Electrical and Computer Engineering
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Item Distributed Power Flow Controller Based Power Quality Improvement for Grid Connected Wind Farm- Case Study Ashegoda Wind Farm(Mekelle University, 2025-09-23) Ashenafi SelemaGrid penetration level of renewable energy is growing and merging dramatically. However, poor power quality creates a major integration and operation problems. Ashegoda wind farm represents Ethiopian first large scale wind power installation, having a total rated capacity of 120 MW. The substation is equipped with two 230kv buses that interconnect Lachi and Alamata substations. In the analyzed system, both transmission lines experienced high reactive power flow approximately 43MVAr and more than 3% current total harmonic distortion (THD), which leads to additional power loss, voltage drop, equipment overheating, and network congestions. On the other hand, the on-load tap changer (OLTC) transformer used to support voltage sag, swell, under-voltage, and over-voltage has slow response time up to 10 seconds per tap, and creates transformer overheating and mechanical fatigue. To solve such integration challenges, the incorporation of a distributed power flow controller (DPFC) with in the system is necessary for improved power quality and reliability. This study analyzed and modeled the integration, impact, cost benefit analysis, and Genetic Algorithm (GA) based optimal sizing and placement of DPFC for Ashegoda wind farm. The objective was to model, simulate, and asses its impact on voltage stability, reactive power compensation, and steady state and dynamic performance. The result indicated that, without DPFC the 230kv system operated at under-voltage of 0.89 pu with 5% current THD at bus 690v. After integration of 8MVA series and 25MVA shunt DPFC controller, the voltage profile is improved to 0.96 pu and current THD is minimized to 1.8%. Integrating DPFC exhibited excellent performance in maintaining voltage stability and limiting short-circuit current levels under different fault scenarios. The cost benefit analysis was carried out over a 20-year period. The total net present value (NPV) is estimated around $15,310,316.0 US dollars, while the total investment cost amounts $7,883,000.00 US dollars. By implementing the system, Ethiopian electric utility is expected to gain $7.5 million US dollars profit without considering scrap value. Generally, the researcher proved that DPFC is technically and economically feasible. MATLAB/Simulink 2018a and Excel-2013 was employed to model, simulate, and analyze the proposed DPFC system.Item Artificial Neuro-Fuzzy Inference System (ANFIS) Based Speed Control of Separately Excited DC Motor for Load Torque Variations(Mekelle University, 2025-09-09) Kibrom ZerauSeparately excited direct Motor (SEDCM) is an electromechanical actuator used to power different loads across several industrial and domestic applications. One fundamental characteristic for controlling when driving is the motor's speed. The external load linked to the drive negatively impacts speed if the controller is weak and the load varies. Objective of this thesis work is to design an Artificial Neuro-Fuzzy Inference System (ANFIS)-based speed control mechanism for a separately excited DC motor under varying load torque conditions. The ANFIS controller integrates neural networks and fuzzy logic to improve motor speed regulation, ensuring robust performance despite disturbances in load torque. Additionally, this this work explores the effectiveness of armature voltage control (source voltage adjustment) for dynamically regulating motor speed, comparing its performance with conventional control strategies. ANFIS, fuzzy logic controllers, PID controllers, and open loop (without controller) have all been used to measure the speed of an independently stimulated SEDC motor. At first, the motor speed can be regulated and modified by changing the armature voltage (the input supply voltage).When the torque of the load grows and the transient and steady state faults rise, the motor's speed falls in the absence of a controller. The motor performs poorly as a result, and its speed will not maintain its rated level. A PID controller improves the motor's speed over an open loop, but the overall performance is still poor and there are still some transient and steady state issues. Although fuzzy logic controllers perform better than PID controllers in terms of system performance, the speed still fluctuates as the torque of the load changes. However, ANFIS better than fuzzy, PID, and open loop control systems, operates at its rated speed, has low steady state and transient errors, and keeps the motor's speed constant as the load increases. In conclusion, NFIS is superior to fuzzy and PID controllers due to its zero overshoot and reduced 38.31% settling time compared to fuzzy and 50.93 % compared to PID, reduced 37.12% rise time compared to fuzzy and 44.87 % compared to PID, and reduced 85% steady-state error compared to fuzzy and 98.5 % compared to PID. Additionally, by resolving the motor's nonlinear properties, the system's overall performance will improve.Item Voltage Control of DC-DC Boost Converter Using Lyapunov Rule Based Model Reference Adaptive Controller(Mekelle University, 2024-10-24) : Mebrahtu NgusseThe switched mode DC-DC converters are the simplest power electronic circuits that facilitate the