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Voltage security assessment is an integral part of the modern Energy Management System. The traditional methods of contingency selection based on approximate or full AC load flow are either inaccurate or time consuming. To overcome these difficulties, development of fast, accurate and transparent voltage security assessment tools is required, so that in real-time, potentially dangerous operating conditions can be identified quickly and necessary corrective actions can be initiated within the given time frame of interest. Machine learning is a broad area of artificial intelligence, which is concerned with design and development of algorithms and techniques that allow computers to learn. Data mining is one of the branches of machine learning which uses past data for prediction of future results. In this book, data mining tools like fuzzy decision trees and case-based reasoning (CBR) is discussed in detail for application to voltage security assessment in power systems.
About the author
La dott.ssa Sonali Paunikar lavora come professore e responsabile del dipartimento di ingegneria elettrica ed elettronica dell'IES College of Technology, Bhopal, India. Ha conseguito il dottorato di ricerca in Power System presso l'Università di Nagpur nel 2019. La sua area di ricerca è l'applicazione dell'intelligenza artificiale nel sistema energetico, il funzionamento e il controllo del sistema energetico, la valutazione della sicurezza della tensione e le energie rinnovabili.
