Ethiopia Institute of Technology- Mekelle

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    Efficiency Improvement in MV Distribution System through Feeder Reconfiguration (Case Study: Adwa Distribution System)
    (Mekelle University, 2025-04-07) Yosef Amaha
    The electric power industry is increasingly challenged by rising load demand, aging infrastructure, and the complex spatial distribution of electrical loads, all of which undermine distribution system efficiency. These are especially pronounced in radial networks, where high technical losses and voltage deviations compromise power quality and reliability. Adwa feeder, a 15 kV radial distribution system in Ethiopia, embodies these concerns, with technical losses reaching 940.84 kW, voltage levels dropping to 0.83 per unit, and line loading peaking at 148.14 percent. This study explores feeder reconfiguration using Particle Swarm Optimization (PSO) to improve energy efficiency, voltage stability, and line overloading under varied load conditions. Load flow analysis was conducted using the Backward/Forward Sweep method in MATLAB under peak, medium, and minimum loading conditions, with total demands of 7,839 kW, 4,710 kW, and 2,160 kW, respectively. PSO identified an optimal switching scheme by opening sectionalizing switches 52 and 71, keeping tie switches 79 and 82 open, and closing 80 and 81. The optimized configuration reduced peak losses by 35.7%, lowering them to 604.87 kW and improving minimum bus voltage to 0.9161 per unit. For medium and minimum loads, losses dropped from 570 kW to 370 kW and from 140 kW to 90 kW, respectively, improvements of 35% and 36% with corresponding voltage profile enhancements. To validate robustness, Monte Carlo simulations (1,000 iterations, ±10% load variation, ±0.05 PF deviation) confirmed the optimized topology sustained losses near 605 kW and voltages above 0.9 per unit under uncertainty. Furthermore, upgrading the most overloaded segment (Line 1–2) to an AAC 150 conductor further improves losses to 524.32 kW. While this upgrade alone provided an 8.6% gain beyond reconfiguration, the combined effect achieved a total loss reduction of 44.3% from the original case. These results demonstrate that intelligent feeder reconfiguration, enhanced by PSO and supported by probabilistic analysis, provides a scalable, cost-effective solution for improving performance in radial distribution networks like Adwa's.
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    OPTIMAL DISTRIBUTED GENERATION INTEGRATION PLANNING (CASE STUDY: SHIRE CITY POWER DISTRIBUTION SYSTEM)
    (Mekelle University, 2025-05-21) TSIGE ABRHA
    This thesis examines the challenges of power distribution in Shire City, Tigray, Ethiopia, where rapid urban growth driven by industrial, commercial, and residential expansion has increased electricity demand. This rising demand is causing voltage degradation and higher power losses, raising significant stress on the existing infrastructure. The study forecasts future load requirements and explores the addition of renewable energy sources like solar and wind to optimize the distribution network, ensuring a sustainable and reliable power supply. The study uses advanced econometric methods to forecast future load requirements. The results show that peak load demand will rise from 17.29 MW in 2024 to 31.39 MW by 2043, driven by population and economic growth. This increase necessitates significant improvements to both generation and distribution infrastructure. The study further explores the integration of DG to mitigate power losses and improve voltage stability. Optimal distributed generation (DG) integration, as shown by the analysis, yields a significant 91% decrease in active and a 90.84% decrease in reactive power losses while simultaneously optimizing voltage profiles. Cost estimation for the 8.2 MW DG system, comprising photovoltaic and wind technologies, was found to be $3,233,880, with a return on investment expected in 3.77 years. This research demonstrates that integrating DG into the Shire City distribution network offers a viable solution to meet rising electricity demand, reduce power losses, and enhance system stability
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    Loss Reduction and Voltage Stability Enhancement of Distribution Network through Optimal Allocation of Distribution STATCOM Case Study: (Axum Distribution Network)
    (Mekelle University, 2025-06-05) Luel Awetehey
