Distribution network fault identification based on AdaBoost combined with KNN algorithm

This paper addresses the challenge of fault type identification in distribution networks, where small-current grounding systems often lead to weak fault current characteristics and limit the performance of traditional methods. To overcome issues of low feature discriminability, poor noise immunity, and weak generalization, a fault identification scheme based on root mean square (RMS) values, symmetrical component analysis, and the Adaboost ensemble learning algorithm is proposed. An improved IEEE 33-bus distribution network model with distributed generation is developed in PSCAD/EMTDC to generate diverse fault data and construct a fault fingerprint database. Feature matrices derived from multiple sensor nodes are trained and optimized using Adaboost combined with KNN, achieving a classification accuracy of 99.12%. The results verify the robustness, effectiveness, and practical applicability of the proposed method under complex operating conditions.