CNN based power system transient stability margin and voltage stability index prediction

Abstract

Operators at electric grid control centers are faced with the task of making important decisions in real-time. With the plethora of data available it becomes important to extract information from the available data, based on which knowledge of system condition can be formed. This knowledge can then be used in decision making. Metrics such as transient stability margin (TSM) and voltage stability load index (VSLI) help in assessing the stability of the system. In this study, cellular neural network (CNN) based stability margin prediction system is developed in a distributed computing framework. The developed system not only extracts information from available data but also predicts the same, one step ahead of time. Moreover, the framework employed uses distributed computing and hence could be used on a large scale power system with a linear increase in computation time instead of an exponential increase. A reduced version of New Zealand's South Island power system is used as the test system to demonstrate the feasibility of CNNs for TSM and VSLI prediction.Index Terms-Cellular neural networks, dynamic security assessment, real-time monitoring, smart grid, transient stability margin, voltage stability load index.