Application of Automatic Control in Power System
A.S. Altuma; R. Khalid; A.I. Alanssari; A. Hussien; Y.S. Mezaal; K. Al-Majdi; T. Alawsi
Abstract
Insufficient synchronization between the operational efficiency of capacitors and tap-changer transformers in regulating voltage presents a fundamental challenge in distribution networks, which in turn hinders the control performance. This challenge is caused by the inability of these two components ...
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Insufficient synchronization between the operational efficiency of capacitors and tap-changer transformers in regulating voltage presents a fundamental challenge in distribution networks, which in turn hinders the control performance. This challenge is caused by the inability of these two components to synchronize their respective operations properly. In this study, a novel control strategy is presented with the objective of achieving synchronization in the functioning of capacitors and tap transformers. Depending on the load change, various devices can be used to control the distribution network voltage. On Load Tap Changers (OLTCs) and Capacitor Banks (CBs) respond slowly to voltage changes. If the voltage changes rapidly, such devices are useless and should be avoided. Keying may shorten lifespan. This study investigated a new optimal control mechanism for coordinating tap transformers and capacitors. The optimization of tap trans- and capacitor-stage operation through the use of a Genetic Algorithm (GA) results in the reduction of superfluous switching. The limits for Point of Common Coupling (PCC) bus voltage and power factor are 0.94 and 1.02 per unit, respectively. The secondary control stage regulates the voltage of the feeder bus within the range of 0.95 to 1.05 per unit. Following the second-stage regulation of the terminal buses in the N network feeder, the third stage governs the PCC bus voltage. To prevent an infinite control loop, the voltage of the PCC bus is regulated within the range of 0.95 to 1.05 per unit (PU). These findings indicate that the optimization model is capable of achieving maximum efficiency in controlling the voltage of the distribution network. In the interim, this optimization technique produces outcomes of greater accuracy, as evidenced by a voltage value that remains consistently close to unity [Root Mean Square Error (RMSE) = 0.85] across a broad spectrum of network-loading scenarios.
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
А. Sadratdin; W.K. Al-Azzawi; B.M. Ali; A.N. Obeed; N.A. Hussien; A.M. Shareef; Kadhum Al-Majdi; A.S. Ibrahim
Abstract
This study investigates a hybrid electric system that utilizes novel energy sources and is subject to variable production and uncertainty. The study proposes a multi-objective optimization methodology using Genetic Algorithm (GA) to optimize energy source consumption and utilization, accounting for variations ...
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This study investigates a hybrid electric system that utilizes novel energy sources and is subject to variable production and uncertainty. The study proposes a multi-objective optimization methodology using Genetic Algorithm (GA) to optimize energy source consumption and utilization, accounting for variations in production/load levels across different time intervals. The proposed approach enables the end-user to achieve desired operational outcomes while adhering to specified constraints, taking into account both economic constraints and environmental considerations. The study explores the implementation of intelligent electric energy management in a model electric motor system that incorporates various electric energy generators, including solar cells, fuel cells, micro-turbines, and batteries. The optimization problem was formulated with multi-objectives of minimizing operating cost and environmental pollution. The presented approach demonstrated that the energy management system or electrical system operator is a proficient mechanism. Ultimately, the investigation has resulted in the development of an intelligent energy management system aimed at enhancing the efficiency of the energy production and storage sampling and planning system. The findings of the optimization clearly demonstrate an inverse link between the operating costs and pollution emissions in the system under study.
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.
Application of Automatic Control in Power System
A. Akbarimajd; M. Olyaee; H. Shayeghi; B. Sobhani
Abstract
This paper aims to design an optimal distributed multi-agent controller for load frequency control and optimal power flow purposes. The controller parameters are optimized using Grey Wolf Optimization (GWO) algorithm. The designed optimal distributed controller is employed for load frequency control ...
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This paper aims to design an optimal distributed multi-agent controller for load frequency control and optimal power flow purposes. The controller parameters are optimized using Grey Wolf Optimization (GWO) algorithm. The designed optimal distributed controller is employed for load frequency control in the IEEE 30-bus test system with six generators. The controller of each generator is considered as one agent. The controllers of agents are implemented in a distributed manner that is control rule of each agent depends on the agents’ own state and the states of their neighbors. Three other types of controllers including centralized controller, decentralized controller, and optimal centralized controller are considered for comparison. The performances of decentralized and distributed controllers are compared with two centralized controllers. In the optimal centralized controller and optimal distributed controller, the objective function is considered to achieve the objective of load frequency control as well as minimize power generation. Simulation results using MATLAB/SIMULINK show that although there is no global information of system in the optimal distributed controller, it has suitably reduced the frequency deviation. Meanwhile the power is optimally generated in the three scenarios of load increasing, load reduction and generator outage.
Application of Automatic Control in Power System
N. Zendehdel
Volume 3, Issue 1 , June 2015, , Pages 1-22
Abstract
This paper presents a comprehensive robust distributed intelligent control for optimum self-healing activities in smart distribution systems considering the uncertainty in loads. The presented agent based framework obviates the requirements for a central control method and improves the reliability of ...
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This paper presents a comprehensive robust distributed intelligent control for optimum self-healing activities in smart distribution systems considering the uncertainty in loads. The presented agent based framework obviates the requirements for a central control method and improves the reliability of the self-healing mechanism. Agents possess three characteristics including local views, decentralizations and autonomy. The message, exchanged among neighboring agents, is used to develop a global information discovery algorithm and updates the topology information of out-of-service areas, available supply capacity and routing information. Fuzzy description is employed to take into account the uncertainties of measurements in which are exchanged between agents. Moreover, to find the optimal restoration plan, incorporating the discovered data, a routing problem is developed as a fuzzy binary linear optimization problem. This problem is approached by a novel method using a specific ranking function. Finally, robustness and applicability of the proposed self-healing method is tested on two standard case studies. The obtained results emphasize that ignoring the uncertainties may lead to non-realistic solutions.
Application of Automatic Control in Power System
Z. Moravej; S. Bagheri
Volume 3, Issue 1 , June 2015, , Pages 71-82
Abstract
Power transformers provide a vital link between the generation and distribution of produced energy. Such static equipment is subjected to abuse during operation in generation and distribution stations and leads to catastrophic failures. This paper reviewed the techniques in the field of condition monitoring ...
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Power transformers provide a vital link between the generation and distribution of produced energy. Such static equipment is subjected to abuse during operation in generation and distribution stations and leads to catastrophic failures. This paper reviewed the techniques in the field of condition monitoring of power transformers in recent years. Transformer monitoring and diagnosis are the effective techniques for preventing the eventual failures and contributing to ensure the plan’s reliability. This paper provided a survey on the existing techniques for monitoring, diagnosis, condition evaluation, maintenance, life assessment and possibility of extending the life of the existing assets of power transformers with be appropriate classifications. Thus, this survey could help researchers through providing better techniques for condition monitoring of power transformers.
