Fast and Effective Technique in Evaluation of Lightning Impulse Voltage Parameters
| dc.contributor.author | Yutthagowith, Peerawut | |
| dc.contributor.author | Kitwattana, Krit | |
| dc.contributor.author | Kunakorn, Anantawat | |
| dc.date.accessioned | 2026-08-06T10:31:42Z | |
| dc.date.available | 2026-08-06T10:31:42Z | |
| dc.date.issued | 2021-01-01 | |
| dc.description.abstract | This paper presents an approach for the waveform parameter evaluation of lightning impulse voltage in high voltage tests according to the IEC standards. Such waveform parameters are composed of peak voltage (U<inf>p</inf>), front time (T<inf>1</inf>), time to half (T<inf>2</inf>), and the overshoot rate (B<inf>e</inf>). An artificial neural network with a back-propagation learning algorithm was applied to determine a base curve and its parameters from 14 points along the recorded waveform between 20% of the peak voltage on the wave front part to 40% of the peak voltage on the wave tail part. The 29 waveforms recommended by the standard were used in the training process of the development of the network model, and some experimental cases were also utilized for verification of the proposed method. It is found that the waveform parameters evaluated by the proposed approach are in the tolerances of the standard requirements. Maximum absolute deviations of U<inf>p</inf>, T<inf>1</inf>, T<inf>2</inf>, and B<inf>e</inf> are 0.06%, 2.00%, 0.12%, and 0.79%, respectively. Due to that no iteration process in the proposed approach is required, the efficiency in calculation process is significantly faster than the standard recommended approach. | |
| dc.identifier.citation | Journal of Electrical Engineering and Technology, 16(1), 459-467, 2021 | |
| dc.identifier.doi | 10.1007/s42835-020-00555-x | |
| dc.identifier.issn | 19750102 | |
| dc.identifier.other | 2-s2.0-85091824469 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/11792 | |
| dc.source | Journal of Electrical Engineering and Technology | |
| dc.subject | Artificial neural network | |
| dc.subject | Curve fitting | |
| dc.subject | Evaluation | |
| dc.subject | Lightning impulse voltage | |
| dc.subject | Waveform parameters | |
| dc.title | Fast and Effective Technique in Evaluation of Lightning Impulse Voltage Parameters | |
| dc.type | Article |