title: Detection of Power Disturbances for Power Quality Monitoring Using Mathematical Morphology
Creators: name: Dwijaya Saputra, I
Publisher: name: University of Liverpool Repository
Year: 2017

description: In power quality monitoring, determining the type of power quality disturbance occurring in the power system is important. Some disturbances such as, voltage dip, momentary interruption, voltage swell, or oscillatory transients in power systems may result internal-function or failure in the operation of some devices. Knowing the location where the disturbances occur in the system can yield an effective and efﬁcient result when an appropriate method is applied in the attempt to solve the power quality issues. Some traditional strategies such as, wavelet or Fast Fourier transforms have been applied to detect and locate power quality disturbances, suffer from the complexity of the algorithm and the calculation load. In this thesis, mathematical morphology has been investigated for this purpose due to the merits of robustness and the simple calculations needed. In this thesis, some novel strategies using mathematical morphology are presented to ﬁnd the time location of the disturbances, that is deﬁned as the start and end points when the disturbance occur in the time domain. The ﬁrst method was using morphology gradient, top-hat transform, and Skeletonization to identify the time location of the disturbances and noise in the system, and then plotting the results in 3D for pattern recognition. This Skeletonization is also combined with Morphology Edge Detection to ﬁnd the accurate time location of disturbances in the system for both noise free signals and signals with noise. The overall result shows the reduction of the error was signiﬁcant compared to the result of morphology edge detection strategy. Another novel strategy is presented by converting a signal to an image then applying image processing techniques, which are then evaluated using a control chart to ﬁnd the time location of any disturbances. This conversion strategy is also applied for detecting the times of power quality disturbances uses short data samples of the signal (4 samples), so that it can be implemented as a real time detection strategy. The results show an accurate strategy in detecting disturbances. Half Multi-resolution Morphology Gradients (HMMG) based on multi-resolution morphology gradients (MMG) is also presented as a novel strategy and it operates in level 1 only, reducing the processing and increase the speed of detection of disturbance. The results show accurate detection when disturbances occur in the system. Other applications of MM are also presented such as a new alternative method in estimating the frequency in a signal based on top-hat and bottom-hat transforms with the results showing the ability of this method to handle low frequencies when the signal is a noise free signal. Neural networks are also implemented with MM for the identiﬁcation and classiﬁcation of disturbances. All the novel strategies using Skeletonization, signal/image conversion and HMMG for disturbances detection were then evaluated using a real dataset and an experimental dataset. Overall results show that this three methods can detect disturbances accurately.