Optimizing Electrical System Performance with Machine Learning: An Analysis of Algorithms

The analysis of large amounts of data received from dispatch centers is crucial for the electrical industry, retrieved from various electrical systems such as generation, transmission, and distribution are analyzed using control systems like SCADA and HMI without human intervention. However, to meet the industry 4.0 standards, automation of every system is essential. This can be achieved by integrating the data into the Internet of Things (IoT) with adequate cybersecurity measures. In this regard, this paper proposes the use of intelligent predictive data analysis to optimize the operational maintenance of electrical systems in the future. The research analyzes the recent and historical data using several state electrical utility data files. In order to examine the available data, supervised machine learning algorithms are used, and the analysis of anticipated data is used to assess each algorithm’s accuracy.