Introduction to Hybrid Dynamical Systems
Introduction to Hybrid Dynamical Systems
Benchmarking a Reduced Multivariate Polynomial Pattern Classifier
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear controller optimization of a power system based on reduced multivariate polynomial model
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A reduced multivariate polynomial model for multimodal biometrics and classifiers fusion
IEEE Transactions on Circuits and Systems for Video Technology
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This paper describes the nonlinear parameter optimization of power system stabilizer (PSS) by using the reduced multivariate polynomial (RMP) algorithm with the one-shot property. The RMP model estimates the second-order partial derivatives of the Hessian matrix after identifying the trajectory sensitivities, which can be computed from the hybrid system modeling with a set of differential-algebraic-impulsive-switched (DAIS) structure for a power system. Then, any nonlinear controller in the power system can be optimized by achieving a desired performance measure, mathematically represented by an objective function (OF). In this paper, the output saturation limiter of the PSS, which is used to improve low-frequency oscillation damping performance during a large disturbance, is optimally tuned exploiting the Hessian estimated by the RMP model. Its performances are evaluated with several case studies on both single-machine infinite bus (SMIB) and multi-machine power system (MMPS) by time-domain simulation. In particular, all nonlinear parameters of multiple PSSs on IEEE benchmark two-area four-machine power system are optimized to be robust against various disturbances by using the weighted sum of the OFs.