Surface discharge analysis of high voltage glass insulator using ultraviolet pulse voltage
Contaminated and aged transmission line insulators are susceptible to flashover during service, due to temporary or permanent loss of their insulating properties, resulting in power system failure. Surface discharges are precursors to flashovers. To pre-empt the occurrence of flashover incidents, ut...
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| 主要作者: | |
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| 格式: | Thesis |
| 語言: | English |
| 出版: |
2017
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| 主題: | |
| 在線閱讀: | http://eprints.utm.my/id/eprint/93146/1/SaifulMohammadIezhamMSKE2017.pdf |
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| 總結: | Contaminated and aged transmission line insulators are susceptible to flashover during service, due to temporary or permanent loss of their insulating properties, resulting in power system failure. Surface discharges are precursors to flashovers. To pre-empt the occurrence of flashover incidents, utility companies need to regularly monitor the condition of line insulators. Recent studies have shown that monitoring of ultra-violet (UV) signals emitted by surface discharges of the insulators is a promising technique. This study presents a method of detecting impairment on contaminated and aged insulators during surface discharge activities, by using UV pulse voltage method. For verification, time and frequency domain of the UV signals for a group of insulator samples with varying contamination levels and degree of ageing have been analysed. Experimental result shows that a strong correlation exists between the frequency components of the UV signals and discharge intensity levels under varying contamination levels and degree of ageing. As the contamination levels increases, the discharge levels of the insulator samples also intensifies, resulting in the increase of total harmonic distortion (THD) and fundamental frequency of the UV signals. Frequency components of the UV signals have been employed by using MATLAB simulation to develop a technique based on artificial neural network (ANN) to classify the flashover prediction based on the discharge intensity levels of the insulator samples. The results of the ANN simulation show 87% accuracy in the performance index. This study illustrates that UV pulse detection method is a potential tool to monitor insulator surface conditions during service. |
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