Output limits of the power system stabilizer (PSS) can improve the system damping performance immediately following a large disturbance. Due to nonsmooth nonlinearities arising from the saturation limits, these values cannot be determined by the conventional tuning methods based on linear analysis. Only ad hoc tuning procedures can been used. A feedforward neural network (with a structure of multilayer perceptron neural network) is applied to identify the dynamics of an objective function formed by the states and, thereafter, to compute the gradients required in the nonlinear parameter optimization. Moreover, its derivative information is used to replace that obtained from the trajectory sensitivities based on the hybrid system model with the differential-algebraic-impulsive-switched structure. The optimal output limits of the PSS tuned by the proposed method are evaluated by time-domain simulation in both a single-machine infinite bus system and a multimachine power system.
S. Baek et al., "Power System Control with an Embedded Neural Network in Hybrid System Modeling," IEEE Transactions on Industry Applications, Institute of Electrical and Electronics Engineers (IEEE), Sep 2008.
The definitive version is available at https://doi.org/10.1109/TIA.2008.2002172
Electrical and Computer Engineering
Keywords and Phrases
Feedforward Neural Network (FFNN); Hybrid System; Nonlinearities; Nonsmoothness; Parameter Optimization; Power System Stabilizer (PSS)
International Standard Serial Number (ISSN)
Article - Journal
© 2008 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Sep 2008