    This thesis presents the way of improving the performance of the distribution network by improving the voltage profile and reduces the power loss by integrating D-STATCOM to Axum K4 distribution feeder. D-STATCOM is commonly used in the distribution system for reactive power compensation so that it improves voltage profile, reduces power losses and improves the system voltage stability. The study of this work was conducted on Axum distribution K4 feeder it consists of a total number of Fifty-nine–bus feeders, of which bus-1 is taken as a reference node or slack bus, the other 47 nodes are connected to loads through step-down distribution transformer, and the remaining 12 nodes are common coupling nodes. The voltage profiles of most buses are not in an acceptable range, and the voltage stability index of the buses shows that the network is prone to voltage stability problems. The active and reactive power loss of feeder is 131.72KW and 111.35KVAr respectively. The optimal D-STATCOM allocation in electric distribution system enhances maximizing energy utilization, feeder loss reduction, and voltage stability and profile improvement. To allocate power control variable in the best possible location and with proper size two solution methods are applied. As the first method, the weakest bus of the system was selected for the optimal placement of D-STATCOM using bus based voltage stability index analysis. In the second method, particle swarm optimization (PSO) was applied for selecting optimal placement and size of DSTATCOM. The PSO optimization algorithm formulates a problem by considering system loss reduction, enhancement of voltage profile and voltage stability index of the operated network. A direct load flow analysis also carried out for the purpose of total system loss and bus voltage magnitude determination before and after compensation with D-STATCOM. The optimal allocation problem was tested in different system case based on the number and size of D-STATCOM. By comparing the net cost of D-STATCOM in relation to total system loss reduction single D-STATCOM installation has a better system. After the installation of single D-STATCOM with an optimal allocation through the feeder. The voltage profile of the system improved between 0.95-1.05p.u. The voltage stability index of the operated network increases as compared with base case stability. The active and reactive power loss through the line reduced to 25.03% and 25.25% respectively
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    Design and Analysis of Solar water pumping system for Agricultural Farming of Sweet Potato
    (Mekelle University, 2025-04-07) Tsegabrhan Gebretsadkan
    Quality of life, sustainable development, and economic progress are all largely influenced by energy. The shift to renewable energy sources has become essential in light of the growing global energy crisis, which is characterized by rising demands and the depletion of fossil fuel resources. Solar energy is one of the most promising of these, especially when it comes to solving important issues with agricultural methods.The design and study of a solar photovoltaic (PV) irrigation pumping system for sweet potato growing in the Abraha-Atsbeha hamlet are the focus of this thesis. Because they rely on expensive and environmentally damaging diesel pumping equipment, local farmers today confront many challenges. To address these challenges, the research begins with a comprehensive review of existing literature on solar irrigation technology, identifying key gaps and challenges in the field. Building on this foundation, a conceptual design for the PV irrigation system is developed, incorporating essential components such as the PV array, pump, storage tank, and control systems tailored to the specific needs of sweet potato cultivation. A detailed computer model of the proposed system is created and simulated using advanced software tools, including CROWAT, MATLAB, PSIM, HOMER, PVSyst, EasyEDA, and Drawing.io. Benchmarking exercises are conducted to evaluate the system's efficiency across varying climatic conditions by adjusting parameters like temperature and solar irradiance. The findings highlight the effectiveness of the Perturb and Observe (P&O) Maximum Power Point Tracking (MPPT) algorithm in optimizing power output and ensuring efficient operation. According to the results, the Perturb and Observe (P&O) Maximum Power Point Tracking (MPPT) algorithm is a good way to maximize power output and operational efficiency. In summary, a comparative study shows that the solar-powered irrigation system has several benefits over conventional diesel systems, such as decreased carbon emissions and operating expenses. The potential of solar technology to revolutionize farming methods, enhance food security, and support a sustainable energy emphasized by is this study
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    Reliability Enhancement of Distribution System by Using Feeder Reconfiguration (Case Study: Mekelle City Distribution System)
