دانلود رایگان مقاله انگلیسی تشخیص خطای امپدانس بالا مبتنی بر داده کاوی با استفاده از مورفولوژی ریاضی - الزویر 2018

ABSTRACT

High impedance fault (HIF) detection is a challenging task in power system protection because of the random nature of current. HIFs are not efficiently detected by conventional protection systems because of their low current magnitudes. The proposed method presents an intelligent HIF protection technique using Mathematical Morphology (MM) and a data mining-based Decision Tree (DT) model. The current signals are produced by a MATLAB / SIMULINK model of an actual distribution system with real data. The features of these current signals are computed after processing with MM filter. A data mining-based DT model is then generated using these features of the current signals, and this DT model makes a final decision on classification into HIF and non-HIF. The proposed scheme is tested on different HIF and non-HIF cases and the results were found to be encouraging.

10. Conclusions

A simple and novel technique for high impedance fault detection method has been presented using the current signal with a low sampling rate of 3 KHz. The effectiveness of the proposed detection scheme has been verified on an actual distribution system under different operating conditions and the results show an improved success rate with a detection delay of 30.66 ms. The proposed method may be improved and extended by considering other comparable signals which could be similar to HIF. Also, the fast-growing distributed generations force to improve the proposed work. So, the future goal of this research is to test the proposed scheme under distributed generations, including other kinds of non-HIF events.