A new signal processing-based islanding detection method using pyramidal algorithm with undecimated wavelet transform for distributed generators of hydrogen energy. (26th May 2022) - Yılmaz, Alper; Bayrak, Gökay
Abstract: Machine learning-based fault detection methods are frequently combined with wavelet transform (WT) to detect an unintentional islanding condition. In contrast to this condition, these methods have long detection and computation time. Thus, selecting a useful signal processing-based approach is required for reliable islanding detection, especially in real-time applications. This paper presents a new modified signal processing-based islanding detection method (IDM) for real-time applications of hydrogen energy-based distributed generators. In the study, a new IDM using a modified pyramidal algorithm approach with an undecimated wavelet transform (UWT) is presented. The proposed method is performed with different grid conditions with the presence of electric noise in real-time. Experimental results show that oscillations in the acquired signal can be reduced by the UWT, and noise sensitivity is lower than other WT-based methods. The non-detection zone is zero and the maximum detection and computational time is also 75 ms at a close power match. Highlights: A new signal processing-based IDM is presented for hydrogen energy-based DGs. A UWT-based IDM with an improved pyramidal algorithm was implemented in real-time. The proposed IDM provides both zero NDZ and short detection time. Threshold values can be selected easily and noise sensitivity is more reliable. Only PCC voltage signal is required, thus, loss of computation time is minimum.