    (Mekelle University, 2025-04-04) Meron Debru
    Equipment failures and customer interruptions are the primary factors that affect distribution reliability. In many developing nations, including Ethiopia, reliability modeling and evaluation of distribution networks receive less attention "compared to" generating and transmitting systems. Since electric power is now used directly or indirectly for many of our activities, the utility should provide dependable electricity every day of the year to meet consumer demands and enable employees to do their jobs as efficiently as possible. However, several fault types affect the power supply’s reliability and quality. Power quality issues and interruptions are common in Mekelle distribution substation. Short-circuit faults, whether permanent or transient, system overloading and other factors are the primary causes of these interruptions. As a result, consumers are not receiving reliable power. The main goal of this work is the enhancement of the 15 kV side Mekelle distribution networks at a reasonable cost. Because several factors are causing power outages that mostly affect the 15 kV side. The reliability assessment has been done on fifteen feeders of the 15 kV Mekelle city distribution network. A reliability assessment of feeders on the 15 kV side has been carried out in order to assess the effectiveness of the current system and predict upcoming reliability evaluations. All the interruption data of five years (2011 E.C. up to 2015 E.C.) has been used which have been collected from Mekelle substation as well as north region Ethiopian electric power. Different alternatives have been assessed using ETAP 19.0.1 software method and the alternative with low SAIDI, SAIFI and EENS with a reasonable cost has been preferred. The reliability of Mekelle city distribution network has been enhanced significantly by implementing auto-reclosers that are justified economically. Even if the ambiguity of the input data is taken into account, SAIFI has been reduced by 59% as compared with the average reliability indices values of the existing system. In the similar way SAIDI and EENS have been decreased by 61% and 78.1% respectively.
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    Optimal Sizing of Micro-Grid: A Case Study of Kelawlo
    (Mekelle University, 2025-04-08) Goitom Tekia
    Access to reliable and cost-effective electricity is crucial for socio-economic development, yet rural off-grid communities face significant challenges in obtaining electricity due to the high cost and technical difficulties of extending the national grid. Hybrid micro-grid systems, which integrate multiple renewable energy sources, offer a sustainable alternative for decentralized electrification. This study aims to design and optimize a stand-alone micro-grid system to meet the energy demand of Kelawlo, a rural community in northwestern Tigray, Ethiopia, while minimizing costs and environmental impacts. HOMER Pro was employed as the optimization tool to determine the most cost-effective micro-grid configuration. The optimal sizing process involved estimating the electricity demand of the community based on previous studies and commonly used appliances. Solar and wind resource data for Kelawlo (latitude 14.2864, longitude 37.9611) were obtained from NASA Power, and wind speed at 10 meters was extrapolated to the wind turbine hub height using the wind shear power law. Micro-grid components, including photovoltaic (PV) modules, wind turbines, batteries, and converters, were selected and modeled, with their associated costs incorporated into the Homer Pro optimization process. Four scenarios were analyzed to determine the most suitable solution for the selected site: (1) diesel generator-only system, (2) PVbattery micro-grid system, (3) PV-wind-battery hybrid micro-grid system, and (4) PV-wind-diesel generator-battery hybrid micro-grid system. The scenarios were evaluated based on Net Present Cost (NPC), Levelized Cost of Energy (LCOE), renewable fraction, and capacity shortage. Scenario three, the PV-wind-battery hybrid micro-grid configuration, was identified as the optimal solution for the Kelawlo community, with an NPC of $505,707 and an LCOE of $0.126/kWh. This optimal system achieved a 100% renewable fraction with an allowable capacity shortage. It was determined that the PV system contributes 73% (277,937 kWh/year) of the total electricity production, while the wind turbines account for the remaining 27% (102,555 kWh/year). This study provides valuable insights into designing optimal hybrid micro-grid systems for rural electrification, contributing to sustainable energy development and reduced reliance on traditional energy sources.
