Identification of a continuous time nonlinear state space model for the external power system dynamic equivalent by neural networks

• Published in: International journal of electrical power & energy systems Vol. 31; no. 7; pp. 334 - 344
• Main Authors: Shakouri G., Hamed, Radmanesh, Hamid Reza
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.09.2009; Elsevier
• Subjects: Applied sciences; Artificial neural networks; Dynamic equivalent; Electrical engineering. Electrical power engineering; Electrical power engineering; Exact sciences and technology; Miscellaneous; Nonlinear identification; Numerical differentiation; Power networks and lines; Nonlinear identification; Numerical differentiation; Dynamic equivalent; Artificial neural networks; Differential equation; High precision; Continuous time; State space; Numerical method; State space method; Neural network; Non linear phenomenon; Learning; Equations of state; Time domain method; Electrical network; Non linear model; Dynamic stability; Algebraic equation; Non linear effect
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2009.03.016

Based on the concept of the external power system dynamic equivalent obtained for the study system, in this paper a reduced-order artificial neural network is proposed, to construct a model for the external part. The mastermind behind the proposed method is to identify the external part as a dynamic–algebraic ANN, and this separation between dynamic equations in the state space and algebraic equations is useful to solve the prediction problem. Moreover, using similarity transformations, the state space model can be simplified, such that all the nonlinearities are embedded in the algebraic part. Since usually the study system equations are available in the continuous time domain, the external part is converted to the continuous time domain by a novel method. To obtain this model, the system should be excited first by a sort of random disturbances, and then data measured on the boundary nodes is used to identify the model. Identification process is accomplished by training the proposed network which can be used to predict behavior of the external system with a high degree of accuracy. Such an equivalent has wide applications for dynamic stability studies.