Automated negative lightning return strokes classification system

• Main Authors: Abdul Haris, Faranadia, Ab. Kadir, Mohd. Zainal Abidin, Sudin, Sukhairi, Johari, Dalina, Jasni, Jasronita, Mohammad Noor, Siti Zaliha
• Format: Article
• Published: IOP Publishing 2021
• Online Access: http://psasir.upm.edu.my/id/eprint/96168/; https://iopscience.iop.org/article/10.1088/1742-6596/2107/1/012022
• Institution: Universiti Putra Malaysia

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.