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    Fuel blending Options in cement pyro processing of Messebo Cement factory by Co firing of Sawdust and Coal
    (Mekelle University, 2024-01-25) Guesh Tewele G/her
    This research assesses the technical, economic, and environmental viability of co-firing biomass fuels (eucalyptus sawdust, olea sawdust, and pine sawdust) alongside coal in cement manufacturing at Messebo Cement Factory. The evaluation emphasizes fuel blend composition, calorific values, combustion properties, and the potential for emissions reduction. Employing a mass-based fuel blend composition model, the study determines the molar composition of the blended fuels while examining their performance across 10%, 15%, and 20% co-firing ratios. Key results indicate that co-firing biomass significantly lowers CO₂ emissions compared to coal. At a co-firing rate of 10%, eucalyptus sawdust emits 0. 46426 kg of CO₂ per kg of cement, in contrast to 0. 760 kg for coal by itself. This results in a CO₂ emissions reduction of 0. 304875%, which escalates to 1. 009417% at a co-firing rate of 20% for olea and pine sawdust. The flame temperature of 1770 K (1497°C) for 10% eucalyptus co-firing satisfies kiln operational specifications, confirming its technical feasibility. From an economic standpoint, substituting 10% of coal with eucalyptus yields approximately 75,297,000 birr in annual savings, with savings rising to 150,600,000 birr at a 20% co-firing rate. The cost of eucalyptus (0. 38205 birr/kg of cement) is considerably lower than that of coal (1. 637 birr/kg of cement), leading to a 7. 67% decrease in fuel expenses at a 10% co-firing rate. Moreover, 10% eucalyptus co-firing decreases coal consumption by 5. 6%, which further boosts economic and environmental advantages. Environmental benefits encompass significant reductions in SO₂ and NOₓ emissions. For instance, 20% eucalyptus co-firing decreases SO₂ emissions by 19. 15264 units and NOₓ emissions by 12. 49865 units. Pine sawdust exhibits the greatest reduction in SO₂ (19. 77646 units at 20% co-firing), while olea sawdust achieves the most substantial reduction in NOₓ (13. 02422 units at 20% cofiring). A lower ash content (18. 67% at 20% co-firing) and minimal sulfur content in biomass further enhance combustion efficiency and lessen environmental impacts. The study also underscores the practical use of pine sawdust as an alternative fuel, which lowers the air-to-fuel ratio, excess air ratio, oxygen demands, flame temperature, as well as SO₂ and NOₓ emissions. Locally sourced pine sawdust offers benefits such as decreased transportation expenses, reduced moisture content, and less biological degradation. It can be processed and burned in a manner similar to coal or pet coke, needing only slight modifications. The thesis concludes that co-firing biomass fuels, especially pine sawdust, represents a feasible and sustainable approach to cement production, delivering considerable environmental and economic advantages. It suggests additional actions like waste heat recovery, alternative raw material usage, carbon capture, and the integration of renewable energy to further enhance system efficiency. Co-firing coal with biomass provides significant benefits for cement kiln pyro processing but necessitates precise optimization and execution suited to specific operational circumstances
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    Enhanced Solar Irradiation Forecasting Using LSTM
    (Mekelle University, 2025-03-12) Silas Gebretsadik Mebrahtom
    Accurate forecasting of solar irradiation is critical for improving grid stability, optimizing energy storage, and maximizing photovoltaic (PV) system efficiency. Traditional forecasting methods, including statistical and numerical weather prediction models, often struggle with the nonlinear and complex nature of solar irradiation data. This research work explores the application of deep learning techniques, specifically Long Short-Term Memory (LSTM) neural networks, to enhance day-ahead solar irradiation forecasting for solar energy applications. In this research, an LSTM-based model was developed and trained using historical irradiation data, covering the period from January 1984 to March 2025 in Mekelle. The methodology involved data collection, cleaning, and preprocessing, followed by model training and evaluation. Key preprocessing steps included removing anomalies and normalizing the data set using Min-Max scaling. The LSTM model demonstrated superior performance compared to traditional machine learning models, with a Mean Bias Error (MBE) of 0 kWh/m²/day, Mean Absolute Error (MAE) of 0.46 kWh/m²/day, and Root Mean Squared Error (RMSE) of 0.65 kWh/m²/day. The model demonstrated a 71% lower RMSE in winter compared to summer and achieved 56% higher accuracy on sunny days than on cloudy days. These improvements can be attributed to the more stable weather conditions of the winter season and the consistency of solar irradiation on sunny days in Mekelle. The model was evaluated using two different dataset sizes, 5556 and 15040 data points. With the larger dataset, performance improved by 15%, highlighting the critical role of data availability in enhancing model accuracy and reliability. The results suggest the potential of LSTM networks in providing reliable day-ahead solar irradiation forecasts, contributing to the broader adoption of renewable energy. This study recommends integrating the solar irradiation forecasting model with energy forecasting systems to optimize grid performance and storage utilization. Future research should explore extended datasets, additional meteorological parameters, and ensemble methods to enhance the adaptability and accuracy of LSTM-based forecasting models
