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ART artificial neural networks based adaptive phase selector

机译:基于ART人工神经网络的自适应相位选择器

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This paper introduces a new phase selector based on adaptive resonance theory (ART). Because conventional phase selector cannot adapt dynamically to the power system operating conditions, it presents different characters under different power system conditions. To overcome the disadvantage, an adaptive phase selector, which utilizes artificial neural network based on ART, is designed. ART based neural network (ARTNN) has some advantages such as no local extremum, quickly convergence and so on. Therefore, the proposed ARTNN based phase selector has better performances compared with other neural networks based phase selector, and the new selector can adapt dynamically to the varying power system operation conditions. Furthermore, the phase selector can be trained and learned on-line. A lot of EMTP simulations and experimental field data tests have illustrated the phase selector's correctness and effectiveness.
机译:本文介绍了一种基于自适应共振理论(ART)的新型相位选择器。因为常规的相位选择器不能动态地适应电力系统的运行条件,所以它在不同的电力系统条件下呈现出不同的特性。为了克服该缺点,设计了一种自适应相位选择器,其利用了基于ART的人工神经网络。基于ART的神经网络(ARTNN)具有一些优点,例如没有局部极值,快速收敛等。因此,与基于其他神经网络的相位选择器相比,基于ARTNN的相位选择器具有更好的性能,并且新的选择器可以动态地适应变化的电力系统运行条件。此外,可以在线训练和学习相位选择器。大量的EMTP仿真和实验现场数据测试已经证明了相位选择器的正确性和有效性。

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