Online Static Security Assessment Module Using Artificial Neural Networks

• Published in: IEEE transactions on power systems 2013-11, Vol.28 (4), p.4328-4335
• Main Authors: R, Sunitha, Kumar, Sreerama Kumar, Mathew, Abraham T.
• Format: Article
• Language: English
• Subjects: Composite security index; Computational modeling; contingency screening and ranking; Indexes; Load modeling; Loading; multi-layer feed forward neural network; Neural networks; online static security assessment; Power systems; radial basis function network; Security
• ISSN: 0885-8950; 1558-0679
• DOI: 10.1109/TPWRS.2013.2267557

Fast and accurate contingency selection and ranking method has become a key issue to ensure the secure operation of power systems. In this paper multi-layer feed forward artificial neural network (MLFFN) and radial basis function network (RBFN) are proposed to implement the online module for power system static security assessment. The security classification, contingency selection and ranking are done based on the composite security index which is capable of accurately differentiating the secure and non-secure cases. For each contingency case as well as for base case condition, the composite security index is computed using the full Newton Raphson load flow analysis. The proposed artificial neural network (ANN) models take loading condition and the probable contingencies as the 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 numerical results of applying the proposed approach to IEEE 118-bus test system demonstrate its effectiveness for online power system static security assessment. The comparison of the ANN models with the model based on Newton Raphson load flow analysis in terms of accuracy and computational speed indicate that the proposed model is effective and reliable in the fast evaluation of the security level of power systems. The proposed online static security assessment (OSSA) module realized using the ANN models are found to be suited for online application.