The output limits of the power system stabilizer (PSS) can improve the system damping performance immediately following a large disturbance. Due to non-smooth nonlinearities 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 (FFNN) (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 (DAIS) 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 (SMIB) and a multi-machine power system (MMPS).
S. Baek et al., "Power System Control with an Embedded Neural Network in Hybrid System Modeling," Conference Record of the 41st IAS Annual Meeting of the Industry Applications Conference, 2006, Institute of Electrical and Electronics Engineers (IEEE), Jan 2006.
The definitive version is available at https://doi.org/10.1109/IAS.2006.256595
41st IAS Annual Meeting of the Industry Applications Conference, 2006
Electrical and Computer Engineering
Keywords and Phrases
Feedforward Neural Network; Component; Hybrid System; Non-Smoothness; Nonlinearities; Parameter Optimization; Power System Stabilizer
International Standard Serial Number (ISSN)
Article - Conference proceedings
© 2006 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.