Study of Neural Network Models for Security Assessment in Power Systems

اسناد مشابه

• Comparative Study of Backpropagation Algorithms in Neural Network Based Identification of Power System

  سند علمی۲۰۱۳Sheela Tiwari, Ram Naresh, Rameshwar Jha

  This paper explores the application of artificial neural networks for online identification of a multimachine power system. A recurrent neural network has been proposed as the identifier of the two area, four machine system which is a benchmark system for studying electromechanic

• Neural Network Predictive Control Based Power System Stabilizer

  سند علمی۲۰۱۴Ali Mohamed, Yousef Ali

  The present paper investigates the power system stabilizer based on neural predictive control for improving power system dynamic performance over a wide range of operating conditions. In this study a design and application of the neural network model predictive controller (NN-MPC

• An Evolving Fuzzy Neural Network Model Based Reactive Power Control

  سند علمی۲۰۰۰Ajith Abraham

  In this paper, we present an Evolving FUzzy-Neural Network (EFuNN) model for intelligent reactive power control and efficient utilization of power. The proposed EFuNN model will assist the conventional power control systems with added intelligence. For on-line control, voltage an

• Using Genetic Algorithm Based Variable Selection to Improve Neural Network Models for Real-World Systems

  سند علمی۲۰۰۲ICMLADonald Sofge

  Real-world systems are often modeled by sampling sensor data taken during system operation. System states may not be all known or measurable, sensor data may be biased or noisy, and it is not often known which sensor data may be useful for predictive modeling. Neural network mode

• Design of Adaptive Neural Network Controller for Thermal Power System Frequency Control

  سند علمی۲۰۱۲Ognjen Kuljača, Krunoslav Horvat, Bruno Borović

  This paper deals with analytical and simulation approach for choice of activation functions and instead of a number of nodes for a class of neural network controllers for frequency control of thermal power systems. Neural network update laws are derived via Lyapunov like stabilit

• Reachable Set Estimation and Verification for Neural Network Models of Nonlinear Dynamic Systems

  سند علمی۲۰۱۸CoRRWeiming Xiang, Diego Manzanas Lopez, Patrick Musau, Taylor T. Johnson

  Neural networks have been widely used to solve complex realworld problems. Due to the complicate, nonlinear, non-convex nature of neural networks, formal safety guarantees for the behaviors of neural network systems will be crucial for their applications in safety-critical system