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Abstract
Overvoltages are the main causes of damages and accidents in electric power grids. The traditional approach is to install some static protection devices that are passive and cannot identify the overvoltage types or locate where the overvoltage event occurs. In recent years, with the development of smart grids, some online overvoltage monitoring systems have been developed. However, the approaches to data processing still need further development. A novel technique for identification and location of overvoltages in power distribution systems is proposed, which uses capacitor bank energization overvoltages (CBOVs) and ground fault temporary overvoltages (TOVs) as the study cases. The wavelet packet decomposition (WPD) theory is used for frequency band decomposition, and a general regression neural network (GRNN) is used in identification and location. Simulation results based on real-world power distribution systems show that the method is accurate and fast.
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