Electrical Computer Engineering
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Item Reliability Enhancement and Power Loss Reduction in Distribution System by Optimal Reconfiguration (A case study of Kombolcha Distribution Network)(Hawassa University, 2020-10-26) DEGAREGE ANTENEH YALEWThe purpose of power system is to provide an adequate and secured electrical supply to its customers as economical as possible with reasonable level of reliability. In distribution system, reliability and power loss are the major issues for the consumers and utility. This thesis presents a research work on the reliability assessment and optimal reconfiguration of Kombolcha radial distribution network to minimize the overall distribution losses, to enhance reliability and improve the voltage profile by using a proposed MSSO algorithm and ETAP. In the power flow analysis, the effectiveness of the proposed MSSO algorithm is conducted and compared in two cases (before reconfiguration and after reconfiguration) of Kombolcha distribution at line-3, which is severe. A modeling of electrical system elements were first developed to have a complete single line diagram of the existing network using ETAP. The reliability enhancement is measured and analysis by different reliability indices that include SAIFI, SAIDI and EENS. Reliability index values have been compared with standard benchmark values and comparison clearly indicates that Kombolcha feeder-3 distribution system is extremely unreliable. Generally, after optimal reconfiguration of the network, power loss of the system is minimized by 60%, SAIFI has been reduced by 85% as compared with the average reliability index value of the system in the base years. Similarly, SAIDI and EENS have been reduced by 86% and 70% respectively.Item “DETECTION, CLASSIFICATION AND MITIGATION OF POWER QUALITY DISTURBANCE: A CASE STUDY ON THE 15KV DISTRIBUTION FEEDER 6(R4-G5) AT HAWASSA SUBSTATION”(Hawassa University, 2025-12-19) DAWIT DABAModern power systems face several difficulties due to power quality disturbances, such as voltage sags and swells, which call for effective detection, classification, and mitigation techniques. In order to fully address these problems, this study offers an integrated strategy that combines modern machine learning methods with optimization techniques. Artificial Neural Networks (ANN) trained and refined with MATLAB's Classification Learner Toolbox are used for detection and classification after features are extracted from voltage/current signals. This thesis was carried out on one of the Hawassa Feeder-6 (R4-G5) 15 kV distribution feeders utilizing distribution network analysis and MATLAB simulation. The purpose of power flow analysis is to ascertain the active and reactive power flows on the distribution lines, as well as the voltage magnitude and phase angle at each bus (node) in the system. Power quality disturbances are divided into four distinct wavelet filter levels using Debechesh-4 (Db-4). This enables the improvement of an approximate and detailed coefficient distribution in addition to the extraction of features such as the mean, maximum, and lowest values of the disturbances for power quality disruptions. The classification efficacy of neural networks (ANN) and support vector machines (SVMs) is 100%. Finally, a dynamic voltage restorer (DVR) is positioned optimally using the Grasshopper Optimization (GOA) techniques. Power loss decreased from 1913.3 kW (active) and 1202.4 kVAR (reactive) to 295.534 kW and 261.803 kVAR when GOA was used, and the voltage profile was increased from 70% to 98.5% and lowered the voltage swell from 110% to 98%, and also, by applying different kinds of faults, easily tested the voltage sag and swell by using DVR integrated with wavelet transforms algorithm. This reduces the possible influence on delicate systems and equipment while simultaneously enhancing the power supply's quality