Operating-condition-dependent ARMA model for PSS application

Adaptive power system stabilizers (APSS) have attracted plenty of interests in recent years. Most APSSs are model-based. The widely used models in APSSs are auto regression moving average (ARMA) and nonlinear auto regression moving average with exogeneous inputs (NARMAX) models. In This work, an operating-condition-dependent (OC-dependent) ARMA model is presented and realized by local model networks (LMN). The proposed model has the capability of fast learning similar to the RBF-based NARMAX model and can work for various operating conditions without updating its parameters. The effectiveness of the model is verified by simulation studies.