Generator coherency determination in a smart grid using artificial neural network

Smart grid is a concept of modern power grid that an intelligently integrates all supply, grid and demand elements connected to it in order to efficiently deliver sustainable, economic and secure electricity supplies. Power system security is one of the major objectives of the Smart Grid. This paper presents an artificial neural network based approach for fast and accurate assessment of transient security status and generator coherency. Radial basis function (RBF) neural network is employed to obtain the objectives for a given operating condition. The methodology can serve as decision making tool for the power planners to take preventive control actions for generation shedding/rescheduling for online applications. A feature selection technique based on the correlation coefficient has been employed. The effectiveness of the proposed methodology is demonstrated by overall accuracy of the test results for unknown patterns for IEEE 39-bus New England system.