In this paper, two accurate hybrid islanding detection schemes are proposed based on Wavelet Transform and Stockwell transform (S-transform). The proposed methods use the potential of sequence voltage (negative) retrieved at the target Distributed Generation (DG) location of the distribution network under study. In one of the schemes, Discrete Wavelet transform (DWT) is applied to process the negative sequence voltage signal and for its decomposition, which is further used to extract six statistical features like energy, entropy, mean, kurtosis, standard deviation, and skewness from the reconstructed DWT coefficients. Test and train data sets are generated with the wide variation of loading conditions, and optimal features are chosen from the full feature set by forward feature selection method (FFS) during the training process by an artificial neural network (ANN). After that, the trained system is tested to get the detection result. Another scheme presented in this paper for islanding detection is based on S-transform, which is used to decompose the negative sequence voltage signal. Amplitude, frequency, and phase are the three coefficients acquired from the pre-processing of the raw signal by S-transform. Then the cumulative sums of the energy content of the S-transform coefficients are determined and are compared with a threshold value to get the detection result. The proposed schemes are tested in a distribution network consisting of two 9 MW wind farm driven by six 1.5 MW wind turbine connected to 120 kV main grid through a 25 kV, 30 km feeder. Several cases have been investigated like normal condition, islanding, DG line trip, disconnection of point of common coupling, and sudden change in load to test the performance of the proposed schemes. It can be observed from the results that both the approaches gave high accuracy in the detection of islanding conditions and demarcates properly from the non-islanding state. However, results show that the S-transform based approach provides a better resolution and quick detection of islanding than the wavelet transform approach.

中文翻译：

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配电网的两种精确的混合孤岛检测方案

本文提出了两种基于小波变换和斯托克韦尔变换（S-transform）的精确混合孤岛检测方案。所提出的方法使用在研究中的配电网络的目标“分布式发电”（DG）位置获得的序列电压（负）电位。在其中一种方案中，离散小波变换（DWT）用于处理负序电压信号并对其进行分解，进而用于提取六种统计特征，例如能量，熵，均值，峰度，标准差和偏度。重建的DWT系数。测试和训练数据集是根据各种负载条件而产生的，在训练过程中，通过人工神经网络（ANN）从正向特征选择方法（FFS）从全部特征集中选择最佳特征。之后，对经过训练的系统进行测试以获得检测结果。本文提出的用于孤岛检测的另一种方案是基于S变换的，用于分解负序电压信号。幅度，频率和相位是通过S变换对原始信号进行预处理获得的三个系数。然后，确定S变换系数的能量含量的累积总和，并将其与阈值进行比较以获得检测结果。在由两个9兆瓦的风电场组成的配电网络中测试了这些方案，该风电场由六个1.5兆瓦的风力发电机驱动，这些风机通过25 kV电压连接到120 kV主电网，30公里支线。已经研究了几种情况，例如正常情况，孤岛，DG线路跳闸，公共耦合点断开以及负载突然变化，以测试所提出方案的性能。从结果可以看出，这两种方法在检测孤岛情况时都具有很高的准确性，并且可以正确地从非孤岛状态中划出界限。但是，结果表明，与小波变换方法相比，基于S变换的方法可提供更好的分辨率和快速的孤岛检测。从结果可以看出，这两种方法在检测孤岛情况时都具有很高的准确性，并且可以正确地从非孤岛状态中划出界限。但是，结果表明，与小波变换方法相比，基于S变换的方法可提供更好的分辨率和快速的孤岛检测。从结果可以看出，这两种方法在检测孤岛情况时都具有很高的准确性，并且可以正确地从非孤岛状态中划出界限。但是，结果表明，与小波变换方法相比，基于S变换的方法可提供更好的分辨率和快速的孤岛检测。