Alfredo Vaccaro – författare

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. Provides a uniquely comprehensive review of affine arithmetic in both its core theoretical underpinnings and their developed applications to power system analysis Details the exemplary benefits derived by the deployment of affine arithmetic methods for uncertainty handling in decision-making processes Clarifies arithmetical complexity and eases the understanding of illustrative methodologies for researchers in both power system and decision-making fields

Electrical grids are, in general, among the most reliable systems in the world. These large interconnected systems, however, are subject to a host of challenges - aging infrastructure, transmission expansion to meet growing demand, distributed resources, and congestion management, among others. Innovations in Power Systems Reliability aims to provide a vision for a comprehensive and systematic approach to meet the challenges of modern power systems. Innovations in Power Systems Reliability is focused on the emerging technologies and methodologies for the enhancement of electrical power systems reliability. It addresses many relevant topics in this area, ranging from methods for balancing resources to various reliability and security aspects. Innovations in Power Systems Reliability not only discusses technological breakthroughs and sets out roadmaps in implementing the technology, but it also informs the reader about current best practice. It is a valuable source of information for academic researchers, as well as those working in industrial research and development.

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 matricesPerfect 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.

Electrical grids are, in general, among the most reliable systems in the world. These large interconnected systems, however, are subject to a host of challenges - aging infrastructure, transmission expansion to meet growing demand, distributed resources, and congestion management, among others. Innovations in Power Systems Reliability aims to provide a vision for a comprehensive and systematic approach to meet the challenges of modern power systems. Innovations in Power Systems Reliability is focused on the emerging technologies and methodologies for the enhancement of electrical power systems reliability. It addresses many relevant topics in this area, ranging from methods for balancing resources to various reliability and security aspects. Innovations in Power Systems Reliability not only discusses technological breakthroughs and sets out roadmaps in implementing the technology, but it also informs the reader about current best practice. It is a valuable source of information for academic researchers, as well as those working in industrial research and development.

This book considers the emerging technologies and methodologies of the application of computational intelligence to smart grids.From a conceptual point of view, the smart grid is the convergence of information and operational technologies applied to the electric grid, allowing sustainable options to customers and improved levels of security. Smart grid technologies include advanced sensing systems, two-way high-speed communications, monitoring and enterprise analysis software, and related services used to obtain location-specific and real-time actionable data for the provision of enhanced services for both system operators (i.e. distribution automation, asset management, advanced metering infrastructure) and end-users (i.e. demand side management, demand response).In this context, a crucial issue is how to support the evolution of existing electrical grids from static hierarchal systems to self-organizing, highly scalable and pervasive networks. Modern trends are oriented toward the employment of computational intelligence techniques for deploying advanced control, protection and monitoring architectures that move away from the older centralized paradigm to systems distributed across the field with an increasing pervasion of intelligence devices. The large-scale deployment of computational intelligence technologies in smart grids could lead to a more efficient tasks distribution amongst energy resources and, consequently, to a sensible improvement of the electrical grid flexibility.

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.

Wide area monitoring, protection and control systems (WAMPACs) have been recognized as the most promising enabling technologies to meet challenges of modern electric power transmission systems, where reliability, economics, environmental and other social objectives must be balanced to optimize the grid assets and satisfy growing electrical demand. To this aim WAMPAC requires precise phasor and frequency information, which are acquired by deploying multiple time synchronized sensors, known as Phasor Measurement Units (PMUs), providing precise synchronized information about voltage and current phasors, frequency and rate-of-change-of-frequency.This book provides an overview of this emerging technology. Topics covered include an introduction to WAMPACs; reliability-based substation monitoring systems placement; system integrity protection scheme based on PMU technology; new methodologies for large scale power system dynamic analysis; a fuzzy-based knowledge discovery paradigm for on-line optimal power flow analysis; and false data injection attacks and countermeasures for wide area measurement system.

This book of Advances in Deep Learning, Artificial Intelligence and Robotics (proceedings of ICDLAIR 2020) is intended to be used as a reference by students and researchers who collect scientific and technical contributions with respect to models, tools, technologies and applications in the field of modern artificial intelligence and robotics.

This book presents new technologies and applications in deep learning, artificial intelligence and robotics. The field of machine intelligence (MI), unifying robotics and artificial intelligence is experiencing constant growth and change. The challenge to reproduce human behavior in machines requires the interaction of many fields, from engineering to mathematics, from neurology to biology, from computer science to robotics, from web search to social networks, from machine learning to game theory, etc. This book Progresses in Artificial Intelligence & Robotics : Algorithms & Applications (proceedings of  3rd International Conference on Deep Learning, Artificial Intelligence and Robotics (ICDLAIR) 2021 ) introduces key topics from artificial intelligence algorithms and programming organization and explains how they contribute to autonomous capabilities. The book is primarily intended for researchers, students, and engineers who wish to use the applications of artificial intelligence to solve concrete problems. We hope that companies and technology developers also find it interesting to be used in industry.

The book (proceedings of the 4th International Conference on Deep Learning, Artificial Intelligence and Robotics (ICDLAIR) 2022) introduces key topics from artificial intelligence algorithms and programming organisations and explains how they contribute to health care, manufacturing, law, finance, retail, real estate, accountancy, digital marketing, and various other fields. Although artificial intelligence (AI) has generated a lot of hype over the past ten years, these consequences on how we live, work, and play are still in their infancy and will likely have a significant impact in the future. The supremacy of AI in areas like speech and picture recognition, navigational apps, personal assistants for smartphones, ride-sharing apps, and many other areas is already well established. The book is primarily meant for academics, researchers, and engineers who want to employ AI applications to address real-world issues. The authors hope that businesses and technology creators will also find it appealing to utilise in industry.

This book presents best selected papers presented at the International Conference on Advances in Energy Technology (ICAET 2020) organized by Gandhi Institute for Education and Technology (GIET), Bhubaneswar, India, during 17–18 January 2020.