A Classified Warning Method for Heavy Overload in Distribution Networks Considering the Characteristics of Unbalanced Datasets

In order to achieve heavy overload warning and capacity planning for the distribution network, it is necessary to classify the heavy overload warning of the distribution network. A distribution network with heavy overload classification warning method based on imbalanced dataset feature extraction is proposed. Screening the feature indicator set related to distribution network overload, constructing a hierarchical prediction framework for distribution network load situation, combining information such as power distribution points, road construction, municipal planning, and power load distribution to form distribution network capacity planning and line renovation plans. Based on K-means clustering, the undersampling method is used to extract features from the unbalanced dataset of distribution network overload classification, using decision trees as the basic learning unit. It includes multiple decision trees trained by Bagging integrated learning theory and random subspace method. The random forest algorithm is used to realize the feature detection and distribution network capacity planning of distribution network weight overload grading, and the grading early warning of distribution network weight overload is realized according to the capacity planning results. Tests have shown that this method has good accuracy in predicting electrical loads and can effectively solve the problem of excess capacity caused by light or no load, improving the ability of heavy overload warning and capacity planning in the distribution network.

@article{Ren2023,
  title     = {A Classified Warning Method for Heavy Overload in Distribution Networks Considering the Characteristics of Unbalanced Datasets},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {6},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Guohui Ren},
  doi       = {10.14569/IJACSA.2023.0140694},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140694}
}