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dc.contributor.authorAl-Roomi, Ali Ridha
dc.date.accessioned2020-04-14T14:46:18Z
dc.date.available2020-04-14T14:46:18Z
dc.identifier.urihttp://hdl.handle.net/10222/78503
dc.description.abstractThe main goal of this research is to pave a new path to solve electric power system problems from a realistic perspective. The problems covered in this thesis are power transmission lines, power flow (PF) or load flow (LF) analysis, economic load dispatch (ELD), power and energy forecasting, optimal relay coordination (ORC), fault location, and state estimation. In this journey, we try to push the borders by digging everywhere to find other alternatives to precisely solve many existing real-world power system problems. This study considers many hidden facts and phenomena. To ensure accomplishing this ambitious task with some sorts of intelligence, advanced tools are involved; including traditional and meta-heuristic optimization algorithms and machine learning (ML) computing systems. Some superior hybrid optimization algorithms and ML computing systems are developed. The mission starts from the Telegrapher's equations where the distributed- and lumped-parameter transmission line models are built on. The realization phase is done by considering the effects of surrounding weather, system frequency, load current, and cable design/status/age. Some innovative techniques are proposed to solve the inherent weaknesses in explaining the deviation in distributed series and shunt parameters of lines under sag. This realization is applied to enhance the solutions of PF, ELD, short-circuit analysis, power system stability and ORC problems. To avoid tedious and highly time-consuming computational methods, a new set of optimization-free/modeling-free techniques are designed to solve ELD problems. Through the realization and integration phases, many new innovative ideas are presented. Also, because many problems heavily depend on ML tools, so a new computing system is designed to achieve the accuracy and precision criteria without losing the explainability and interpretability criteria. That is, compromising between the strengths of classical linear regression (LR) and nonlinear regression (NLR) analysis and modern artificial neural networks (ANNs) and support vector machines (SVMs). To judge the performance of each technique, many theoretical and real-world test systems and datasets are used with considering different scenarios and conditions.en_US
dc.language.isoenen_US
dc.subjectPower System Realizationen_US
dc.subjectPower System Integrationen_US
dc.subjectEconomic Load Dispatchen_US
dc.subjectModeling of Transmission Linesen_US
dc.subjectOptimal Relay Coordinationen_US
dc.subjectTemperature-Dependent Power Flowen_US
dc.subjectMeta-Heuristic Optimization Algorithmsen_US
dc.subjectHybrid Optimization Algorithmsen_US
dc.subjectMachine Learningen_US
dc.subjectRegression Analysisen_US
dc.subjectState Estimationen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectSupport Vector Machinesen_US
dc.subjectUniversal Functions Originatoren_US
dc.subjectSymbolic Regressionen_US
dc.subjectFunction Approximationen_US
dc.subjectBiogeography-Based Optimizationen_US
dc.subjectSimulated Annealingen_US
dc.subjectDifferential Evolutionen_US
dc.subjectLinear Programmingen_US
dc.subjectSequential Quadratic Programmingen_US
dc.subjectPower Flowen_US
dc.subjectSmart Griden_US
dc.subjectLocal Energy Tradingen_US
dc.subjectDemand Forecastingen_US
dc.subjectOptimal Hyperparametersen_US
dc.subjectLinear Heat Sensorsen_US
dc.subjectPower System Protectionen_US
dc.subjectPower Stationsen_US
dc.subjectDirectional Overcurrent Relaysen_US
dc.subjectHeat Transferen_US
dc.subjectPower Lossesen_US
dc.subjectTelegrapher's Equationsen_US
dc.titleIMPROVED POWER SYSTEM REALIZATION AND INTEGRATIONen_US
dc.date.defence2020-04-08
dc.contributor.departmentDepartment of Electrical & Computer Engineeringen_US
dc.contributor.degreeDoctor of Philosophyen_US
dc.contributor.external-examinerSherif O. Farieden_US
dc.contributor.graduate-coordinatorDmitry V. Trukhacheven_US
dc.contributor.thesis-readerJason J. Guen_US
dc.contributor.thesis-readerWilliam J. Phillipsen_US
dc.contributor.thesis-supervisorMohamed E. El-Hawaryen_US
dc.contributor.thesis-supervisorDmitry V. Trukhacheven_US
dc.contributor.ethics-approvalNot Applicableen_US
dc.contributor.manuscriptsNot Applicableen_US
dc.contributor.copyright-releaseNot Applicableen_US
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