A Novel Parameter Identification Approach via Hybrid Learning for Aggregate Load Modeling

@article{Bai2009ANP,
  title={A Novel Parameter Identification Approach via Hybrid Learning for Aggregate Load Modeling},
  author={Hua Bai and Pei Zhang and Venkataramana Ajjarapu},
  journal={IEEE Transactions on Power Systems},
  year={2009},
  volume={24},
  pages={1145-1154}
}
Parameter identification is the key technology in measurement-based load modeling. A hybrid learning algorithm is proposed to identify parameters for the aggregate load model (ZIP augmented with induction motor). The hybrid learning algorithm combines the genetic algorithm (GA) and the nonlinear Levenberg-Marquardt (L-M) algorithm. It takes advantages of the global search ability of GA and the local search ability of L-M algorithm, which is a more powerful search technique. The proposed… CONTINUE READING
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Power system load modeling by learning based on system measurements

  • J. Y. Wen, L. Jiang, Q. H. Wu, S. J. Cheng
  • IEEE Trans. Power Del., vol. 18, no. 2, pp. 364…
  • 2003
2 Excerpts

The load modeling and parameter identification for voltage stability analysis

  • Q. S. Liu, Y. P. Chen, D. F. Duan
  • Proc. Int. Conf. Power System Technology, 2002…
  • 2002
2 Excerpts

Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models

  • O. Nelles
  • 2001
1 Excerpt

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