The key issues in security assessment are fast identification of insecure contingencies and evaluation of their impact on power system operation. As such, an artificial neural network (ANN) aided method for security assessment is proposed and illustrated for a model six-bus power system. Using 15 different line-loading patterns for training, the network successfully classifies the unknown loading patterns. In particular, this powerful and versatile feature is especially useful for power system operation.

Shanti K. Swarup

Department of Electrical Engineering

P. Britto Corthis

Busbars

Computer simulation

Database systems

Electric load management

Electric power distribution

Electric power system interconnection

Electric power system protection

Mathematical models

Neural Networks

KOHONEN SELF ORGANIZING FEATURE MAP

Network topology

security assessment

SECURITY INDEX

SIX BUS POWER SYSTEM

Security systems

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

ANN approach assesses system security

IEEE Computer Applications in Power

Artificial neural networks using pattern recognition methodology for security assessment of electric power systems is presented. Conventional numerical methods are either too complex or time consuming. An alternative method using neural networks to address the security assessment problem and its effectiveness against conventional methods is discussed. Neural networks using pattern recognition techniques is a promising methodology for different types of security assessment. Feature selection and extraction are used for selecting best features having highest discriminating capabilities. An important feature of the approach is that it can be generalized for steady state, transient and dynamic security assessment, which is a desirable feature for on-line security analysis. The proposed approach has been tested on the WSCC 9-bus 3-generator system. Steady state, transient and dynamic security assessment classification and contingency ranking results are provided to highlight the overall classification accuracy and suitability of the approach. © 2007 Elsevier B.V. All rights reserved.

Neurocomputing

Artificial neural network using pattern recognition for security assessment and analysis

IEEE Transactions on Power Systems

Power system static security assessment using the Kohonen neural network classifier

IEE Proceedings C Generation, Transmission and Distribution

Towards static-security assessment of a large-scale power system using neural networks

Journal	IEEE Computer Applications in Power
ISSN	08950156
Open Access	No