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A novel algorithm for asymmetrical fault detection in DFIG based wind-farm using wavelet singular entropy function

机译:一种使用小波奇异熵函数的DFIG基于风电场不对称故障检测算法

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This paper proposes a novel technique for asymmetrical fault detection (LLL-G) in DFIG based wind farm using wavelet singular entropy function. In this study, there are six wind turbine driven DFIG are grouping together to make wind farm and 9 MW power is feeding to the grid. Further, the rotor is supplied by a bidirectional PWM converter for the control of active and reactive power flows from DFIG to the grid. In the case study, the three-phase fault is created in the grid and proposed algorithm detects the fault with in one and half cycles for 60 Hz system. A new diagnostic method based on the grid modulating signals pre-processing by Discrete Wavelet Transform (DWT) to derive singular values, used to find out Shannon entropy, called Wavelet Singular Entropy (WSE) is here proposed to detect grid faults dynamically over time. Simulation results demonstrate the effectiveness of the proposed approach under time-varying conditions.
机译:本文采用小波奇异熵函数的基于DFIG的风电场中的非对称故障检测(LLL-G)的新技术。 在这项研究中,有六个风力涡轮机驱动的DFIG一起分组以使风电场和9 MW电力馈送到栅格。 此外,转子由双向PWM转换器提供,用于控制从DFIG到网格的主动和无功电流的控制。 在案例研究中,在网格中创建了三相故障,并将算法在一个半周期中检测到60 Hz系统的一个半周期。 一种新的基于网格调制信号预处理的新的诊断方法通过离散小波变换(DWT)来导出奇异值,用于找出Shannon熵,称为小波奇异熵(WSE),以便随着时间的推移动态地检测网格故障。 仿真结果证明了所提出的方法在时变条件下的有效性。

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