conversion of electrical voltage from one level to another through a switching process efficiently. DC-DC boost converters are utilized for step-up voltage in various applications. The output voltage of boost converter has, oscillation, overshoot, undershoot, and steady-state error. PID controllers have been usually applied to the converters because of their simplicity to obtain the desired voltage. But, PID controller could work well in one operating condition and cannot continuously adapt the changes in the process dynamic. To overcome this problem an advanced controller is required. The proposed Lyapunov Rule Based MRAC is adaptive and non-linear controller designed to overcome the uncertainties and nonlinearities for DC-DC boost Converter under Continuous Conduction Mode (CCM) operating condition. Using MATLAB/Simulink the performance of the proposed Lyapunov rule based MRAC is compared with that of the PID controller based on the dynamic response of the system. Using PID controller, overshoot and settling time have been improved by reducing from 67.5229% to 7.7717% and 0.6985 Sec to 0.277 Sec respectively. In the case of the proposed controller (Lyapunov rule based MRAC), overshoot, settling time, and undershoot have been improved by reducing from 67.5229% to 4.5993%, from 0.6985 Sec to 0.1458 Sec, and from 0.0036% to 0.0% respectively. To test the performance of the DC-DC boost converter, it is assumed that, the input voltage has been decreased and increased from its operating point by 25% and 41.67% respectively. Also the load resistance is assumed to be decreased and increased from its operating load resistance by 25% and 20% respectively. An external disturbance is applied to the system to check how the controller handles to uncertainties and PID controller has shown deviation from the desired value but, the controller MRAC maintained the desired value.Item Comparative Analysis of Passive, PID And Fuzzy_PID Controller For Quarter Car Model(Mekelle University, 2025-09-15) Atsede GebreyohansThis paper presents a comparative analysis of passive, PID, and fuzzy PID control strategies for a quarter-car suspension system utilizing a fuzzy PID controller to enhance ride comfort and stability by effectively managing displacement and velocity. The quarter-car model serves as a simplified representation of vehicle dynamics, where the primary objective is to minimize the vertical displacement of the vehicle body and control the relative velocity between the body an the wheel. Additional PID controllers often struggle with non-linearity and uncertainties present in suspension systems; therefore, it is proposed a fuzzy logic approach to tune the PID parameters dynamically based on real-time system states. The fuzzy-PID controller integrates the benefit of fuzzy logic’s ability to handle uncertainty and the robustness of classical PID control. Simulation results demonstrate that the proposed fuzzy-PID versus passive controller significantly reduces body displacement by 88％of overshoot and 56％of settling time with bumpy input road1. Fuzzy-PID versus passive controller significantly reduces velocity response by 24％of overshoot and 55％of settling time with bumpy input road1.Item EMOTION DETECTION AND CLASSIFICATION ON TIGRIGNA SOCIAL MEDIA TEXTS USING TRANSFORMER MODELS(Mekelle University, 2025-09-23) Rahel GebruThe rapid growth of social media has reshaped emotional expression, producing large-scale digital data for social, cultural, and political analysis, thereby highlighting the importance of reliable automated emotion detection tools. Despite advances in Natural Language Processing (NLP), Tigrigna remains underrepresented, with existing multilingual models often underperforming due to limited annotated data, lack of tailored resources, and linguistic complexity. To address this gap, this study introduces transformer-based models tailored for emotion detection and classification in Tigrigna social media texts, focusing on four emotion categories: happiness, sadness, neutral, and disgust. A total of 4,000 Tigrigna sentences were collected from Facebook and YouTube and manually annotated with a high Inter-Annotator Agreement. To expand and balance the corpus, 6,000 additional sentences were generated using data augmentation techniques, including backtranslation and synonym replacement, resulting in a final dataset of 10,000 sentences. Following preprocessing, including normalization, tokenization, and cleaning, the data was split into training (8,000), validation (1,000), and testing (1,000) subsets. Three transformer-based models namely XLM-RoBERTa, tiBERT, and the Tigrigna-specific tiRoBERTa were fine-tuned and evaluated using Macro-F1, precision, and recall metrics to address class imbalance. The results demonstrated progressive improvements across models: XLM-R achieved an F1-score of 81%, tiBERT 84.4%, and tiRoBERTa 88%, with tiRoBERTa outperforming the others across all emotion categories, particularly in distinguishing subtle distinctions between sadness and happiness. Misclassifications between neutral and disgust persisted, reflecting data-related issues, model-specific challenges, and the low-resource nature of Tigrigna. Data augmentation improved F1-scores by 2–10% across models, underscoring its crucial role in enhancing performance in low-resource NLP tasks. The study concludes that transformer models, when culturally and linguistically