Application of Automatic Control in Power System
A. Sadratdin; A.A. Sabah; M. Zaidi; K. Raed; K.A. Jamal; H.O. Al-Mansor; F. Khattab
Abstract
Over the last few decades, the majority of industrialized and developed countries have placed a strong emphasis on reducing the amount of wasted energy. In this study, electrical energy consumption is optimized by monitoring power consumption caused by residents' activities at various times of the day ...
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Over the last few decades, the majority of industrialized and developed countries have placed a strong emphasis on reducing the amount of wasted energy. In this study, electrical energy consumption is optimized by monitoring power consumption caused by residents' activities at various times of the day and storing this data in a database. An optimization algorithm was used in this study to smarten up the management of energy consumption in the building based on inhabitants' activities. The Genetic Algorithm (GA) was used to optimize the energy consumption in a smart building compared to a traditional building. Furthermore, the algorithm will enable the creation of a smart building that requires no human intervention by presenting a model based on the energy efficiency management system for the automatic operation of household equipment based on the presence of the resident scenario. The main benefit of implementing smart grid technology in the studied building was the ability to manage and monitor the energy supply and demand process. The results showed that the proposed management system in the smart building consumes less energy and power than conventional buildings. The smart building reduces energy consumption for outlets, lighting, cooling, and heating by 38%, 28%, 34%, and 33%, respectively.
Application of Automatic Control in Power System
S.A. Abdul-Ameer; A.K.J. Al-Nussairi; R. Khalid; J.K. Abbas; A.H.O. Al-Mansor
Abstract
In this study, the Particle Swarm Optimization (PSO) method was employed to optimize the anticipated energy yield of a wind farm. The architecture of a wind farm, including its location, height, and shadow reduction, is determined using the PSO algorithm based on the turbine height and rotor diameter. ...
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In this study, the Particle Swarm Optimization (PSO) method was employed to optimize the anticipated energy yield of a wind farm. The architecture of a wind farm, including its location, height, and shadow reduction, is determined using the PSO algorithm based on the turbine height and rotor diameter. The proposed model presents two potential scenarios for the wind velocity and dispersion direction originating from a level wind location. The findings indicate that the optimization of the wind farm layout, encompassing factors such as location, height based on hub and rotor diameter of turbines, and maximum energy output, leads to a reduction in the shadow effect. This is in contrast to prior methodologies that optimized only one or two elements at a time. The wind farm's output power was observed to have a significant increase (ranging between 40% and 98%), despite having the same total number of wind turbines. This increase was attributed to the utilization of different hub heights and rotor diameters in comparison to the wind farm with different hub heights and rotor diameters, but the same number of wind turbines.
