Power System Control with an Embedded Neural Network in Hybrid System Modeling

Seung-Mook Baek
Jung-Wook Park
Ganesh K. Venayagamoorthy, Missouri University of Science and Technology

This document has been relocated to http://scholarsmine.mst.edu/ele_comeng_facwork/1215

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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).