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dc.contributor.advisorMian Qaisar, Saeed
dc.contributor.authorAljefri, Raheef Ahmed
dc.date.accessioned2022-10-06T07:34:58Z
dc.date.available2022-10-06T07:34:58Z
dc.date.issued2020
dc.date.submitted2020
dc.identifier.urihttp://hdl.handle.net/20.500.14131/86
dc.description.abstractPower Quality (PQ) disturbances cause rigorous issues in classical and smart grids, combine both renewable and conventional power sources, and industries. The performance of power networks can be noticeably affected by these intermittent events. The sustainability of the energy supply can be significantly disturbed. Moreover, it can cause damage to appliances and industrial machines. The identification of PQ disturbances and effective prevention of such events are essential. The first research step in these studies is to collect or effectively model different types of PQ disturbances signals. Later on, these can be used for the PQ disturbances interpreting systems training and performance quantification. The recording and collection of such signals is not an easy task. To overcome this limitation a common trend is the generation of real-like signals from the mathematical models. The working steps of this thesis are to firstly identify and employ the existing potential PQ disturbances mathematical models. In second step these models are implemented in MATLAB. The output of this MATLAB model serves as the database and is used for the proposed system parameterization and performance quantification. In third step, signal reconstruction is performed in order to realize quasi analog signals. In this context mature, precise, cubic-spline and interpolators based signal reconstruction algorithms are evaluated. In fourth step, the reconstructed signals are acquired by using the MATLAB based event-driven sensing models. The acquired signal is segmented by using novel event- driven signal selection techniques. Afterwards, the segmented signal pertinent features are extracted by using an effective time-domain and hybrid parameters extractor. These features are later on used to prepare templates, testing instances and to train the considered classification algorithms. 7 The idea is based on smartly combining the event-driven signal acquisition and segmentation along with local features extraction and classification for realizing an efficient and high precision solution. The system performance is tested and the findings are reported in four case studies. It is shown that the solution proposed achieves the first order of magnitude reduction in the total sample count as compared with conventional equivalents. It indicates a major performance advantage and reliability when compared to peers in terms of power consumption and data transmission of the suggested solution. The suggested system achieves high automatic accuracy in reconnaissance of PQ signals. It shows the advantages of using the suggested approach to realize computationally efficient and cost- effective elucidators for automated PQ disturbances. A novel development is the use of event- sensing and processing techniques to contribute to the realization of new computationally effective strategies for detecting and minimizing PQ disturbances. We assume that it will result in energy efficient and simple hardware realizations as compared to the counter classical sensing and processing based approaches. This Study is well aligned with the 2030 vision of Saudi Arabia and can be well integrated in the NEOM smart metering system. The solution has a potential and it could be commercialized in collaboration with the authorities and the industrial partners in Saudi Arabia. The summary of the thesis consists of 490 words and written in 1.5 spaced between the rows
dc.language.isoen_US
dc.publisherEffat University
dc.subjectEvent-Driven Processing
dc.subjectTime-Domain Features Extraction
dc.subjectPower Quality (PQ)
dc.subjectDisturbances
dc.subjectCompression Gain
dc.subjectClassification
dc.titleModeling and event-driven processing based elucidation of the power quality disturbances in smart grids
dc.typeThesis
refterms.dateFOA2023-02-06T11:23:46Z
dc.contributor.researcherGraduate Studies and Research


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