Abstract
Real-time operation of electric power systems requires monitoring and control on a continuous basis. This paper proposes Machine Learning (ML) based Prediction model for assessment and enhancement of static security. The objective of the model is to assess the security state of the current operating point by predicting Static Security Index (SSI). If the system is insecure or critically secure, generators are re-dispatched based on Relative Electrical Distance (RED) to bring back the system to secure state. The re-dispatch of generators is defined by a control vector, denoted as Corrective Generation Schedule (CGS). Most suitable Machine Learning based classifier is identified to make the necessary predictions of the security state and the required corrective action. Correlation-based Feature Subset Selection (CFS) is utilized to improve the classification process. The prediction statistics obtained for IEEE 39-bus New England system clearly indicate effectiveness of the proposed Machine Learning based model.
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Srilatha, N., Priyanka Rathod, B., Yesuratnam, G. (2020). Machine Learning Based Prediction Model for Monitoring Power System Security. In: Satapathy, S.C., Raju, K.S., Shyamala, K., Krishna, D.R., Favorskaya, M.N. (eds) Advances in Decision Sciences, Image Processing, Security and Computer Vision. ICETE 2019. Learning and Analytics in Intelligent Systems, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-030-24318-0_53
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DOI: https://doi.org/10.1007/978-3-030-24318-0_53
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