Md. Abul Kalam

University of Technology Sydney, School of Civil & Environmental Engineering, Faculty Member

University of Malaya, Malaysia, Department of Mechanical Engineering, Faculty Member

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Dr Md. Abul Kalam is currently working as an academic staff in the Faculty of Engineering and IT at the University of Technology Sydney (UTS), Australia. Previously he worked as an academic staff in the Department of Mechanical Engineering at Universiti Malaya from 2008 to 2021. His research specialization is in Internal-combustion engines, Biodiesel/Biofuel, Lubricant testing, and Engine Tribology. Prof Kalam obtained his undergraduate degree from Bangladesh Institute of Technology (B.I.T) Khulna, presently known as Khulna University of Engineering & Technology (KUET), and Master & PhD from Universiti Malaya, Malaysia. Recently, Prof Kalam’s name appeared in the list of “Highly Cited Researchers” in the years 2017, 2018, 2019, 2020, and 2021” in recognition of ranking among the top 1% of researchers for the most cited documents in engineering by Web of Science and Clarivate Analytics.
Furthermore, he received ‘Universiti Malaya's Excellence Award in 2017’ for outstanding achievement in the category of “Academic Staff with Most Highly Cited Paper (Sciences), as well as the Excellence Service Award in 2018. In addition, he received Malaysia's research star award in the years 2018 and 2019. Currently, he has been placed in a research centre named "The School of Civil and Environmental Engineering at UTS, Sydney, Australia.
Phone: 0061480345546
Address: School of Civil and Environmental Engineering
Faculty of Engineering and IT
University of Technology Sydney
NSW 2007, Australia

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Wavelet-FFNN based Fault Location Estimation of a Transmission Line

In order to maintain the quality power supply and reliable operation of power delivery service to... more In order to maintain the quality power supply and reliable operation of power delivery service to the utilities with minimum interruption, it is extremely important that the transmission line faults need to be detected and located in a reliable and accurate manner. To meet these requirement an approach based on discrete wavelet transform using neural network (NN) is proposed to estimate the fault location of the three phase transmission line and the results have been compared with the conventional method (measuring the sequence components of voltage and current). The proposed approach is based on the measurements of the three phase fault current signals at one terminal of the transmission line (at relaying point), created for different fault operating conditions using MATLAB software. To accomplish the task, features of these measured signals are extracted in terms of summation of ninth level detail coefficients using multiresolution analysis (MRA) based on discrete wavelet transform, selecting daubechies4 (db4) as mother wavelet at 100 kHz sampling frequency. These features are then used as an input to train and test the feed forward neural network (FFNN) to provide fault location from the relaying point on the transmission line. The NN estimates the location of the fault with a significant reduction in percentage error compared to the conventional method of fault location for wide variations of operating condition. Hence, the use of multi-resolution analysis (MRA) based on discrete wavelet transform in combination with NN is quite promising for fault location estimation of the transmission line.

Md. Abul Kalam

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