A settings tracking and providing scheme for differential protection based on machine learning

With the extensive use of differential protection in micro-grid, the demand for online obtaining the settings becomes more urgent than the process in the past. This paper proposes a new differential protection scheme for micro-grid where a settings tracking and providing scheme is implemented to acquire the latest enabled protection settings by tracking multiple setting group control block (SGCB) class services. A machine learning technique is implemented to assist classifier in identifying the most relevant electrical features which are required for the fault detection and to establish the best efficient differential protection strategy to micro-grid. A practical case study has successfully verified the adaptability and practicability of the micro-grids protection scheme where statistical classifier will make a decision based on protection settings and differential features.