Application of affine narma model to design of adaptive power system stabilizer

Jingsen Zhou, K. E. Deping, Chi Yong Chung, S. U.N. Yuanzhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review


An affine nonlinear autoregressive moving average (NARMA) model is derived from the neural network (NN) based general NARMA model in this paper, by using Taylor series expansion. The predictive error of this affine NARMA model will be quite acceptable, at least for the control purpose, if the amplitude of control input is properly limited. Therefore, an adaptive control scheme based on this model is proposed and applied to the design of adaptive power system stabilizer (APSS) since the amplitude of PSS output is usually well limited. The feature of this control scheme is that the control input can be online analytically obtained. Thus, comparing to the traditional NN based APSS (TAPSS), the affine NARMA model based APSS (AAPSS) does not need the training of a NN as neuro-controller, which may be a troublesome and time consuming step during the design. Moreover, the AAPSS can generally perform better than the TAPSS. Simulation studies on a single machine infinite bus system and a multi-machine system show that the AAPSSs can consistently well perform to damp electromechanical oscillations in the systems over a wide range of operating conditions.

Original languageEnglish
Pages (from-to)105-114
Number of pages10
Issue number2
Publication statusPublished - 2018
Externally publishedYes


  • Adaptive control
  • Affine
  • NARMA model
  • Neural network
  • Power system stabilizer (PSS)

ASJC Scopus subject areas

  • Software
  • Mechanical Engineering
  • Electrical and Electronic Engineering


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