Synchrophasor data driven islanding detection strategies and a defensive scheme for microgrids

Abstract

The horizon of microgrid operations has increased with advancements in newlinecommunication protocols, sensor technologies and huge deployment of non dispatchable newlinerenewable energy sources (NDRES). With the upsurge in microgrids and its ability to newlineperform dual functionalities, islanding detection has become critical and inevitable. newlineIslands assumed to be disconnected from all energy sources are a safety threat for newlineoperators. Absence of proper voltage and frequency regulating equipments in the formed newlineisland may give rise to power quality problems, large surge currents upon reconnection newlinecausing damage to utility equipment, DG and customer loads. Thus, it is imperative for a newlinemicrogrid to have a reliable, precise and agile islanding detection algorithm to detect an newlineunintentional islanding event (IE). newlineMajor deficiencies of the existing islanding detection methods (IDMs) are nuisance newlinetripping and increased non-detection zone (NDZ). Synchrophasor based islanding newlinedetection is one of the remote IDMs that offers fast, reliable, and accurate islanding newlinedetection under different operating conditions. Data in the form of voltage, current newlinephasors, frequency and rate of change of frequency can be transformed to useful newlineinformation that can help operators in taking an unambiguous decision. However, newlineintensive data from phasor measurement units (PMUs) and micro-PMUs pose a challenge newlineto process the data and distinguish IEs from the embedded dynamics of the system. newlinePrevious research using multivariate statistical techniques like static principal component newlineanalysis of dynamically correlated data had unnecessarily high missed detection and false newlinealarm rates. In this research work, initially, a passive IDM for a NDRES intensive newlinemicrogrid enabled with PMUs is proposed. Here, a data compressing moving window newlineprincipal component analysis (MWPCA) is performed along with extended mathematical newlinemorphology for islanding detection using multiple system variables from various newlinelocations for the first time. newline