Machine Learning Based Coordination for IDMT Overcurrent and Earth Fault Relays

 In the dynamic and ever-changing realm of smart grid technology, it is of utmost importance to guarantee the efficient coordination of Inverse Definite Minimum Time (IDMT) overcurrent and earth fault relays. This coordination is crucial for maintaining the stability and safety of the grid. The conventional approaches to establishing these settings are characterized by their arduous nature and susceptibility to errors, frequently resulting in suboptimal outcomes. The primary objective of this work is to introduce a groundbreaking approach to this essential undertaking through the utilization of machine learning techniques. A comprehensive assessment was conducted on some commonly employed machine learning models, such as Linear Regression, Decision Tree, Random Forest, Support Vector Regression, and Gradient Boosting, to determine their effectiveness in the context of relay coordination. The Gradient Boosting model demonstrated superior performance compared to other models, achieving an R2-score of nearly 97% and exhibiting exceptionally low values for both Mean Square Error (MSE) and Mean Average Error (MAE). This finding suggests a strong alignment with the data and a high capacity to effectively apply the model to unfamiliar data, as evidenced by a Cross-Validation Score of 86.2%. The results of our study indicate that Gradient Boosting presents a highly precise, efficient, and dependable strategy for relay coordination in smart grid systems. Consequently, it emerges as an appealing alternative to conventional calculation-based methods.