Reference
G. Bajracharya, T. Koltunowicz,
R. R.
Negenborn, D. Djairam, B. De Schutter, and
J. J. Smit, "Optimization of transformer loading
based on hot-spot temperature using a predictive health model,"
Proceedings of the 2010 International Conference on Condition
Monitoring and Diagnosis (CMD 2010), Tokyo, Japan, pp. 914-917, Sept.
2010.
Abstract
In the future grid, power equipment will need to work with distributed
generation, deregulation, and accelerated aging. To this end, a model-based
framework for the optimization of usage of power equipment is proposed. The
framework uses a predictive health model of the equipment in order to optimize
the usage of the equipment. In particular, the predictive health model predicts
the hot-spot temperature of the transformers in a network over a future time
window based on the expected loading. The allowed loading limits of the
transformers are based on the hot-spot temperature. Therefore, the optimal
loading of the transformers is maintained by performing an optimal power flow
(OPF) computation of the network that takes into account hot-spot temperature
dynamics. The optimization determines values for the tap position of the
transformers and the active and reactive power of generators in the network.
Moreover, shedding of the loads in the network is considered when the
aforementioned options are not sufficient to control the loading of the
transformers. A case study using the IEEE 14-bus benchmark system is presented.
The shedding of the loads is minimized by using this technique.
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BibTeX
@inproceedings{BajKol:10-037,
author = {Bajracharya, Gautam and Koltunowicz, Tomasz and Negenborn, Rudi
R. and Djairam, Dhiradj and De Schutter, Bart and Smit, Johan
J.},
title = {Optimization of Transformer Loading Based on Hot-Spot
Temperature Using a Predictive Health Model},
booktitle = {Proceedings of the 2010 International Conference on Condition
Monitoring and Diagnosis (CMD 2010)},
address = {Tokyo, Japan},
pages = {914--917},
month = sep,
year = {2010}
}