using benchmark Webster Model. This paper is crucial for bridging the current traffic network modelling towards advanced modelling techniques and transportation network optimisation strategies to be incorporated in intelligent transportation systems. The traffic responses and results from the Webster model simulation and signalization were reported in this paper as a realistic reference under different case studies including normal traffic and congested traffic scenarios, besides when traffic delays occurred.

Benchmark (surveying)

Executable

Traffic network

Network traffic simulation

Network model

10.1109/iicaiet55139.2022.9936766

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Citations (1)