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| Differential Evolution Algorithm for Dynamic Load Model Parameter Identification |
| Huang Yulong1, Chen Xun2, Liu Mingbo3, Chen Xiaoke2, Yang Fenyan2, Zeng Yan2, Lin Shunjiang3 |
| 1. Jinan University Zhuhai 519070 China 2. Electric Power Research Institute of Guangdong Power Grid Corporation Guangzhou 510600 China 3. South China University of Technology Guangzhou 510640 China |
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Abstract Parameter identification is the key technology in measurement-based load modeling. A differential evolution algorithm is proposed in this paper to identify dynamic load model’s parameter, which overcomes the slowly convergent speed and oversize population problem existing in the differential evolution algorithm for static load model parameter identification. The proposed differential evolution algorithm shows good robustness, fast convergence, strong global optimization searching capability, and reduces population size and computation burden. Dynamic load model parameter identification for field measurement data from two substations in a metropolitan shows that the accuracy of the proposed algorithm is obviously better than the genetic algorithm and the Levenberg-Marquardt algorithm combined hybrid learning algorithm, and the practicability of the proposed algorithm is validated accordingly.
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Received: 16 February 2013
Published: 26 March 2014
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2013, Vol. 28 