adapted, are highly effective for Tigrigna emotion detection. Future research should expand Tigrigna-specific pretraining corpora, explore advanced augmentation, investigate hybrid architectures, and integrate multimodal data (e.g., combining text with images or videos). Applying these findings via APIs and dashboards can support researchers, policymakers, and organizations in leveraging Tigrigna social media for informed decision-making.Item Performance Evaluation of CNN, ViT, and Hybrid Models in CT Based Brain Stroke Classification(Mekelle University, 2025-08-25) Angesom BerhaneMedical image classification is the use of AI tools to automatically detect disease, and segment affected body section which ultimately assist medical experts diagnose disease easily. So far, CNNs have played a major role in the field of medical image processing. Recently, another deep learning approach known as transformers have evolved, and outperformed CNN models; especially, when trained with enough amount of data. These models were originally developed for natural language processing. But, they have shown promising results in the computer vision also. This research investigates the performance of the standalone CNN and vision transformer models, hybrid these deep learning approaches to entertain the advantage of both at the same time, evaluate performance of the hybrid models, and ends up comparing the performance of the standalone and hybrid models. The research was conducted by gathering CT scan image data of stroke disease from an online repository i.e. kaggle. Eight models were trained and evaluated, including two CNN (ConvNext, EfficientNet), two transformers (Swin, ViT), and four hybrid models (ConvNext + Swin, ConvNext + ViT, Efficient + Swin, Efficient + ViT). Several metrics such as accuracy, precision, recall, f1 score, and ROCAUC plot were used to compare the performance of the models. In general, the hybrid models have outperformed the standalone models. Specifically, the ConvNext + Swin outperformed all with an accuracy of 95.42%, and AUC of 0.99. Overall, the findings shows that hybrid models are more preferable for better accuracy in classification tasks, and Swin based architectures are less prone to over fitting.Item Forecasting-Based Operation and Maintenance Planning for Sustainable Urban Energy Supply(Mekelle University, 2025-07-29) Sa’ad Aden YousufThis study presents a comprehensive analysis of energy load forecasting and operation and maintenance (O&M) planning for domestic, industrial, and commercial sectors within a rapidly growing urban utility network. Using historical energy consumption data from the past year, forecasts were generated for the years 2024, 2025, and 2026 using different regression techniques. The one with lower value of MAPE is selected to do the load forecast for the selected site. The analysis revealed a consistent upward trend in energy demand across all sectors, with notable seasonal variations that highlight peak consumption during the summer and winter months. These findings underscore the necessity for strategic infrastructure development and improved load management practices to ensure uninterrupted power supply and system reliability. The domestic sector showed the most dynamic growth, driven by increased electrification and lifestyle improvements. Industrial and commercial sectors also demonstrated steady rises in demand, linked to economic activity and service expansion. The monthly aggregate forecast identified critical peak-load periods, particularly in May, June, and December, suggesting the need for intensified operational readiness and real-time monitoring during these months. Conversely, months like April and October with lower forecasted loads were identified as ideal windows for conducting preventive maintenance. Based on the forecast results, an optimized O&M schedule was proposed, aligning maintenance activities with seasonal demand patterns to minimize disruptions and maximize resource efficiency. Recommendations include infrastructure upgrades, integration of smart technologies, targeted staff training, and enhanced demand-side management. The study concludes by emphasizing the importance of accurate forecasting and proactive maintenance planning in building a resilient and efficient energy distribution system. Future work will focus on incorporating machine learning algorithms, real-time data, and renewable energy integration to further refine the forecasting model and optimize utility operations.Item Performance Evaluation of Ashegoda Wind farm using Key Performance Indicators and Proposing Mitigation Measures(Mekelle University, 2025-09-02) Tesfay GebremedhinThe global rise in electricity demand has driven many countries to use fossil fuels; however, environmental concerns have shifted focus toward renewable energy. Ethiopia is leveraging its renewable energy potential, particularly wind energy, with an estimated 10 GW capacity. As part of this initiative, the Ashegoda wind farm, become operational since 2013/14 with a 120 MW capacity. However, data from four Ethiopian fiscal years (EFY) indicate a notable decline in energy output, particularly in the final two years of the study. This thesis evaluates the wind farm’s performance using KPIs and proposes mitigation strategies based on the findings. Wind speed data collected from the SCADA system were processed using Excel and IBM SPSS after missing data