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Fast detection of islanding is very important for effective operation and control in distributed generation (DG) penetrated distribution networks. The islanding detection techniques such as passive, active, communication, and hybrid have their own merits and demerits. This paper proposed wavelet transform (WT) and wavelet packet transform (WPT) based techniques for detection of islanding and fault disturbances in a microgrid consisting of resources like wind turbine generator, fuel cell (FC), and microturbine. Voltage signal is extracted at the point of common coupling (PCC) and is passed through these detection techniques to obtain the time-frequency multi-resolution analysis. Further, to validate the graphical study, performance indices (PIs) like standard deviation and entropy are calculated for the disturbance detection using suitable selection of threshold. A comparative analysis using WT and WPT is presented in the form of graphical simulation as well as in terms of PIs to analyse their effectiveness and robustness under different operating conditions. It is observed that WPT shows better detection capability in comparison to WT even under 20-dB noisy scenarios. [ABSTRACT FROM AUTHOR]

Abstract This paper presents wavelet transform, independent component based detection of the power quality disturbances in a microgrid. Wavelet transform (WT) is well known for its better time-frequency multi-resolution analysis because of selection of adaptive window size depending inversely with the frequency. This makes it suitable for identification of both stationary and non-stationary characteristics of the power quality disturbances. But, it is analyzed in the study that the detection capability of wavelet is degraded when the input voltage signal contains noise and completely fails when the noise level is increased to 25 dB. Of course, a wavelet-denoising technique is tested to improve the ability of wavelet transform under noisy conditions, but observed to increase the detection time. Hence, independent component analysis (ICA) is proposed for detection based on its property of finding the significant components which are statistically independent and non-Gaussian. In addition, the statistical features are extracted to formulate the input dataset for support vector machine (SVM) to classify different power quality disturbances like sag, swell, interruptions, harmonics, transients, and flickering. A comparative analysis is presented and it is observed from the study that ICA-SVM hybrid combination provides better classification accuracy as comparison to WT-SVM under different operating scenarios. [ABSTRACT FROM AUTHOR]

Modified wavelet called wavelet packet transform (WPT) based approach is proposed in this paper for the detection of different disturbances occurring due to faults in grid-connected solar photovoltaic (SPV) power system. The detection performance of WPT is compared with that of wavelet transform under different operating scenarios. Further, performance indices such as energy and standard deviation (SD) are calculated to identify the faults from normal operating conditions. The qualitative and quantitative study suggests better detection performance of WPT over WT under different operating scenarios. [ABSTRACT FROM AUTHOR]