Electrical Engineering
http://repository.dkut.ac.ke:8080/xmlui/handle/123456789/44
Electrical Engineering2024-03-28T10:48:50ZEnhancing Energy Efficiency in CF mMIMO Systems Using Hybrid Transmit Power Control and Optimal Power Allocation Algorithms
http://repository.dkut.ac.ke:8080/xmlui/handle/123456789/8484
Enhancing Energy Efficiency in CF mMIMO Systems Using Hybrid Transmit Power Control and Optimal Power Allocation Algorithms
Kunje, Chimwemwe Emily; Manene, Franklin Muriuki; Langat, Philip Kibet
As real-time access and high-capacity requirements in wireless communication networks increase rapidly, solutions
must balance the complex relationship between Spectral Efficiency (SE), and Energy Efficiency (EE) metrics. The proposed
approach emphasizes the importance of combining Transmit Power Control (TPC), and Optimal Power Allocation (OPA)
methods to achieve optimal results. The basic premise is that the hybrid algorithm will boost EE while retaining an acceptable
level of SE. The CF mMIMO technology is first tested in a controlled setting without TPC and OPA. A hybrid algorithm
combining TPC (Max-min EE) and OPA (Sum SE maximization) is then created, and EE and SE are optimized in the hybrid
algorithm. The mixed technique is found to outperform the individual TPC and OPA algorithms. With an unparalleled
33,263,040.4068 bits/Joule, the hybrid algorithm boosts average EE. The hybrid algorithm also exceeds the targeted SE of 21
bits/s/Hz, demonstrating its capacity to balance EE and SE. This study advances the theory of CF mMIMO systems and offers
practical insight into energy-efficient wireless communication. Future research and development for sustainable and highperforming wireless networks can build on these insights.
2024-02-01T00:00:00ZDSAIL-TreeVision: A software tool for extracting tree biophysical parameters from stereoscopic images
http://repository.dkut.ac.ke:8080/xmlui/handle/123456789/8461
DSAIL-TreeVision: A software tool for extracting tree biophysical parameters from stereoscopic images
Kiplimo, Cedric; Epege, Collins Emasi; Maina, Ciira Wa; Okal, Billy
Reliable forest data is crucial for policy and investment decisions, hence the need to monitor forests. Tree harvesting policies, informed by tree attribute data, can help prevent excessive logging. The use of stereoscopic vision for estimating tree attributes is an active field of research. DSAIL-TreeVision is open-source software for extracting tree diameter at breast height, crown diameter, and height from stereoscopic images. It is written in Python and comprises three modules for capturing and storing images, calibrating single and stereo cameras, and extracting the tree attributes. While other open-source software implementing stereoscopic vision for other applications exists, no software for estimating these tree attributes exists. DSAIL-TreeVision was developed to fill this gap and has been tested in a real forest setting and on the DIML/CVL RGB-D dataset.
2024-02-01T00:00:00ZTransformer Hot Spot Temperature Estimation through Adaptive Neuro Fuzzy Inference System Approach
http://repository.dkut.ac.ke:8080/xmlui/handle/123456789/8419
Transformer Hot Spot Temperature Estimation through Adaptive Neuro Fuzzy Inference System Approach
Mharakurwa, Edwell. T.; Gicheru, Dorothy. W.
Transformer performance and efficiency can be enhanced by effectively address the properties of its
insulation system. The power transformer insulation system weakens as a result of operational thermal stresses
brought on by dynamic loading and shifting environmental patterns. Winding hot spot temperature is a crucial
metric that must be maintained below the prescribed limit while power transformers are operating so as to
maintained power system reliability. This is due to the fact that, among other variables, the time-dependent aging
effect of insulation depends on transitions in hot spot temperatures. Due to the nonālinear nature of the
conventional mathematical models used to determine these temperatures, and complexity of thermal
phenomena, investigations still need to be exercised to fully understand the variables that associate with hot spot
temperature computation with minimum error. This paper explores the possibilities of enhancing top oil and hot
spot temperature estimation accuracy through the use of an adaptive neuro-fuzzy inference (ANFIS) technique.
The paper presents an adaptive neuro fuzzy model to approximate the hot spot temperature of a mineral oilfilled power transformer based on loading, and established top oil temperature. Initially, a sub-ANFIS top oil
temperature estimation model based on loading and ambient temperature as inputs is established. Using a hybrid
optimization technique, the ANFIS membership functions were fine-tuned throughout the training process to
reduce the difference between the actual and anticipated outcomes. The correctness and reliability of the created
adaptive neural fuzzy model have been verified using real-world field data from a 60/90MVA, 132kV power
transformers under dynamic operating regimes. The ANFIS model results were validated against field measured
values and literature-based electrical-thermal analogous models, establishing a precise input-output correlation.
The developed ANFIS model achieves the highest coefficient of determination for both TOT and HST (0.98 and
0.96) and the lowest mean square error (7.8 and 10.3) among the compared thermal models. Correct
determination of HST can help asset managers in thermal analysis trending of the in-service transformers,
helping them to make proper loading recommendations for safeguarding the asset.
2023-02-01T00:00:00ZOptimal Switching Sequence Using an Improved Metaheuristic Technique in a Distribution Network System with Fixed DG Units
http://repository.dkut.ac.ke:8080/xmlui/handle/123456789/8367
Optimal Switching Sequence Using an Improved Metaheuristic Technique in a Distribution Network System with Fixed DG Units
Juma, Shaibu Ali; Mtonga, Thomson; Mharakurwa, Edwell T.; Nyachionjeka, Kumbirayi
Integration of renewable DGs in distributed
networks has become a critical issue in meeting the increasing
electricity demand while considering economic and
environmental impacts. DGs integrated into the electric
distribution network assist with voltage improvement and
power loss mitigation. However, it may also come with other
technical challenges such as stability, reliability and power
quality issues if poorly designed. This would lead to worsening
the performance of the power system and the degradation of
network components. Optimal network reconfiguration has
proven to be very beneficial in effective reduction of power
system losses when simultaneously employed with DG units.
This research reports on the development and valuation of a
proposed metaheuristic optimum network reconfiguration
method for total real power loss decrease in radial distribution
schemes with fixed DG units. The proposed method was
designed in MATLAB and the IEEE 33-bus radial distribution
system was employed in assessing its performance.
2023-12-01T00:00:00Z