ABSTRACT
Energy Management Systems have found widespread application with the growing complexity of the power grid. EMS includes control of a variety of processes that generate huge data of the parameters, namely, voltage, power flow, current, power factor, and frequency, acquired from remote locations. Of these, voltage is directly connected to the stability of the grid. Sudden voltage drops of larger parts of the system may cause outages. Thus, the prediction of probable voltage sags and swells in the system becomes an indispensable requisite for system stability. With such voluminous amount of data generated at high velocity, voltage prediction is a perfect candidate for Big Data Analytics. There are various Big Data tools being put to use; Of them all, R is a powerful programming language along with its R Studio environment as a framework for statistical computing. This discussion throws light on analytics for voltage prediction of various buses in an IEEE standard power system, using R programming. The analysis is done as an academic research study with data, obtained from power flow analysis of the system under study. The data procured is subjected to analytical algorithms implemented using R framework to predict the load bus voltages of the system.
