@inproceedings{dc1a5f0e3df64fbc9929be5374c7268c,
title = "Neural network based composite load models for power system stability analysis",
abstract = "Load modeling is an essential element in power system stability analysis. With the continuing increase of nonlinear and composite loads in power system, the modeling techniques used in the past may no longer be adequate. This paper proposes a methodology for the development of neural network based composite load model which can be applied to power system transient stability analysis. A two-layer neural network has been implemented to estimate the load power (P and Q) from terminal voltage and system frequency. The model has been validated using simulation test bed. The effect of measurement noise on the proposed methodology is also studied.",
keywords = "Artificial neural networks, Load modeling, Power systems, Stability analysis",
author = "Ali Keyhani and Wenzhe Lu and Heydt, {Gerald T.}",
year = "2005",
month = dec,
day = "1",
doi = "10.1109/CIMSA.2005.1522821",
language = "English (US)",
isbn = "0780390261",
series = "Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005",
pages = "32--37",
booktitle = "Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005",
note = "2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005 ; Conference date: 20-07-2005 Through 22-07-2005",
}