Advanced Analytics for Reliability and Resilience of Energy System

Häftad, Engelska, 2026

AvFausto Pedro Garcia Marquez,Ren� Vinicio S�nchez Loja,Mayorkinos Papaelias

2 429 kr

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Advanced Analytics for Reliability and Resilience of Energy Systems prepares students, researchers, and industry engineers to design and maintain reliable, sustainable energy systems using state-of-the-art AI techniques. This book provides a clear foundation in the fundamentals of power systems, statistics, and reliability, including resilience principles and strategies, practical applications, and real-world solutions. The AI tools presented range across forecasting, the Internet-of-Things, machine learning, digital twin technology, and big data analysis, with a variety of applications to avoid power outages, minimise disruption, and accurately assess system resilience. Including case studies and details methodology for practical techniques, Advanced Analytics for Reliability and Resilience of Energy Systems helps energy systems engineers and researchers to provide a stable and consistent power supply, in the face of climate change challenges and the energy transition

Provides a foundation with the fundamental principles and strategies for reliability and resilience
Introduces the readers to a toolbox of AI methodologies with a variety of applications
Leverages clear chapter objectives, worked problems, and detailed case studies to support real-world solution-building

Produktinformation

Utgivningsdatum2026-01-15
Mått152 x 229 x 25 mm
Vikt450 g
FormatHäftad
SpråkEngelska
Antal sidor484
FörlagElsevier Science
ISBN9780443247224

Tillhör följande kategorier

Energiteknik inom Naturvetenskap och teknik
Artificiell intelligens inom Data och IT

Fausto Pedro García Márquez works as a Professor and as Director of the Ingenium Research Group at the Universidad De Castilla-La Mancha, Spain. He is an Honorary Senior Research Fellow at Birmingham University, UK, and a Lecturer at the Postgraduate European Institute. He has published numerous papers and books with international publishers of repute, and has been involved as a principal investigator in numerous international projects. His main interests are: Artificial Intelligence, Maintenance, Management, Renewable Energy, Transport, Advanced Analytics, and Data Science. René Vinicio Sánchez Loja is a Professor at the Universidad Politécnica Salesiana, Ecuador, working mainly in areas related to the automation of sequential processes. In 2014, he founded the Research and Development Group in Industrial Technologies, and Overseas invited Ph.D. at Chongqing Technology and Business University, China. He is a senior member of IEEE. He has extensive experience in the organization of conferences, implementation of technological projects, management and execution of research projects; currently he has more than 60 publications in Web of Science. His current focus is in the areas of project management, condition-based maintenance, engineering education and industry 4.0 especially for SMEs. Mayorkinos Papaelias is a Reader in NDT and Condition Monitoring in the School of Metallogy and Materials at the University of Birmingham, UK. Dr Papaelias leads the research activity in Non-Destructive Testing and Structural Health Condition Monitoring at the Birmingham Railway Centre for Research and Education and conducts research in structural health condition monitoring of wind turbine towers, and advanced condition monitoring of wind turbine gearboxes and rotating machinery. He served as a technical consultant to TWI, ENGITEC, Innovative Technology and Science Ltd, and Instituto de Soldadura e Qualidade. He is editor of two books on fault detection and condition monitoring, and has contributed chapters to books in fault detection and rail inspection. Mayorkinos is chairman of the Education Committee of the International Society for Condition Monitoring of the British Institute of Non-Destructive Testing.

Introduction1. Enhancing Resilience of Power Transmission Systems Against Typhoon Disasters: A Hybrid Data-Model Driven Approach2. Reliability of Power Electronics in Smart Grids3. Electricity distribution grids resilience enhancement by network reconfiguration4. Teaching learning-based optimized artificial neural network for predicting the maximum power point of a large-scale grid-connected solar PV system5. A reliable PV system based on FPPT implementation incorporating forecasted load demand using Neural Network6. Robust Stabilization Ellipsoidal Design for Normal and Contingency Operated Power Systems Using Markov Jump7. From Review to Revolution: Innovative Under-Frequency Load Shedding for Enhanced Power System Resilience8. Fault Detection and Classification in Hybrid AC/DC Systems Using Artificial Neural Networks9. A Reliability Constrained Load Balancing Procedure for Neutral Current Reduction in Distribution Systems10. The impact of cyber network configuration on the dynamic-thermal failure of transformers considering distributed generator controller11. Operation of Energy Hub to Enhance Power System Resilience12. Resilient Operation of Power and Gas Networks to Service Restoration13. Challenges and Solutions in Power Sharing Control of Microgrids with Integrated Renewable Energy Sources14. Statistical Analysis of Supervisory Control and Data Acquisition System for Maintenance Management of Photovoltaic Solar Power Plant15. Small-Signal Stability Analysis and Compensation Control for DC Networked-Microgrid under Multiple Time Delays16. Multiplexing Power Supply Networks with DC Sub-Grid: Control Strategy and Economic Impact Assessment