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AI Based Static Security Assessment of Power System

N. Aishwarya *, Shekhappa G. Ankaliki **

*-** Department of Electrical and Electronics Engineering, SDM college of Engineering and Technology, Dharwad, Karnataka, India.

Periodicity:May - July'2020
DOI : https://doi.org/10.26634/jps.8.2.17629

Abstract

Contingency selection and ranking using fast and accurate method has become a main issue to ensure the secured operation of power systems. This work proposes multi-layer feed forward artificial neural network (MLFFN) and radial basis function network (RBFN) to demonstrate the static security assessment. In this, the composite security index which is capable of accurately differentiating the secure and insecure cases of power system based on contingency selection, and ranking are done. For each contingency in power system, the composite security index is computed using conventional Newton-Raphson load flow analysis. The MLFFN and RBFN takes all possible loading conditions and probable contingencies as input and assess the system security by screening the credible contingencies and ranking them in the order of severity based on composite security index. The proposed approach is demonstrated by considering the IEEE 30 Bus system using MATLAB simulation.

Keywords

Security Index, Contingency Screening and Ranking, Multi-Layer Feed Forward Neural Network, Static Security Assessment, Radial Basis Function Network.

How to Cite this Article?

Aishwarya, N., and Ankaliki, S. G. (2020). AI Based Static Security Assessment of Power System. i-manager's Journal on Power Systems Engineering, 8(2), 16-23. https://doi.org/10.26634/jps.8.2.17629

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