https://repository.ju.edu.et//handle/123456789/1212 2026-05-11T16:31:24Z 2026-05-11T16:31:24Z Hagos, Solomon https://repository.ju.edu.et//handle/123456789/9466 2025-04-04T06:02:01Z 2024-01-01T00:00:00Z

Modelling and Simulating of Hybrid Biomass and Solar Microgrid System for Electrification Raya Brewery PLC Hagos, Solomon This research presents a simulation and modeling analysis of a hybrid energy-based microgrid system in Raya brewery factory, which integrates solar photovoltaic panels, biogas generators, batteries, and converters. The system can meet full load of the company annual electricity demand, with PV panels contributing 75% and biogas generator 25%. The study examines the performance of a solar energy model in the Raya Brewery factory found in Mychew town of Ethiopia, using data from sites like Raya Beer. The model estimated solar radiation values, with an average monthly global radiation of 6.4 kWh/m2. From the total 1.12 MWh/day 75% (0.84MWh/day) is covered by solar energy resources and the rest 25% (0.28MWh/day) is covered by the biomass sources of power. The NMSA data reveals variations in solar radiation and energy measurements at the Mychew station, indicating suitability for solar energy applications. The Raya Bear site's Total AH needed per day requirements are 17,500 AH/day, with detailed calculations for an efficient and sustainable system. The numerical analysis of the stand-alone PV system design at Raya Bear site reveals a total daily energy requirement of 840,000 WH/day which is 75% of the total energy demand, with a total ampere-hour requirement of 26,250 AH/day. The system requires 312 PV panels and 156 modules in parallel, with a battery capacity of 103,618.421 AH/day. The solar charge controller size is 4,036.032A, and the inverter capacity is 290.15001 KW. The economic analysis shows a simple payback period of 13.8 years and an equity payback duration of 15.5 years, with the capital cost being the largest expenditure. The financial analysis demonstrates a positive net present value (NPV) and a reduction in CO2 emissions, contributing to environmental benefits. The biomass hybrid system uses a 280-kW generator with a capital cost of $140,000. The proposed scenario results in a significant reduction in greenhouse gas emissions from 9.7 tCO2/year to 1.8 tCO2/year.

2024-01-01T00:00:00Z Tizazu, Mahlet https://repository.ju.edu.et//handle/123456789/9461 2025-04-01T11:49:30Z 2024-01-01T00:00:00Z

Droop Control and Synchronization of Hybrid Microgrid Energy Management System Using Adaptive Neuro-Fuzzy control (case study; Debre Birhan specialized hospital) Tizazu, Mahlet It is well known fact that the number of populations increase from time to time throughout the world. It increases the energy demand. It can be balanced using replenished energy sources that are known as renewable energy source. Due to environmentally friendly nature and unlimited existence, they are highly applicable for generation of power. Solar and wind energy sources are the basic types of renewable energy. To obtain better energy service and improve reliability, a hybrid system is recommended that standalone system. The fitful nature of solar and wind energy sources causes a power quality and sustainability problem. As a result, a continuous monitoring, controlling, and optimization of generation system performance is required using different software’s and algorithms. This process is known as energy management system (EMS). Basically, the required power demand is efficiently supplied by a good management system. In this thesis, a droop control strategy and synchronization of wind and solar hybrid microgrid EMS is designed and presented using adaptive Neuro-fuzzy inference control system as a case study at debire birhan referral Hospital energy distribution system. In solar energy source, an adaptive neural fuzzy inference system (ANFIS) technique is used to attain a maximum power point tracking of photovoltaic panels. Whereas, proportional integral (PI) controller controls the wind energy. Moreover, a fuel cell is used as a battery for storage of charges from solar panels. The simulation results show that an effective stability and transmission of power without any interruption is obtained by using PSO optimized ANFIS algorithm. Finally, the effectiveness of PSO and ANFIS on fuel cell and PV system is compared with and without PSO.As a result the PSO optimization have good effectiveness with ANFIS controller. Hence, the required power demand is supplied effectively with an increase of reliability to the users.

