Alfredo Vaccaro

Wind power is unanimously recognized as one of the major drivers of the energy transition. Increasing renewable power generation introduces significant challenges for both the operation and planning of power systems, driven by the intrinsic uncertainty of stochastic renewable resources and by the growing spatial distribution of generation assets. Wind power presents a distinctive set of challenges in this context. Wind turbines are complex machines operating under highly non-stationary conditions and are composed of tightly coupled mechanical, electrical, and electronic subsystems. In order to ensure reliable power system operation and to minimize the levelized cost of energy, it is essential to continuously monitor the health status of wind turbines, and to improve the efficiency of wind energy conversion as much as possible. Artificial intelligence has the potential to help address these challenges. The objective of this book is to address the gap between domain expertise in wind energy and the rapid proliferation of machine learning techniques. While advanced data-driven models offer unprecedented flexibility and predictive capabilities, their increasing complexity can come at the cost of transparency, physical interpretability, and engineering insight. Bridging this gap demands a critical understanding of the problem at hand, a clear definition of the operational objective, and a conscious selection of the most appropriate techniques compatible with the available data sources. Offering concise but thorough coverage of the topic, AI for Wind Turbine Performance and Condition Monitoring explores data sources from turbines and fleets, reviews the fundamentals of ML, then covers AI-based wind turbine performance analysis, AI-based detection of static misalignment and sensor errors, and condition monitoring for wind turbine maintenance. Wind power researchers in academia and industry, grid operators, and maintenance managers will find this book offers a valuable overview and analysis of AI-based methodologies for wind generators.

Integrating intermittent distributed generation, distributed storage systems, electric vehicles, and flexible loads will present security, stability, and power quality challenges in future smart grids. The amount of data to be processed to face these issues can overwhelm grid operation tools and conventional IT-based applications, limiting situational awareness and decision support. Decentralized and self-organizing technologies can help with that problem. In a self-organizing system, information processing is based on local interactions of its elementary parts (dynamic agents), enabling the cooperative solution of complex decision-making problems by only requiring local information exchange without needing a fusion center for data collection and processing. Self-Organizing Dynamic Agents for the Operation of Decentralized Smart Grids describes the technology of cooperative sensor networks for smart grid computing, which allows for solving the fundamental power system operation problems by enabling the cooperation of dynamic agents. The resulting computing architecture is highly scalable, flexible, robust against perturbation, and able to self-repair. Chapters cover the needs and challenges in smart grids, cooperative and self-organizing sensor networks, self-organizing wide area measurement systems, decentralized voltage regulation and economic dispatch of distributed generators, grid monitoring estimation and control, and dynamic thermal rating assessment of overhead lines. Written with graduate students, researchers, and power system engineers in mind, this book offers a concise but thorough overview of the role of decentralized and self-organizing sensors in smart grids.

Interval Methods for Uncertain Power System Analysis In Interval Methods for Uncertain Power System Analysis, accomplished engineer Dr. Alfredo Vaccaro delivers a comprehensive discussion of the mathematical foundations of range analysis and its application to solving traditional power system operation problems in the presence of strong and correlated uncertainties. The book explores highly relevant topics in the area, from interval methods for uncertainty representation and management to a variety of application examples. The author offers readers the latest methodological breakthroughs and roadmaps to implementing the mathematics discussed within, as well as best practices commonly employed across the industry. Interval Methods for Uncertain Power System Analysis includes examinations of linear and non-linear equations, as well as: A thorough introduction to reliable computing, including discussions of interval arithmetic and interval-based operatorsComprehensive explorations of uncertain power flow analysis, including discussions of problem formulation and sources of uncertainty in power flow analysisIn-depth examinations of uncertain optimal power flow analysisFulsome discussions of uncertain small signal stability analysis, including treatments of how to compute eigenvalues of uncertain matrices Perfect for engineers working in power flow and optimal power flow analyses, optimization theory, and computer aided simulation, Interval Methods for Uncertain Power System Analysis will also earn a place in the libraries of researchers and graduate students studying decision making under uncertainty in power systems operation.

Affine Arithmetic-Based Methods for Uncertain Power System Analysis presents the unique properties and representative applications of Affine Arithmetic in power systems analysis, particularly as they are deployed for reliability optimization. The work provides a comprehensive foundation in Affine Arithmetic necessary to understand the central computing paradigms that can be adopted for uncertain power flow and optimal power flow analyses. These paradigms are adapted and applied to case studies, which integrate benchmark test systems and full step-by-step procedure for implementation so that readers are able to replicate and modify. The work is presented with illustrative numerical examples and MATLAB computations.