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Fault detection for offshore wind farm connected to onshore grid via voltage source converter-high voltage direct current

机译:通过电压源转换器-高压直流电连接到陆上电网的海上风电场的故障检测

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The fault study in offshore wind farm connected to onshore grid is carried out using empirical mode decomposition (EMD) which is a powerful technique for analysing linear, non-linear, stationary and non-stationary signals. The efficacy of the proposed approach is carried out by virtue of comparative assessment with other well-established signal processing techniques: wavelet transform, Stockwell transform, hyperbolic Stockwell transform in the literature. The intrinsic mode functions (IMFs) are obtained using EMD for signals (normal and fault) retrieved at different sections. Hilbert transform is applied to each IMF in order to evaluate the magnitude and phase angle information used for analysing the signal. Both qualitative and quantitative analyses are carried out to demonstrate the effective detection of fault occurrence. The adopted approach accurately discriminates the occurrence of fault in AC/DC network section. The validation of the detection algorithm has been demonstrated through real-time digital simulator studies. In addition, faulty sections are characterised adopting classification strategy using support vector machines.
机译:使用经验模式分解(EMD)进行与陆上电网相连的海上风电场的故障研究,该模型是分析线性,非线性,平稳和非平稳信号的强大技术。所提方法的功效是通过与其他公认的信号处理技术进行比较评估来实现的:文献中的小波变换,Stockwell变换,双曲线Stockwell变换。使用EMD针对在不同部分获取的信号(正常和故障)获得固有模式函数(IMF)。将希尔伯特变换应用于每个IMF,以评估用于分析信号的幅度和相位角信息。进行了定性和定量分析,以证明对故障发生的有效检测。采用的方法可以准确地区分AC / DC网络部分中的故障。通过实时数字仿真器研究证明了检测算法的有效性。另外,通过使用支持向量机的分类策略来对故障区域进行特征化。

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