Application of Artificial Intelligence Algorithms in Monitoring and Prediction of Four Rate Integration in Power Grid

In the current landscape of rapidly advancing smart grid technology, safeguarding the grid’s safety, efficacy, and reliability stands at the forefront of the power industry’s priorities. Central to this endeavor is the integration of four key grid metrics: line loss, trip frequency, complaint rate, and electricity fee recovery. This composite index serves as a pivotal yardstick for assessing the grid’s operational status. Monitoring and forecasting these metrics are crucial for enhancing grid efficiency, optimizing resource allocation, and minimizing operational expenses. This article explores the utilization of artificial intelligence (AI) algorithms in overseeing and anticipating the integration of these four grid metrics. Specifically, it introduces a prediction algorithm rooted in deep learning (DL). This approach excels at uncovering the inherent patterns and trends within grid operational data, enabling real-time monitoring of grid status and forecasting the future trajectory of the integrated four-rate index. The empirical findings demonstrate that the proposed algorithm not only elevates the intelligence quotient of grid management but also provides prompt alerts upon detecting abnormal grid conditions. This timely intervention significantly diminishes the incidence of failures, thereby bolstering the grid’s stability and reliability.