Comprehensive Exploration of Limitations of Simplified Machine Learning Algorithm for Fault Diagnosis Under Fault and Ground Resistances of Multiterminal High-Voltage Direct Current System

High power density and better efficiency make the multiterminal high-voltage direct current (MT-HVDC) system the best candidate for long-distance bulk power transfer in the cases of onshore and offshore power systems. Many machine learning-based algorithms have been developed for the protection of MT-HVDC systems. However, the exploration of the effects of change in the fault and ground resistances of MT-HVDC systems has not been studied comprehensively. In this study, a four-terminal HVDC test system is employed for the analysis of the effects on fault diagnosis under change in the fault and ground resistances. A simplified medium tree-based machine learning algorithm that works on Gini’s index of diversity is developed for fault diagnosis in the MT-HVDC system. It is found from the simulation analysis that the preprocessing based on mean and differences in featured data extracted for fault current is required to reduce the impacts of the accuracy of machine learning algorithms. The preprocessing not only retains the accuracy of the machine learning algorithm in different cases of faults, but also minimizes the reduction in accuracy in some fault cases. In the test cases, the accuracy is 88.7%, 60%, and 57.1% without preprocessing of featured data for the machine learning algorithm under different values of fault and ground resistances, but the accuracy is improved to 99.5%, 84.1%, and 77.8%, respectively. Hence, the machine learning algorithm can be made applicable under different values of fault and ground resistances for the protection of the MT-HVDC system. This helps to develop a protected MT-HVDC system for long distances without the fear of different soil conditions.