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AbstractsWith the rapid development of China's marine economy and the port economy, involved in the work of the security problem is becoming more and more prominent, especially the crane operation risk relates to the port operation, which gets more attention. The operational risk and the early warning and scheduling should be

There is abundant research about achieving zero-voltage switching (ZVS) of dual active bridge (DAB) converters, among which the splitting of interfacing inductance and placing on both sides of the transformer is an effective method for extending the ZVS region for all the switching devices. However, the traditional analytical model can hardly imitate the proposed converter precisely under the high switching frequency (i.e.>1MHz) due to the complex converter model with the consideration of the parasitic components. Thus, the converter system can be regarded as a gray-box model. Consequently, artificial intelligence (AI) techniques can be utilized for the targeted optimization inside this gray-box. In this case, a genetic algorithm is employed in the DAB converter parametric design with an explicit fitness desire. The methodology of implementing AI techniques into converter parametric design is introduced and verified with a 1 MHz Gallium Nitride (GaN) based DAB converter prototype.

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