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• W1961721111 abstract "The goal of this paper is to develop a global identification framework based on the maximum likelihood principle to achieve the accurate dynamic parameters of generator units. The proposed procedure is formulated as a multi-parameter optimisation problem aiming at the minimisation of the mismatch between the actual measurement and the simulated model output. In most cases a large number of unknown parameters enter the optimisation problem, therefore resulting in large search spaces. In order to cope with this difficulty, a hybrid method combining the improved shuffled frog leaping algorithm and differential evolution algorithm is used to solve the optimisation problem. Since noise and uncertainties are inherent parts of system identification, which may cause problems to parameters estimation, the effect of these factors on the performance of the proposed identification framework are studied. Examples based on synthetic data in frequency domain show that the estimated parameters are close to the correct values even in the presence of significant measurement noise and considerable uncertainties." @default.
• W1961721111 created "2016-06-24" @default.
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• W1961721111 date "2015-01-01" @default.
• W1961721111 modified "2023-09-27" @default.
• W1961721111 title "Parameter identification of power plant characteristics based on PMU data using differential evolution-based improved shuffled frog leaping algorithm" @default.
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• W1961721111 doi "https://doi.org/10.1504/ijbic.2015.071065" @default.
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