Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Padarha, Shreyansh; Modi, Sakshi; Lekha, J. Title A name given to the resource Interconnected Intelligence: Navigating Through Power Quality Checking and Control Using Smart Intelligence-Based Methods Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Power Systems;Volume;Part F914;pp.163-183 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-031-89459-6_9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-89459-6_9</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105018032325?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105018032325?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Padarha S., School of Sciences, CHRIST University, Maharashtra, Lavasa, India; Modi S., School of Sciences, CHRIST University, Maharashtra, Lavasa, India; Lekha J., School of Sciences, CHRIST University, Maharashtra, Lavasa, India Description An account of the resource Globally, power quality issues incur substantial costs. In the United States, power quality problems contribute to a $150 billion annual cost, covering lost productivity, equipment damage, and safety hazards. Smart intelligence-based methods can potentially cut these costs by up to 50%. In India, power quality disturbances result in a $10 billion annual cost involving equipment damage, productivity losses, and customer dissatisfaction. The adoption of smart intelligence-based power quality methods in India is projected to grow annually by 25% for the next 5years due to increasing grid demands. In todays intricate power landscape, dependable electrical systems are crucial. Power quality disturbances, including voltage variations, harmonics, and flicker, can disrupt sensitive equipment, resulting in financial losses and safety risks. Addressing these challenges, smart intelligence-based methods emerge as promising solutions. This chapter systematically explores the application of artificial intelligence, machine learning, and data analytics for elevated power quality monitoring, assessment, and regulation. Such intelligent approaches optimise power system performance, reduce downtimes, and ensure a consistent supply of high-quality electrical energy. The assimilation of smart intelligence-based methods emerges as a promising avenue to address these challenges effectively. Harnessing the capabilities of these intelligent paradigms empower power systems to attain optimal performance, curtail downtimes, and ensure a steadfast provision of high-grade electrical energy. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. Subject The topic of the resource Artificial intelligence; Machine learning; Power quality; Smart intelligence Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 16121287; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online