http://repository.mut.ac.ke:8080/xmlui/handle/123456789/2932 2026-04-07T13:12:34Z http://repository.mut.ac.ke:8080/xmlui/handle/123456789/5545 Efficient Control strategy based on instantaneous power theory and model predictive control for grid connected photovoltaic system Soumana, R. A.; Saulo, M. J.; Muriithi, Christopher M. Due to the rapid decline of fossil resources and the impact of their use for electric power generation on the environment, renewable energy sources are increasingly explored and integrated into the power grid. Among the Renewable energy sources, photovoltaic (PV) systems are one of the most integrated into the utility grid. Thus, this paper presents a control scheme based on instantaneous power theory (IPT)and model predictive control (MPC) to inject the PV power into the grid at unity power factor with minimum current harmonics. The proposed control strategy is applied to a two-stage grid connected PV system which employs boost converter and two-level voltage source inverter. The current references are obtained in the dq reference frame based on IPT. A finite control set model predictive control (FCS-MPC) is used to control the inverter current in order to inject with high accuracy the current references into the grid. The effectiveness and the performance of the proposed control strategy is confirmed by MATLAB/Simulink under various solar irradiance level. 2021-11-01T00:00:00Z http://repository.mut.ac.ke:8080/xmlui/handle/123456789/5544 A Voltage Stability Constrained Optimal Power Flow using Multi-objective Particle Swarm Optimization Algorithm Kyomugisha, R.; Muriithi, Christopher M.; Edimu, M. As the global demand for energy rises, power system networks are teetering on the verge of collapsing owing to a compromise in system stability. During system disturbances, the network's inability to supply adequate reactive power causes instability and eventual collapse. As such, optimized generation scheduling during system disturbances can improve the utilization of the power plants while lowering power loss, improving voltage regulation, reducing branch loading, and ensuring the secure operation of system equipment. Since power systems have conflicting and multiple objectives, this study proposes a multiobjective optimal power flow incorporating three objective functions: generation cost, power loss, and the maximum value of the line Voltage Collapse Proximity Index. The Multiobjective Particle Swarm Optimization Algorithm is used to minimize these objectives on the IEEE 30-bus system for different case studies in normal, contingency, and stressed system conditions. Fuzzy Decision Theory is utilized for obtaining the best compromise solutions amongst a set of Pareto optimal solutions. The results show that the voltage stability of the system is improved by an average of 63.09% during system disturbances with multiobjective optimization. Simultaneous optimization of the three objective functions provides the most voltage stable condition for all system conditions, preventing possible collapse. 2021-11-01T00:00:00Z http://repository.mut.ac.ke:8080/xmlui/handle/123456789/5543 An Assessment of Optimal Allocation of FACTS Devices in Power Systems Using Metaheuristics Kallon, M. A.; Nyakoe, G. N.; Muriithi, Christopher M. The increasing power demand, the optimal use of grids, the need for economic efficiency, and the high costs associated with building new grids have created unavoidable challenges such as power line overload and excessive power transmission, voltage instability, high losses, low power quality, voltage profile problems, and reliability issues. Flexible alternating current transmission systems (FACTS) devices have proven to be highly effective and viable in mitigating the above challenges in transmission systems. However, the type, location, and capacity of FACTS devices should be properly optimized to maximise their resulting benefits. The problem of knowing the optimal type, size, and position of FACTS controllers in power systems, known as FACTS allocation problem, has attracted the attention of many electrical engineering researchers. Analytical techniques have insufficient computation precision in determining the optimal allocation of FACTS devices, also, arithmetic programming methods are often not effective in managing constrained optimization problems. On the other hand, metaheuristics approaches are random population-based optimization algorithms that are highly effective in dealing with multimodal high constraints, multi-objectives, and discrete systems. Metaheuristics are known to be the most commonly used methods to determine the optimal allocation of FACTS devices. In this paper, applications of different metaheuristics for solving FACTS devices allocation problem are deeply assessed. The assessment is limited to FACTS devices and the optimal allocation of these devices using metaheuristics. 2021-11-01T00:00:00Z http://repository.mut.ac.ke:8080/xmlui/handle/123456789/5542 The Impact of Prior Exposure to Engineering Through the MUT Pre-College Course - A Case Study of Kangema Sub-County Secondary Schools Mwangi, P. N.; Mutinda, F. N.; Githinji, E.; Njuguna, V.; Barasa, A.; Cheruiyot, S.; Kipkogei, G.; Mwaniki, C.; Karuru, W.; Okaya, M.; Muriithi, Christopher M. In Kenya, secondary schools have a great role in preparing learners for career progression. In order to realize industrial growth, it is important to prepare more students for careers in STEM. There is relatively little research that exists on the impact of prior exposure to Engineering through pre- college sessions to students' attitude in STEM subjects. In addition, Industry 4.0 requires that the 21st century student be exposed to current trends in the industry. The purpose of this research is to investigate the impact of the pre-college sessions as a mode of prior exposure to Engineering to secondary school students on learning STEM subjects. The pre-college exposure course entailed introducing the students to green energy through Solar photovoltaic systems, automation using Arduino, advanced manufacturing through 3D printing and robotics. The research was conducted in secondary school students from Kangema subcounty. The target population is Form 1 and Form 2. In this research, the first cohort entailed 30 students who were selected from 3 secondary Schools through stratified, systematic and purposive sampling. The students were taken through the pre-college sessions. The study explored the impact of the precollege sessions to the attitude learning of STEM subjects. The study established that the students exhibited an improved attitude in learning of the STEM subjects. 2021-11-01T00:00:00Z