Neural network based composite load models for power system stability analysis

Ali Keyhani; Wenzhe Lu; Gerald T. Heydt

doi:10.1109/CIMSA.2005.1522821

Neural network based composite load models for power system stability analysis

Ali Keyhani, Wenzhe Lu, Gerald T. Heydt

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

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.

Original language	English (US)
Title of host publication	Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005
Pages	32-37
Number of pages	6
DOIs	https://doi.org/10.1109/CIMSA.2005.1522821
State	Published - Dec 1 2005
Event	2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005 - Giardini, Naxos, Italy Duration: Jul 20 2005 → Jul 22 2005

Publication series

Name	Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005
Volume	2005

Other

Other	2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005
Country	Italy
City	Giardini, Naxos
Period	7/20/05 → 7/22/05

Keywords

Artificial neural networks
Load modeling
Power systems
Stability analysis

ASJC Scopus subject areas

Engineering(all)

Access to Document

10.1109/CIMSA.2005.1522821

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Keyhani, A., Lu, W., & Heydt, G. T. (2005). Neural network based composite load models for power system stability analysis. In Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005 (pp. 32-37). [1522821] (Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005; Vol. 2005). https://doi.org/10.1109/CIMSA.2005.1522821

Neural network based composite load models for power system stability analysis. / Keyhani, Ali; Lu, Wenzhe; Heydt, Gerald T.

Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005. 2005. p. 32-37 1522821 (Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005; Vol. 2005).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Keyhani, A, Lu, W & Heydt, GT 2005, Neural network based composite load models for power system stability analysis. in Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005., 1522821, Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005, vol. 2005, pp. 32-37, 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005, Giardini, Naxos, Italy, 7/20/05. https://doi.org/10.1109/CIMSA.2005.1522821

Keyhani A, Lu W, Heydt GT. Neural network based composite load models for power system stability analysis. In Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005. 2005. p. 32-37. 1522821. (Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005). https://doi.org/10.1109/CIMSA.2005.1522821

Keyhani, Ali ; Lu, Wenzhe ; Heydt, Gerald T. / Neural network based composite load models for power system stability analysis. Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005. 2005. pp. 32-37 (Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005).

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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.",

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Neural network based composite load models for power system stability analysis

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