Linear robust control
Neural network design
Year 2000 Solutions for Dummies
Year 2000 Solutions for Dummies
Experimental Studies of a Generalized Neuron Based Adaptive Power System Stabilizer
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Comparison of Adaptive Critic-Based and Classical Wide-Area Controllers for Power Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Levenberg-Marquardt (LM) algorithm, a powerful off-line batch training method for neural networks, is adapted here for online estimation of power system dynamic behavior. A special form of neural network compatible with the feedback linearization framework is used to enable non-linear self-tuning control. Use of LM is shown to yield better closed-loop performance compared to conventional recursive least square (RLS) approach. For successive disturbance use of LM in conjunction with non-linear neural network structure yields faster convergence compared to RLS. A case study on a test system demonstrates the effectiveness of the online LM method for both linear and nonlinear estimation over RLS estimation (linear).