Security assessment and enhancement using RBFNN with feature selection

Abstract

Secure operation of the power system in real time requires assessment of rapidly changing system conditions. Traditional security evaluation method involves running full load flow for each contingency, making it infeasible for real time application. This paper presents Radial Basis Function Neural Network (RBFNN) approach with feature selection for static security assessment and enhancement. The security of the system is assessed based on the intensity of contingencies. The necessary corrective control action to be taken in the event of insecure state is also proposed and the effect of this action has also been observed in order to enhance the security. RBFNN improves the response time compared to other neural networks. Feature selection of the input patterns is done to reduce the dimensionality to a large extent, maintaining the classification accuracy. This method is illustrated using New England 39 bus system.

Semi-wrapper feature subset selector for feed-forward neural networks: Applications to binary and multi-class classification problems

“…Probably, the work more connected to the sketch of the current paper could be one that utilised MLP and RBFNN to train only a concrete problem related with the security containing samples from four classes with a number of properties in the most difficult case around one hundred [32] ; the contribution is based on the usage of a sequential forward selection to extract the relevant features and also is important to remark that the search starts with an empty candidate set and adds feature variables sequentially until the halt. Nonetheless, the forthcoming pages talk about the new contribution that we propose and, of course, the experiments to evaluate our proposal.…”