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    Evaluation of irrigation water allocation for improving water use efficiency and conflict resolution in Hatset irrigation scheme, Eastern Tigray, North Ethiopia
    (Mekelle University, 2025-03-25) Lema Kiros Lema
    Efficient water allocation is crucial for enhancing irrigation water use efficiency and mitigating conflicts among users, especially regions where water is limited. This study tried to evaluate the irrigation water allocation system to optimize water allocation and improve conflict resolution mechanisms in the Hatset irrigation scheme, Eastern Tigray, North Ethiopia. The primary data was collected through soil sampling, flow measurements, household surveys, key informant interviews, and focus group discussions, and the secondary data was collected through meteorological, hydrological and spatial data. WEAP model used to assess current water allocation scenarios and propose improved strategies. Additionally, CROPWAT model was used to estimate crop water demand and HEC-HMS also used to estimate reservoir inflows. The findings reveal, unmet demand 0 m³, 0 m³, 895,360 m³ and water losses 155,721 m³, 309,160 m³, and 430,479 m³, head, middle and tail-end users respectively. Three scenarios were analyzed: a reference scenario, an improved water use efficiency scenario, and an irrigation expansion scenario. The reference scenario was business-as-usual approach and the enhanced water uses efficiency scenario demonstrated a 37.5% reduction in water demand by incorporating canal lining, efficient scheduling, and efficient irrigation techniques and the irrigation expansion scenario, which increased the irrigated area but resulted in a 23% increase in water demand with no unmet demand. Moreover, the study investigated the effect of efficient water allocation in resolving conflicts within the Hatset irrigation scheme, where infrastructure issues and governance gaps created significant disparities. It conducted how inadequate access for tail-end users caused conflict, while traditional and formal mechanisms proved ineffective due to poor coordination. The proposed hybrid approach, which integrated community driven and IWUAs, addressed systemic challenges and promoted sustainable conflict resolution.
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    Enhanced Inception ResNet V2 Model for Grape Leaf Disease Detection and Classification
    (Mekelle University, 2025-01-25) Efrem Gebrewahd Gebreslassie
    Grapes are one of the most widely consumed and globally traded crops, benefiting both agricultural economy and healthy wise. However, their vulnerability to diseases can negatively affect the quality and quantity of the grapes being produced. The most common way of detecting and classifying these disease is through the use of human experts (manual inspection method), such as pathologists and botanists. This manual inspection method is prone to error, time consuming and inefficient for large scale farms. To tackle this problem several researchers have built an automated system that detects and classifies plant diseases in a more accurate and faster way. While these studies show a reasonable result but still struggle with several issues, like poor accuracy, increased computational complexity and strong overfitting. Thus, our model addressed these issues by introducing an enhanced version of Pretrained Inception ResNet V2 architecture, By integrating a lightweight Reduction Module C which is responsible for reducing the grid size of the input image from 8 X 8 to 3 X 3 while increasing the feature maps from 1536 to 1600. This module allows the model to capture more complex features while ignoring relevant information leading to improved feature extraction capability. The proposed model achieved a superior F1 score of 99.89% on the validation set with only 0.21 million trainable parameters, outperforming EfficientNet-B4 (99.81% F1 score, 0.46 million parameters), Xception (99.73% F1 score, 1.05 million parameters), and the baseline Inception ResNet V2 (99.87% F1 score, 0.79 million parameters) in both accuracy and computational efficiency. The results show that the suggested model presents a promising solution for accurate and efficient grape leaf disease detection and classification.