were estimated using the Moving Average (MA) model and monthly averages. The analysis revealed the annual average wind speeds at the site are 6.52 m/s (2015/16), 6.96 m/s (2016/17), 7.45 m/s (2017/18), and 7.38 m/s (2018/19), the same all phases. Gross and net energy outputs were estimated using frequency distributions and the turbine power curve, adjusted for site air density and losses. Based on P50 exceedance, estimated net outputs for 2017/18 and 2018/19 were 233 GWh and 229 GWh, respectively. In contrast, the actual measured outputs for the same years were 61.5 GWh and 88.6 GWh, highlighting a significant gap between potential and actual generation. Performance indicators showed low values: capacity factors of 5.86% and 8.43%, energy-based availability of 26.4% and 38.75%, and time-based availability of 35.12% and 11.93% in 2017/18 and 2018/19, respectively. These figures indicate underperformance, resulting in energy losses of 171.73 GWh and 140 GWh which are mainly caused by long downtime. To improve performance, the study proposes mitigation strategies, including better spare part management, improved SCADA reliability, and optimized maintenance. In conclusion, the Ashegoda wind farm is underperforming relative to its design expectations. Implementing the proposed mitigation strategies is essential to enhance its operational efficiency.Item Design of a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)-based Linear Quadratic Gaussian (LQG) regulator for a Series Double Inverted Pendulum on a Cart(Mekelle University, 2024-04-11) Solomon TeklehaimanotThis thesis focuses on the design of a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)-based Linear Quadratic Gaussian (LQG) regulator for a Series Double Inverted Pendulum on a Cart (SDIPC). The primary issues motivating the design of this regulator are the unstable behavior of the SDIPC, the slow settling times, and the significant steady-state errors observed in previously designed regulators. For this plant model, the LQG is developed using a cascaded combination of a GA and PSO-tuned Linear Quadratic Regulator (LQR) and a full-state observer, based on the separation principle theory of control systems. This approach is optimal for fully stabilizing the SDIPC and can withstand process and measurement noise through a Kalman filter. The entire system is designed in MATLAB®/SIMULINK®. Simulation results demonstrate that the PSO-tuned LQG achieves faster settling times and smaller steady-state errors compared to the GA-tuned LQG. With the PSO-tuned LQG, the steady-state errors for the upper pendulum angle, lower pendulum angle, and cart position are reduced to 1.033 × 10⁻⁴ rad, 4.206 × 10⁻⁵ rad, and 2.920 × 10⁻⁴ m, respectively. Additionally, the settling times for the upper pendulum angle, lower pendulum angle, and cart position are 0.897 s, 0.711 s, and 0.780 s, respectively. Thus, the regulator design objectives are successfully achieved with the PSO-tuned LQG than with GAtuned LQG.Item Design and Optimization of Bamboo/Glass Fiber Reinforced Epoxy Composites for Sustainable Wall Panel Application(Mekelle University, 2024-11-12) Tedros TilahunEstimating the angle of arrival (AoA) of a coming signal can accomplished using various methods. In most cases algorithms are used for such purposes. However, algorithms are naturally complicated and expensive, and also cause a degradation in system performance. Therefore, other methods such as, 1800-hybrid rat race (HRR) coupler can be applied for effectively estimating the AOA of a coming signal. In this thesis work, an 1800 HRR coupler integrated with a 2x1 closely-spaced patch antenna array and a negative permeability metamaterial was studied for estimating AoA of a coming signal. The 1800 HRR was made up of a ring metallic sheet integrated with four additional branches placed at the edges of it. It operates at 10 GHz so as to make compatible with the 2x1 patch antenna array’s operating frequency. The simulation results show, the 1800 HRR coupler is characterized by 00- phase at the sum (Σ)-port while 1800 phase shift at the difference (Δ) port at the given operating frequency. In order to integrate with the 1800 HRR, a 2x1 array patch antenna with an inter - element distance of 0.6λ (where λ is the operating wave length) was designed. The antenna array workes at 10GHz with a maximum simulated gain of 8.824 dB while keeping the mutual coupling to a minimum of -23 dB. To further achieving miniaturization, the inter-element distance reduced to 0.4λ. The simulation result shows a resonance at 10 GHz frequency and maximum gain of 7.8 dB while the mutual coupling increased to -9 dB. The 2x1 patch antenna array with inter - element distance of 0.6λ -1800 HRR coupler system was able to estimate the AoA of the received signal from 00 to 190 with error of less than 50. While with a reduced inter – element distance to 0.4λ, the system was able to estimate signals from 00 to 500 with error of less than 50. Upon integrating split ring resonator (SRR) met materials, mutual coupling reduced to -15.6 dB without affecting the AOA of the system. This study was able to estimate AOA in a wide range of an incoming signal while keeping the inter – element distance smaller. The proposed design can be applied in radar system applications where accurate estimation of AOA of an incoming signal is needed such as in target tracking, surveillance, and navigation missions.