基于人工神经网络的电力变换器下垂控制中的下垂系数设计

Habibu Hussaini, Tao Yang, Yuan Gao, Cheng Wang, T. Dragičević, S. Bozhko
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引用次数: 2

摘要

本文提出了一种利用人工神经网络设计功率变换器下垂控制中的下垂系数的新方法。首先,在设计空间内使用变换器下垂系数的不同组合在环路中模拟了一个详细的多电动飞机(MEA)电力系统(EPS)电路模型。然后从每个模拟中得到由于电缆电阻不等的影响而导致的变换器输出直流电流分配不准确。生成的数据然后用于训练神经网络,使其成为详细的MEA EPS仿真的专用代理模型。因此,对于在设计空间内的任何用户定义的转换器之间的期望电流共享,所提出的神经网络可以提供最优的下垂系数。通过仿真验证了这种神经网络方法可以确保变换器之间准确的电流共享。因此,可以在设计中采用下垂系数来提高常规下垂控制方法的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Droop Coefficient Design in Droop Control of Power Converters for Improved Load Sharing: An Artificial Neural Network Approach
In this paper, a new approach for the design of the droop coefficient in the droop control of power converters using the artificial neural network (ANN) is proposed. In the first instance, a detailed more electric aircraft (MEA) electrical power system (EPS) circuit model is simulated in a loop using different combinations of the converters droop coefficients within a design space. The inaccurate output DC currents sharing of the converters due to the influence of the unequal cable resistance are then obtained from each of the simulations. The data generated is then used to train the NN to be a dedicated surrogate model of the detailed MEA EPS simulation. Thus, for any user-defined desired current sharing among the converters that are within the design space, the proposed NN can provide the optimal droop coefficients. This NN approach has been verified through simulations to ensure accurate current sharing between the converters as desired. Hence, can be used in the design of the droop coefficient to enhance the performance of the conventional droop control method.
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