Sukhairi Sudin
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Preferred name
Sukhairi Sudin
Official Name
Sukhairi, Sudin
Alternative Name
Sudin, S.
Main Affiliation
Scopus Author ID
56572705200
Researcher ID
GFW-2221-2022

Research Output

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Publication

Automated Negative Lightning Return Strokes Characterization Using Brute-Force Search Algorithm

2022-04-01 , Haris F.A. , Kadir M.Z.A.A. , Sukhairi Sudin , Jasni J. , Johari D. , Zaini N.H.

Over the years, many studies have been conducted to measure, analyze, and characterize the lightning electric field waveform for a better conception of the lightning phenomenon. Moreover, the characterization mainly on the negative return strokes also significantly contributed to the development of the lightning detection system. Those studies mostly performed the characterization using a conventional method based on manual observations. Nevertheless, this method could compromise the accuracy of data analysis due to human error. Moreover, a longer processing time would be required to analyze data, especially for larger sample sizes. Hence, this study proposed the development of an automated negative lightning return strokes characterization using a brute-force search algorithm. A total of 170 lightning electric field waveforms were characterized automatically using the proposed algorithm. The manual and automated data were compared by evaluating their percentage difference, arithmetic mean (AM), and standard deviation (SD). The statistical analysis showed a good agreement between the manual and automated data with a percentage difference of 1.19% to 4.82%. The results showed that the proposed algorithm could provide an efficient structure and procedure by reducing the processing time and minimizing human error. Non-uniformity among users during negative lightning return strokes characterization can also be eliminated.

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Publication

Measurement of Negative Lightning Return Strokes Using a Proposed Small-Scale Parallel Plate Antenna at the Central Region in Peninsular Malaysia

2021-12-01 , Haris F.A. , Ab Kadir M.Z.A. , Sukhairi Sudin , Johari D. , Hamzah M.N.

Lightning can occur between the clouds (intra-clouds), ground-to-cloud (CG), and inside the clouds. The lightning strike hazards can be managed appropriately using a lightning detector system consisting of an antenna, buffer amplifier circuit, and a measurement device. In this study, the development and fabrication of small-scale parallel plate antenna were carried out by reducing the physical height and antenna dimension to measure the generated electric fields. The experimental work was conducted at the rooftop of the College of Engineering, Universiti Tenaga Nasional (UNITEN), Selangor, Malaysia, from August 2019 to March 2020. The total number of 115 return strokes (RS) of negative lightning events were recorded during the measurement period. Characterization of seven types of criteria for negative return stroke has been analysed and compared to the existing parallel plate antenna with a similar climate condition and different countries. Based on the comparative study, the proposed and the existing parallel plate antenna shows a good agreement. Hence, the proposed small-scale parallel plate antenna can be used as a portable, lightweight, and easy to install device for the lightning measurement system.

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Publication

Automated Negative Lightning Return Strokes Classification System

2021-12-01 , Haris F.A. , Ab Kadir M.Z.A. , Sukhairi Sudin , Johari D. , Jasni J. , Noor S.Z.M.

Over the years, many studies have been conducted to measure and classify the lightning-generated electric field waveform for a better understanding of the lightning physics phenomenon. Through measurement and classification, the features of the negative lightning return strokes can be accessed and analysed. In most studies, the classification of negative lightning return strokes was performed using a conventional approach based on manual visual inspection. Nevertheless, this traditional method could compromise the accuracy of data analysis due to human error, which also required a longer processing time. Hence, this study developed an automated negative lightning return strokes classification system using MATLAB software. In this study, a total of 115 return strokes was recorded and classified automatically by using the developed system. The data comparison with the Tenaga Nasional Berhad Research (TNBR) lightning report showed a good agreement between the lightning signal detected from this study with those signals recorded from the report. Apart from that, the developed automated system was successfully classified the negative lightning return strokes which this parameter was also illustrated on Graphic User Interface (GUI). Thus, the proposed automatic system could offer a practical and reliable approach by reducing human error and the processing time while classifying the negative lightning return strokes.

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