Overload Alarm Prediction in Power Distribution Transformers

Abstract

The growing demand for electricity puts more strain on the grid, requiring automated and proactive strategies such as overload prediction to improve grid maintenance. However, the intermittent nature of power distribution loads makes the prediction more challenging. This paper proposes a novel framework for overload alarm prediction in distribution transformers, aimed at enhancing the reliability and efficiency of grid operations. Leveraging real-world smart meter data and machine learning techniques, the proposed system develops a classification model to predict overloads for distribution transformers. Due to resource constraints, a new strategy is adopted to assess the significance of alarms based on expert observations. Subsequently, a new approach is developed to imitate the experts, leading to an automated decision-making process using random forest. Ultimately, the transfer learning strategy is utilized to predict overload alarms for distribution transformers facing data scarcity in real-world applications. The proposed system demonstrates high accuracy of overload alarm predictions, paving the way for developing more proactive grid maintenance strategies.