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 |
| Volume | 2005 |
| DOIs | |
| State | Published - 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 |
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 |
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Keywords
- Artificial neural networks
- Load modeling
- Power systems
- Stability analysis
ASJC Scopus subject areas
- Engineering(all)
Cite this
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. Vol. 2005 2005. p. 32-37 1522821.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Neural network based composite load models for power system stability analysis
AU - Keyhani, Ali
AU - Lu, Wenzhe
AU - Heydt, Gerald T.
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
KW - Artificial neural networks
KW - Load modeling
KW - Power systems
KW - Stability analysis
UR - http://www.scopus.com/inward/record.url?scp=33749059227&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33749059227&partnerID=8YFLogxK
U2 - 10.1109/CIMSA.2005.1522821
DO - 10.1109/CIMSA.2005.1522821
M3 - Conference contribution
AN - SCOPUS:33749059227
SN - 0780390261
SN - 9780780390263
VL - 2005
SP - 32
EP - 37
BT - Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005
ER -