Fault identification of distribution network based on distributed sensors and K-nearest neighbor algorithm

It is of great significance to accurately identify the fault type and quickly locate the fault in the distribution system. The access of distributed power sources leads to a decrease in the accuracy of the traditional fault identification method based on single-end or double-end measurement equipment. With the development of sensor technology, the cost of sensors has gradually decreased, the measurement accuracy has gradually increased, and wide-area distributed sensing has become possible. Distributed sensing can collect more fault information from the system and improve the accuracy of fault identification. However, the increase of distributed sensing will also bring disadvantages, that is, the amount of data has greatly increased, and how to effectively use the increased data needs to be studied. The development of artificial intelligence technology has also made the performance of machine learning algorithms in complex data feature extraction and analysis increasingly enhanced. Based on this, this paper proposes a fault type identification and section location scheme based on distributed sensing and KNN algorithm. First, the effective value of voltage and current data is extracted for type identification, and the sequence component is added to optimize the features. On the basis of fault type identification, considering the advantages of distributed sensing, the fault features are artificially constructed to achieve fault section location. The overall accuracy is as high as 99.5%. An improved IEEE33 node data set containing distributed power sources was built, and different types of fault data in different section locations were simulated. The proposed method was used for training and verification, which demonstrated the rationality and applicability of the proposed method.