Institute of Technology

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The Institute of Technology focuses on education, research, and innovation in engineering, technology, and applied sciences to support sustainable development.

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    ENHANCING THE RELIABILITY OF DISTRIBUTION SYSTEM THROUGH RENEWABLE ENERGY RESOURCES (CASE STUDY: GUDER TOWN DISTRIBUTION SYSTEM
    (Hawassa University, 2024-10-25) FIRAOL KASAHUN MENGESHA
    The distribution system connects high-voltage transmission networks with end-users. Most of the time, power plants are situated distant from the consumer's location, resulting in large power losses in both the distribution and transmission systems, However, distribution system losses are typically greater than transmission line side losses. The main objective of this study is to reduce power losses and enhance system reliability using Distributed Generation (DG) in the case of the Guder Substation. The Guder Substation has three feeder lines that provide energy for different customers. From these feeders, the Guder town feeder has been chosen since it is frequently interrupted. The chosen feeder has been modeled in ETAP software, and simulation results have been obtained with both ETAP and MATLAB software. The results show that the feeder has a power loss of 611.9843 KW and 323.8237 kVar active and reactive, respectively. Additionally, the study investigates the existing reliability indices of SAIFI, SAIDI, and EENS, which have values of 303.7458 f/cust.yr, 306.4240 hr/cust.yr, and 2368.307 MWhr/yr, respectively. Particle Swarm Optimization algorithm has been suggested to decide the best size and position of DG. After renewable Distributed Generation penetrated the network, the real and reactive power loss reduced from 611.9843 KW and 323.8237 kVar to 302.75 KW and 132.34 kVar, respectively. Additionally, the SAIFI, SAIDI, and EENS system reliability indices were enhanced from 303.7458 f/cust.yr, 306.4240 hr/cust.yr, and 2368.307 MWhr/yr to 27.4968 f/cust.yr, 13.650 hr/cust.yr, and 111.758 MWh/yr, respectively. Finally, reliability indices and line losses before and after Distributed Generations penetrated the network are compared. In general, the simulation results indicate that the suggested method is efficient in maintaining system reliability and minimizing power losses
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    “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 DABA
    Modern 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
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    ENHANCING THE RELIABILITY OF DISTRIBUTION SYSTEM THROUGH RENEWABLE ENERGY RESOURCES
    (Hawassa University, 2024-04-12) FIRAOL KASAHUN MENGESHA
    The distribution system connects high-voltage transmission networks with end-users. Most of the time, power plants are situated distant from the consumer's location, resulting in large power losses in both the distribution and transmission systems, However, distribution system losses are typically greater than transmission line side losses. The main objective of this study is to reduce power losses and enhance system reliability using Distributed Generation (DG) in the case of the Guder Substation. The Guder Substation has three feeder lines that provide energy for different customers. From these feeders, the Guder town feeder has been chosen since it is frequently interrupted. The chosen feeder has been modeled in ETAP software, and simulation results have been obtained with both ETAP and MATLAB software. The results show that the feeder has a power loss of 611.9843 KW and 323.8237 kVar active and reactive, respectively. Additionally, the study investigates the existing reliability indices of SAIFI, SAIDI, and EENS, which have values of 303.7458 f/cust.yr, 306.4240 hr/cust.yr, and 2368.307 MWhr/yr, respectively. Particle Swarm Optimization algorithm has been suggested to decide the best size and position of DG. After renewable Distributed Generation penetrated the network, the real and reactive power loss reduced from 611.9843 KW and 323.8237 kVar to 302.75 KW and 132.34 kVar, respectively. Additionally, the SAIFI, SAIDI, and EENS system reliability indices were enhanced from 303.7458 f/cust.yr, 306.4240 hr/cust.yr, and 2368.307 MWhr/yr to 27.4968 f/cust.yr, 13.650 hr/cust.yr, and 111.758 MWh/yr, respectively. Finally, reliability indices and line losses before and after Distributed Generations penetrated the network are compared. In general, the simulation results indicate that the suggested method is efficient in maintaining system reliability and minimizing power losses