Power system dynamic load modeling using artificial neural networks

Author

Ku, Bih-Yuan ; Thomas, Robert J. ; Chiou, Chiew-Yann ; Lin, Chia-Jen

Author_Institution

Sch. of Electr. Eng., Cornell Univ., Ithaca, NY, USA

Volume

9

Issue

4

fYear

1994

fDate

11/1/1994 12:00:00 AM

Firstpage

1868

Lastpage

1874

Abstract

The dynamic characteristics of power system loads are critical to obtaining quality operating point-prediction and stability calculations. The composition of components at a load bus makes the aggregated behavior too complicated to be expressed by a simple form. Armed with the theorems recently developed on the approximation capability of artificial neural networks, the authors devise a load model to describe the complex dynamic behavior of loads. Real field data are used to train and test this model. The results verify that this model can emulate load dynamics well and should therefore be suitable as a representation of load for stability analysis

Keywords

approximation theory; digital simulation; learning (artificial intelligence); load (electric); neural nets; power system analysis computing; power system stability; AI; approximation capability; artificial neural networks; computer simulation; dynamic characteristics; load bus; operating point-prediction; power system loads; stability calculations; testing; training; Artificial neural networks; Impedance; Load modeling; Power system analysis computing; Power system dynamics; Power system modeling; Power system stability; Stability analysis; Testing; Voltage;

fLanguage

English

Journal_Title

Power Systems, IEEE Transactions on

Publisher

ieee

ISSN

0885-8950

Type

jour

DOI

10.1109/59.331443

Filename

331443