Detection and Classification of Disturbances in Islanded Micro-grid

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Copyright: Wang, Yunqi
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Abstract
Micro-grids are envisaged as single plug-and-play electrical systems that can operate either in grid-connected or islanded modes. Their main sources of power are renewable and are usually integrated through power electronic devices. These present new challenges to reliable operation of the system due to their inherent incapability to regulate the system. Power quality (PQ) disturbances in the micro-grid make it further vulnerable, especially when the micro-grid operates in an islanded mode. The PQ disturbances only occur for a few cycles and cannot be easily detected directly in the time domain. This thesis utilizes wavelet transformation (WT) method to detect and analyse the disturbance signals in the islanded micro-grid. The signals are collected from both simulation and experimental setup. The wavelet-based and time domain based feature extraction algorithms are investigated and compared. Factors that can affect the performance of these methods are discussed. Furthermore, three different types of wavelet-based feature extraction methods are also tested and two of them – normilized Renyi entropy with signal energy and normalized Renyi entropy with distribution volume, are algorithms which have not been applied into the power system before. The results show that the disturbances created by using simulated data are successfully identified using wavelet transformation. The variation of wavelet coefficients helps in identifying the time and duration of the disturbances. However, when data from the micro-grid laboratory setup is used, the performance of the WT is not satisfactory due to presence of noise and harmonics. Although, single disturbances such as voltage sag, swell and interruption can still be identified and classified, the WT is unable to detect and classify multiple disturbances, especially when non-linear loads are involved. The presence of non-linear loads in islanded micro-grids, increases the fluctuations of the WT coefficients, thus making the disturbance signals to be buried. To overcome this issue, the Renyi entropy is used to improve the performance of WT. A comparison is made between the time-domain Renyi and wavelet-based Renyi. The results show that the wavelet-based Renyi has improved efficiency. Two normalized Renyi entropies are also investigated and compared with the normal Renyi entropy, to overcome the problem whereby Renyi entropy fails to detect zero-mean cross-terms in some cases when odd  is used, as well as to reduce the distribution to the unity signal energy case. These two algorithms are found to show higher accuracy and efficiency for classifying multiple disturbances in an islanded micro-grid.
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Author(s)
Wang, Yunqi
Supervisor(s)
Ravishankar, Jayashri
Phung, Toan
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Publication Year
2018
Resource Type
Thesis
Degree Type
PhD Doctorate
UNSW Faculty
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