Keywords
pole‑mounted breaker; status diagnosis; random forest; annealing evolution algorithm; residual neural network
Abstract
To evaluate the operation status of pole‑mounted breakers in an intelligent and efficient manner, the random forest (RF) is em‑ployed for feature optimization, and the annealing evolution algorithm (AEA) is applied to optimize the parameters of the re‑sidual neural network (ResNet). Firstly, a database is constructed, encompassing 22‑dimensional operational features of pole‑mounted breakers, with their importance indices calculated using the RF. Through the reverse sequence search method, 11 features are determined as inputs. Subsequently, the AEA is employed to optimize the network parameters of ResNet. Sim‑ulation results indicate that the RF effectively eliminates feature redundancy and improves the prediction performance of the model. In comparison to traditional prediction models, the proposed AEA‑ResNet method significantly improves the accuracy, especially in the recall and precision of minority samples.
DOI
10.19781/j.issn.1673-9140.2023.05.015
First Page
150
Last Page
158
Recommended Citation
ZHONG, Wei; YANG, Huanhong; ZHAO, Hengliang; CHEN, Bingsong; CHEN, Rong; and ZHANG, Xueqiang
(2024)
"Operation status diagnosis of pole‑mounted breakers based on RF feature optimization and AEA‑ResNet,"
Journal of Electric Power Science and Technology: Vol. 38:
Iss.
5, Article 15.
DOI: 10.19781/j.issn.1673-9140.2023.05.015
Available at:
https://jepst.researchcommons.org/journal/vol38/iss5/15