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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Jian-Ding Tan
Universiti Tenaga Nasional
Electrical & Electronics Engineering

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Corona fault detection in switchgear with extreme learning machine Sanuri Ishak; Siaw-Paw Koh; Jian-Ding Tan; Sieh-Kiong Tiong; Chai-Phing Chen
Bulletin of Electrical Engineering and Informatics Vol 9, No 2: April 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (244.912 KB) | DOI: 10.11591/eei.v9i2.2058

Abstract

Switchgear is a very important component in a power distribution line. Failure in a switchgear can lead to catastrophic danger and losses. In this research, a fault detection system is proposed with the implementation of Extreme Learning Machine (ELM). This algorithm is capable to identify faults in a switchgear by analyzing the sound wave generated. Experiments are carried out to investigate the performance of the developed algorithm in identifying Corona faults in switchgears. The performances are analyzed in time and frequency domains, respectively. In time domain analysis, the results show 90.63%, 87.5%, and 87.5% of success rates in differentiating the Corona and non-Corona cases in training, validation and testing phases respectively. In frequency domain analysis, the results show 89.84%, 83.33%, and 87.5% success rates in training, validation and testing phases respectively. It can thus be concluded that the developed algorithm performed well in identifying Corona faults in switchgears.
A heat waste recovery system via thermoelectric generator Chai-Phing Chen; Siaw-Paw Koh; Sieh-Kiong Tiong; Jian-Ding Tan; Albert Yu-Chooi Fong
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 2: November 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i2.pp586-590

Abstract

Be it in the power production or consumption end, improvement on the power efficiency has become one of the most pivoting research topics over the past few decades. In order to reduce the reliance on fossil fuels and negative impacts on the environment, many ways are found to show promising results to increase power efficiency. One of the most effective ways is to recover and reuse heat waste. In this research, a heat waste recovery system is proposed by using thermoelectric generators (TEGs). This proposed heat recovery system can be implemented at the exhaust or the chiller section of a power system to abstract the excessive and unwanted heat and reuse it before it dissipates into the environment or goes to waste. Experiments are setup and conducted with controlled heat levels to investigate the performance of the proposed system in converting heat waste into electricity under different temperatures. The results show that the generated power hikes as the heat set-points increase from 30°C to 240°C. The output power fluctuates and shows no significant increase as the temperature increases from 240°C onwards. The maximum power is generated at 290°C. It can thus be concluded that the proposed system successfully generates electricity under different level of heat waste temperature. In time to come, this research can further explore the possibility on the optimization of the generated power.
An adaptive gravitational search algorithm for global optimization Ying-Ying Koay; Jian-Ding Tan; Chin-Wai Lim; Siaw-Paw Koh; Sieh-Kiong Tiong; Kharudin Ali
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 2: November 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i2.pp724-729

Abstract

Optimization algorithm has become one of the most studied branches in the fields of artificial intelligent and soft computing. Many powerful optimization algorithms with global search ability can be found in the literature. Gravitational Search Algorithm (GSA) is one of the relatively new population-based optimization algorithms. In this research, an Adaptive Gravitational Search Algorithm (AGSA) is proposed. The AGSA is enhanced with an adaptive search step local search mechanism. The adaptive search step begins the search with relatively larger step size, and automatically fine-tunes the step size as iterations go. This enhancement grants the algorithm a more powerful exploitation ability, which in turn grants solutions with higher accuracies. The proposed AGSA was tested in a test suit with several well-established optimization test functions. The results showed that the proposed AGSA out-performed other algorithms such as conventional GSA and Genetic Algorithm in the benchmarking of speed and accuracy. It can thus be concluded that the proposed AGSA performs well in solving local and global optimization problems. Applications of the AGSA to solve practical engineering optimization problems can be considered in the future.
Co-Authors Albert Yu-Chooi Fong Chai-Phing Chen Chin-Wai Lim Kharudin Ali Sanuri Ishak Siaw-Paw Koh Sieh-Kiong Tiong Ying-Ying Koay