A hybrid method of GA and BP for short-term economic dispatch of hydrothermal power systems
Mathematics and Computers in Simulation - Special issue from the IMACS/IFAC international symposium on soft computing methods and applications: “SOFTCOM '99” (held in Athens, Greece)
Neural Networks in Computer Intelligence
Neural Networks in Computer Intelligence
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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.