[1]
|
da Silva, M.F., Honório, L.M., Marcato, A.L.M., Vidal, V.F. and Santos, M.F. (2020) Unmanned Aerial Vehicle for Transmission Line Inspection Using an Extended Kalman Filter
with Colored Electromagnetic Interference. ISA Transactions, 100, 322-333.
https://doi.org/10.1016/j.isatra.2019.11.007
|
[2]
|
彭向阳, 易琳, 钱金菊, 王柯, 郑晓光, 韩正伟, 陈国强. 大型无人直升机电力线路巡检系统实用
化 [J]. 高电压技术, 2020, 46(2): 384-396.
|
[3]
|
Zhang, Y., Yuan, X.X., Li, W.Z. and Chen, S.Y. (2017) Automatic Power Line Inspection
Using UAV Images. Remote Sensing, 9, Article 824. https://doi.org/10.3390/rs9080824
|
[4]
|
常龙, 游华武. 无人机激光雷达技术在高压输电线路三维设计中的应用 [J]. 电声技术, 2021,
45(10): 80-82.
|
[5]
|
隋宇, 宁平凡, 牛萍娟, 王辰羽, 赵地, 张伟龙, 韩抒真, 梁立君, 薛高建, 崔颜军. 面向架空输电
线路的挂载无人机电力巡检技术研究综述 [J]. 电网技术, 2021, 45(9): 3636-3648.
|
[6]
|
全国架空线路标准化技术委员会线路运行分技术委员会. DL/T 741-2019. 架空输电线路运行
规程 [S]. 北京: 中国电力出版社, 2019: 1-36.
|
[7]
|
IEEE (2013) IEEE Standard for Calculating the Current-Temperature Relationship of Bare
Overhead Conductors. IEEE Std 738-2012, IEEE, New York, 1-58.
|
[8]
|
孟遂民, 孔伟. 架空输电线路设计 [M]. 第 2 版. 北京: 中国电力出版社, 2015.
|
[9]
|
Xu, Y.J., Huang, C., Chen, X., Mili, L., Tong, C.H., Korkali, M. and Min, L. (2019) Response Surface-Based Bayesian Inference for Power System Dynamic Parameter Estimation. IEEE
Transactions on Smart Grid, 10, 5899-5909. https://doi.org/10.1109/TSG.2019.2892464
|
[10]
|
Ye, G., Xiang, Y., Nijhuis, M., Cuk, V. and Cobben, J.F.G. (2017) Bayesian-Inference-Based
Voltage Dip State Estimation. IEEE Transactions on Instrumentation and Measurement, 66,
2977-2987. https://doi.org/10.1109/TIM.2017.2734138
|
[11]
|
Wang, Y., Zhou, Z., Botterud, A. and Zhang, K.F. (2017) Optimal Wind Power Uncertainty
Intervals for Electricity Market Operation. IEEE Transactions on Sustainable Energy, 9, 199-
210. https://doi.org/10.1109/TSTE.2017.2723907
|
[12]
|
Yue, C.-D., Chiu, Y.-S., Tu, C.-C. and Lin, T.-H. (2020) Evaluation of an Offshore Wind Farm
by Using Data from the Weather Station, Floating LiDAR, MAST, and MERRA. Energies,
13, Article 185. https://doi.org/10.3390/en13010185
|
[13]
|
Wang, S.J., Zeng, J.Y. and Liu, X.P. (2019) Examining the Multiple Impacts of Technological
Progress on CO2 Emissions in China: A Panel Quantile Regression Approach. Renewable and
Sustainable Energy Reviews, 103, 140-150. https://doi.org/10.1016/j.rser.2018.12.046
|
[14]
|
Zheng, H.T., Yuan, J.B. and Chen, L. (2017) Short-Term Load Forecasting Using EMD-LSTM
Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation. Energies, 10,
Article 1168. https://doi.org/10.3390/en10081168
|
[15]
|
Jia, X.W., Willard, J., Karpatne, A., Read, J.S., Zwart, J.A., Steinbach, M. and Kumar, V.
(2021) Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles. ACM/IMS Transactions on Data Science, 2, Article No. 20.
https://doi.org/10.1145/3447814
|
[16]
|
Chen, T.Q., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K.L., et al. (2015)
XGBoost: Extreme Gradient Boosting.
https://cran.r-project.org/web/packages/xgboost/vignettes/xgboost.pdf
|