Predictive analytics for aging U.S. electrical infrastructure: Leveraging machine learning to enhance grid resilience and reliability
1 Department of Computer Sciences and Information System, Western Illinois University, Macomb Illinois USA.
2 Department of Applied Statistics and Decision Analytics Western Illinois University, Macomb Illinois USA.
Review Article
World Journal of Advanced Research and Reviews, 2023, 19(02), 1595-1622
Article DOI: 10.30574/wjarr.2023.19.2.1723
Publication history:
Received on 17 July 2023; revised on 26 August 2023; accepted on 28 August 2023
Abstract:
This paper discusses about U.S. electrical grid which is a vital infrastructure supporting a lot of industries and people around the USA. However, it faces various challenges because of aging components, the threat of extreme weather, and increasing energy demands. Due to this, it will become extremely difficult to maintain the resilience and reliability of the grid and a growing concern for utilities, policymakers, and stakeholders. By using the quantitative method, the research shows potential benefits including a 20% reduction in unplanned outages, with a 15% improvement in operational efficiency that is supported by a 20% reduction in unplanned outages and just 15% improvement observed in operational efficiency level, supported by cost-benefit analysis. This research explores in detail the potential of machine learning, predictive analytics, and Internet of Things (IoT) sensors to modernize the electrical grid, minimize downtime caused by component failure, and enhance efficiency. Therefore, by implementing the historical data, advance machine learning models, real-time data monitoring, and predictive maintenance is helpful to identify main failures present in critical components like transmission lines, transformers, and substations before they occur. This study investigates in detail the design and implementation of a predictive analytics platform reliable for the U.S. grid by focusing on machine learning algorithms, data collection, and scalability challenges. The findings focus on the need for strategic collaboration between policymakers, utilities, and technology providers to minimize challenges related to data integration, cost, and infrastructure. This research is contributing to the ongoing efforts for building a highly resilient and sustainable electrical grid, capable of meeting the required demand of the future and minimizing risks caused by aging infrastructure.
Keywords:
Machine learning; Predictive analytics; Quantitative method. Electrical grid; Predictive maintenance; LoT sensors; Grid modernization; Grid resilience; Aging infrastructure; Data integration; Real-time monitoring
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Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0