2024-01-01T00:00:00Z NAMOMSA, BORCHALA https://repository.ju.edu.et//handle/123456789/9451 2025-03-31T06:38:17Z 2024-01-01T00:00:00Z

Optimized Integral-Backstepping Controller Design for Quadcopter Attitude and Position Control Using Wolf Search Algorithm NAMOMSA, BORCHALA A Quadcopter is an unmanned aerial vehicle (UAV), a small rotary-wing aircraft equipped with four identical motors and fixed-end propellers. These vehicles are characterized by multivariable, unstable, under-actuated, nonlinear, and strongly coupled, presenting significant control challenges due to wind effects and parameter uncertainties. This study proposes a novel Grey Wolf Optimization-based Integral Backstepping (GWO-IBS) controller to address these challenges effectively. A nonlinear mathematical model is developed using the Newton-Euler mechanism and transformed into a state-space representation. The integral and backstepping gains are optimized through the Grey Wolf Optimization algorithm. Lyapunov stability analysis ensures asymptotic stability. Extensive simulations using MATLAB 2021a/Simulink demonstrate the controller's capability to track a helical trajectory and maintain stability under wind disturbances over 25 seconds. Comparative analysis reveals that the GWO-IBS controller significantly outperforms the Integral Backstepping (IBS) and Sliding Mode Control (SMC) controllers, achieving lower Root Mean Square Error (RMSE) values with improvements of 81.68% for the position x-trajectory, 93.65% for the position y-trajectory, 99.86% for altitude, and substantial enhancements 48.38% for roll, 41.29% for yaw, and 88.75% for pitch angles. The results highlight the GWO-IBS controller's superior robustness and stability in handling unknown disturbances, making it a promising solution for the effective control of quad copters.

2024-01-01T00:00:00Z Barko, Etefa Belachew https://repository.ju.edu.et//handle/123456789/9445 2025-03-28T11:24:44Z 2024-01-01T00:00:00Z

Constructing a Predictive Model for Soil-Transmitted Helminths And Schistosomiasis Classification from Microscopic Images Barko, Etefa Belachew Soil-transmitted helminths and schistosomiasis are widespread parasitic diseases in tropical areas, especially in Africa, causing significant health impacts. Prompt treatment offers both health and economic benefits. Current diagnosis, mainly microscopy-based, is time-intensive and challenging in low-resource settings like Ethiopia. This study develops an innovative sys tem that analyzes parasite egg images from microscopes. Unlike previous CNN-only ap proaches, it combines machine learning and deep learning for faster, more accurate disease identification, enhancing diagnostic efficiency and reliability. This study compared predictive model with standalone deep learning for system modeling, focusing on five classes: ascariasis, hookworms, schistosomiasis, Trichuris, and negative sam ples. The dataset, from the Ethiopian Public Health Institute’s research center, contained 1,490 images (300 per class and 290 for negatives). Various image processing steps resizing, normal ization, and augmentation were applied. Models including VGG16, ResNet50, DenseNet121, MobileNetV2, EfficientNetB0, and Vision Transformer served as classifiers and feature ex tractors. Additionally, machine learning classifiers such as XGBoost, SVM, KNN, Random Forest, and Decision Trees were integrated with deep extractors for classifiers. The predictive model demonstrated higher accuracy. Strong results were obtained with SVM, where VGG16 and DenseNet121 as feature extractors led to 99.31% test accuracy. Also VGG and xgboost shows highest test accuracy of 99.35%. However, CNN-only models showed lower accuracy. VGG16 achieved 79.98% test accuracy and 83.4% training accuracy, while DenseNet121 reached 84.12% test and 88.56% training accuracy. ResNet50’s training accuracy was 92.23%, with 86.01% on testing; Ef ficientNetB0 achieved 91.80% training and 84.33% testing accuracy; MobileNetV2 reached 90.49% training and 87.02% test accuracy, and Vision Transformer recorded 93.75% training and 87.43% test accuracy. At class level Negative samples show high accuracy while others show different accuracy based on model types. These applications improve the diagnostic utility as they feed real-time information and are convenient to use in areas where even primary healthcare may not be available. Working with a small and long stored dataset posed challenges due to limited diversity and sample degradation, which hindered accu rate class distinction and affected the model’s generalization performance. To overcome dataset limita tions, collect fresh samples to increase diversity and represent all classes adequately. Implement sys tematic field collection under varied conditions, ensuring data quality. Collaborate with relevant insti tutions or stakeholders to expand the dataset, emphasizing consistency and accuracy.

2024-01-01T00:00:00Z