Title :
Quickest detection of unknown power quality events for smart grids
Author :
Xingze He ; Man-On Pun ; Kuo, C.-C Jay
Author_Institution :
Ming Hsieh Dept. of Electr. Eng., Univ. of Southern California, Los Angeles, CA, USA
Abstract :
In this work, we study a change-point approach to provide the quickest detection of power quality (PQ) event occurrence for smart grids. Despite that both the occurrence time and the PQ event type are unknown beforehand, knowledge of the statistics of post-PQ event signals is required to implement the change-point approach. To circumvent this obstacle, we propose to model the unknown PQ events using different statistical distributions, namely the Gaussian, Gamma and inverse Gamma distributions. It is shown by computer simulation that all distributions under consideration can provide accurate PQ event detection. In particular, the inverse Gamma distribution demonstrates the most promising performance in our simulation.
Keywords :
Gaussian distribution; gamma distribution; power supply quality; smart power grids; Gaussian distribution; computer simulation; inverse Gamma distribution; post-PQ event signal statistics; quickest detection; smart grid; statistical distribution; unknown power quality events; Simulation; Change-point detection theory; Power quality (PQ); cumulative sum (CUSUM) algorithm;
Conference_Titel :
Signal & Information Processing Association Annual Summit and Conference (APSIPA ASC), 2012 Asia-Pacific
Conference_Location :
Hollywood, CA
Print_ISBN :
978-1-4673-4863-8