Title: On-line security monitoring and analysis using Levenberg-Marquardt algorithm-based Neural Network
Authors: Seema N. Pandey, Shashikala Tapaswi, Laxmi Srivastava
Addresses: Information Technology Department, ABV-Indian Institute of Information Technology and Management, Gwalior 474010, India. ' Information Technology Department, ABV-Indian Institute of Information Technology and Management, Gwalior 474010, India. ' Electrical Engineering Department, Madhav Institute of Technology and Science, Gwalior 474005, India
Abstract: Due to open access in the restructured power system, the events of bus voltage limit violation and transmission line overloading are occurring frequently. These events are mainly responsible for several incidents of major network collapses leading to partial or even complete blackouts and due to this, security monitoring and analysis has become a challenging task to be performed at energy control centre. A fast and accurate method of Power Flow (PF) study may be able to investigate the system security by determining the power system static states, i.e. voltage magnitude and voltage angle at each bus. In this paper, a Levenberg-Marquardt algorithm-based Neural Network (LMNN) has been proposed which provides a fast learning to the multi-layer neural network. The effectiveness of the proposed LMNN-based approach for security monitoring and analysis has been demonstrated by computation of bus voltage magnitudes and voltage angles for line-outage contingencies at different loading conditions in IEEE 14-bus system.
Keywords: admittance matrix; Levenberg-Marquardt algorithm; line-outage contingency; multi-layer perceptron model; power flow analysis; security monitoring; security analysis; neural networks; blackouts; open access; restructured power systems; bus voltage limit violation; transmission line overloading.
International Journal of Intelligent Systems Technologies and Applications, 2009 Vol.6 No.1/2, pp.77 - 88
Published online: 25 Jan 2009 *Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article