Advanced Technologies in Power Grids Optimization using IoT and AI: Energy efficiency using the Internet of Things and the Artificial Intelligence - Softcover
Synopsis
This work presents a method for optimizing the performance of energy systems using the Internet of Things and artificial intelligence. The method automatically detects voltage dips. Fault correction is performed automatically in real time by reconfiguring and automatically recombining switches across the entire system. This technique provides an easy way to supervise power networks using SCADA devices. Adaptive particle swarm optimization (APSO) algorithms are proposed to evaluate power losses and voltage dips (PLVD) on the radial distribution system (RDS). The IEEE 33 bus standard test is used to study the power quality of the proposed system. Three suitable locations for the injection of photovoltaic distributed sources (PDS) are determined based on the reduction in power loss and the voltage index. The system rectifies faults such as power factor deviation (PFD) or partial shading of solar cells by reconfiguring and automatically recombining the 33-bus radial system branches. An Adaptive particle swarm optimization (APSO) has enabled the power profile to be improved and demonstrated the reliability of the proposed method.
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Advanced Technologies in Power Grids Optimization using IoT and AI
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Advanced Technologies in Power Grids Optimization using IoT and AI
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Advanced Technologies in Power Grids Optimization using IoT and AI
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Advanced Technologies in Power Grids Optimization using IoT and AI (Paperback)
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LAP Lambert Academic Publishing, 2025
ISBN 10: 6208456738
ISBN 13: 9786208456733
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Paperback. Condition: new. Paperback. This work presents a method for optimizing the performance of energy systems using the Internet of Things and artificial intelligence. The method automatically detects voltage dips. Fault correction is performed automatically in real time by reconfiguring and automatically recombining switches across the entire system. This technique provides an easy way to supervise power networks using SCADA devices. Adaptive particle swarm optimization (APSO) algorithms are proposed to evaluate power losses and voltage dips (PLVD) on the radial distribution system (RDS). The IEEE 33 bus standard test is used to study the power quality of the proposed system. Three suitable locations for the injection of photovoltaic distributed sources (PDS) are determined based on the reduction in power loss and the voltage index. The system rectifies faults such as power factor deviation (PFD) or partial shading of solar cells by reconfiguring and automatically recombining the 33-bus radial system branches. An Adaptive particle swarm optimization (APSO) has enabled the power profile to be improved and demonstrated the reliability of the proposed method. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9786208456733
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Advanced Technologies in Power Grids Optimization using IoT and AI
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Advanced Technologies in Power Grids Optimization using IoT and AI
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ISBN 10: 6208456738
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Advanced Technologies in Power Grids Optimization using IoT and AI
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Advanced Technologies in Power Grids Optimization using IoT and AI (Paperback)
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Paperback. Condition: new. Paperback. This work presents a method for optimizing the performance of energy systems using the Internet of Things and artificial intelligence. The method automatically detects voltage dips. Fault correction is performed automatically in real time by reconfiguring and automatically recombining switches across the entire system. This technique provides an easy way to supervise power networks using SCADA devices. Adaptive particle swarm optimization (APSO) algorithms are proposed to evaluate power losses and voltage dips (PLVD) on the radial distribution system (RDS). The IEEE 33 bus standard test is used to study the power quality of the proposed system. Three suitable locations for the injection of photovoltaic distributed sources (PDS) are determined based on the reduction in power loss and the voltage index. The system rectifies faults such as power factor deviation (PFD) or partial shading of solar cells by reconfiguring and automatically recombining the 33-bus radial system branches. An Adaptive particle swarm optimization (APSO) has enabled the power profile to be improved and demonstrated the reliability of the proposed method. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9786208456733
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Advanced Technologies in Power Grids Optimization using IoT and AI
Published by
LAP Lambert Academic Publishing, 2025
ISBN 10: 6208456738
ISBN 13: 9786208456733
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Advanced Technologies in Power Grids Optimization using IoT and AI
Published by
LAP LAMBERT Academic Publishing Sep 2025, 2025
ISBN 10: 6208456738
ISBN 13: 9786208456733
New
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This work presents a method for optimizing the performance of energy systems using the Internet of Things and artificial intelligence. The method automatically detects voltage dips. Fault correction is performed automatically in real time by reconfiguring and automatically recombining switches across the entire system. This technique provides an easy way to supervise power networks using SCADA devices. Adaptive particle swarm optimization (APSO) algorithms are proposed to evaluate power losses and voltage dips (PLVD) on the radial distribution system (RDS). The IEEE 33 bus standard test is used to study the power quality of the proposed system. Three suitable locations for the injection of photovoltaic distributed sources (PDS) are determined based on the reduction in power loss and the voltage index. The system rectifies faults such as power factor deviation (PFD) or partial shading of solar cells by reconfiguring and automatically recombining the 33-bus radial system branches. An Adaptive particle swarm optimization (APSO) has enabled the power profile to be improved and demonstrated the reliability of the proposed method.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 72 pp. Englisch. Seller Inventory # 9786208456733