Machine Learning Based Fault Type Identification In the Active Distribution Network

To realize the intelligent of the distribution network, it is necessary to identify the fault type accurately. This paper presents the fault type identification method based on machine learning in active distribution networks. The process of machine learning is divided into four steps: data preparation, data preprocessing, feature extraction and model training. When preparing data, a method of generating fault scenarios in the batch of simulation experiments is presented. The IEEE34 Bus System is built in PSCAD to complete the data preparation for machine learning. Variation multiples of voltage and current are extracted as the features to describe the fault type. Various machine learning models are trained by cross-validation method to get the accuracy of identification. The application of decision tree in fault type identification is presented in the form of a tree diagram. The result of fault type identification is shown by the confusion matrix of the decision tree. All the test results show that the proposed fault identifiers can identify all kinds of fault types in the distribution network.