Thunderstorm cloud charge inversion method based on improved sparrow search algorithm
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1.School of Electrial and Energy Enginggering,Nantong Institute of Technology, Nantong 226001, China; 2.School of Electronics and Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China

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TN98

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    Abstract:

    In order to solve the problems of poor accuracy of the charge inversion method for thunderstorm clouds and the influence of the existing charge inversion model by the environmental error caused by multi-station network observation, a nonlinear equation system is derived to establish a charge inversion model based on the three-dimensional atmospheric electric field. On the basis of the assumption of equal thickness model of thunderstorm clouds, a set of nonlinear equations required for the charge inversion of thunderstorm clouds is derived, and a three-dimensional atmospheric electric field-based charge inversion model of thunderstorm clouds is established. The population initialisation of the sparrow search algorithm (SSA) is optimised by the sinusoidal chaotic mapping function to improve the nonlinearity of the distribution of the populations, and the Levy function and the inverse learning strategy are used to optimise the position updating of the algorithm’s discoverer way, a thunderstorm cloud charge inversion method based on improved sparrow search algorithm (ISSA) is proposed. A 3D atmospheric electric field instrument is used to observe the ground electric field data and analyse the electric field characteristics, and the improved SSA algorithm using the hybrid strategy is used to invert the thunderstorm cloud charging model parameters. The experimental results show that the inversion of the data obtained from the three-dimensional atmospheric electric field instrument (3DAEF) can effectively eliminate the errors caused by the multi-station network observation. Compared with the SSA, the deviation rate of the two-second neighboring charges of the thunderstorm cloud obtained by the improved ISSA algorithm is around 1%, and the fitness value reaches as low as 5.38, which is able to accurately invert the charging parameters of the thunderstorm cloud, and provide a certain reference to the study of its charging and discharging process.

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  • Received:
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  • Online: October 31,2024
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