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Effectiveness of a GNG-based MPPT and related control system for marine current turbines in unsteady operating conditions

机译:基于GNG的MPPT和相关控制系统在不稳定工况下对船用水轮机的有效性

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This paper presents the validation of a MPPT technique, based on a Growing Neural Gas (GNG) network, for high performance marine current generator with induction machine, in presence of torque oscillations caused by time and space variation of the turbine operating conditions. The unsteady torque delivered by a notional turbine working in the presence of waves and of sea bottom boundary layer is obtained by an unsteady hydrodynamic formulation based on a Boundary Element Method (BEM). The performance of MPPT has been experimentally tested on test bench equipped by a turbime emulator, an induction generator and a back-to-back power converter configuration with two voltage source converters, one on the machine side and the other on the grid side controlled with a high performance vector control technique, respectively Field Oriented Control (FOC) and Voltage Oriented Control (VOC). Experimental results show the correct behaviour of the proposed MPPT technique which permits to instantaneously estimate the sea speed and correspondingly to compute the optimal machine reference speed for tracking the maximum available power even in presence of the sea waves.
机译:本文介绍了一种基于增长型神经气体(GNG)网络的MPPT技术的验证,该技术适用于带有感应电机的高性能船用发电机,在存在由涡轮机运行条件的时间和空间变化引起的转矩振荡的情况下。通过基于边界元方法(BEM)的非稳态流体动力公式,可以得到在波浪和海底边界层存在下工作的名义涡轮机传递的非稳态转矩。 MPPT的性能已在由涡轮仿真器,感应发电机和背靠背电源转换器配置的测试台上进行了实验测试,该配置具有两个电压源转换器,一个在机器侧,另一个在电网侧,由高性能矢量控制技术,分别是磁场定向控制(FOC)和电压定向控制(VOC)。实验结果表明了所提出的MPPT技术的正确行为,该技术允许瞬时估计海速,并相应地计算出最佳的机器参考速度,以便即使在海浪的情况下也能跟踪最大可用功率。

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