A failure prediction method of power distribution network based on PSO and XGBoost

References

• Breiman, L. 2001. “Random forests[J].” Machine Learning 45 (1): 5–32. doi:10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• Chen, T Q, and C. Guestrin 2016. “XGBoost:A Scalable Tree Boosting System[C].” ACM SIGKDD 2016 Conference on Knowledge Discovery and Data Mining, San Francisco, USA, 785–795.
   Google Scholar
• Chen, T, T He, and M Benesty. 2015. “Xgboost: Extreme Gradient boosting[J]”. R package version 0.4-2. 1–4.
   Google Scholar
• Hanqing, LIANG, LIU Yadong, SHENG Gehao, et al. 2017. “Research on Method for Obtaining Key Transient Information of Transmission Line at Unsymmetrical Earth Fault [J].” Power System Technology 41 (8): 2702–2711.
   Google Scholar
• Hongbin, WANG, FANG Jian, HE Jiaxing, et al. 2019. “A Review of Resilient Distribution Network under Extreme disasters[J].” Distribution and Utilization 36 (7): 20–29.
   Google Scholar
• Hui, HOU, GENG Hao, HUANG Yong, et al. 2019. “Multi-factor Correction Based Failure Prediction Method of Transmission Line with Typhoon Disaster[J].” Automation of Electric Power Systems 43 (23): 193–201+233.
   Google Scholar
• Kai, Song, Yan Feng, Ding Ting, et al. 2020. “A Steel Property Optimization Model Based on the XGBoost Algorithm and Improved PSO[J]”. Computational Materials Science 174(C).
   Google Scholar
• Kennedy, J, and R Eberhart.1995. “Particle Swarm optimization[C].” Proceedings of ICNN95-International Conference on Neural Networks, Perth, Australia. 1942–1948.
   Google Scholar
• MA, S, B CHEN, and Z. WANG. 2018. “Resilience Enhancement Strategy for Distribution Systems under Extreme Weather events[J].” IEEE Transactions on Smart Grid 9 (2): 1442–1451. doi:10.1109/TSG.2016.2591885.
   Web of Science ®Google Scholar
• Rui, LI, LI Yue, SU Jian, et al. 2011. “Power Supply Interruption Cost of Important Power Consumers in Distribution Network and Its Emergency Management [J].” Power System Technology 35 (10): 170–176.
   Google Scholar
• Shi, Y, and R Eberhart . 2002. “Empirical Study of Particle Swarm optimization[C].” Congress on Evolutionary Computation, IEEE, Washington, DC, USA, 6-9 July 1999.
   Google Scholar
• Shouxiang, WANG, HUANG Renshan, PAN Zhixin, et al. 2020. “Construction and Evaluation of Resilience Restoration Capability Indices for Distribution NetworkUnder Extreme Ice and Snow Weather[J].” High Voltage Engineering 46 (1): 123–132.
   Google Scholar
• Tewei, XU, LU Zongxiang, QIAO Ying, et al. 2017. “A Risk Warning Method for Urban Distribution Network Based on Associated Recognition of Typical Fault and Environment Scenario[J].” Power System Technology 41 (8): 2577–2584.
   Google Scholar
• Wen, ZHANG, SHENG Wanxing, LIU Keyan, et al. 2018. “A Prediction Method of Fault Risk Level for Distribution Network considering Correlation of Weather Factors[J].” Power System Technology 42 (8): 2391–2398.
   Google Scholar
• Xiangyang, PENG, HUANG Zhiwei, and DAI Zhiwei. 2010. “Analysis on the Cause of Distribution Lines’s Damage during Typhoon and Counteract measures[J].” Southern Power System Technology 4 (1): 99–102.
   Google Scholar
• Xiaomin, ZHOU, GE Shaoyun, LI Teng, et al. 2018. “Assessing and Boosting resilience of Distribution System under Extreme weather[J].” Proceedings of the CSEE 38 (2): 505–513, 681.
   Google Scholar
• Xiuchen, JIANG, LIU Yadong, FU Xiaofei, et al. 2019. “Construction Ideas and Development Trends of Transmission and Distribution[J].” High Voltage Engineering 45 (5): 1345–1351.
   Google Scholar
• Yong, HUANG, WEI Ruizeng, ZHOU Enze, et al. 2018. “Early Warning Method of Transmission Line Damage under Typhoon disaster[J].”.Automation of Electric Power Systems 42 (23): 142–150.
   Google Scholar
• Zhang, C, P Lim, Qin A K, et al. 2017. “Multi-objective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics [J].” IEEE Transactions on Neural Networks and Learning Systems 28 (10): 2306~2318. DOI:10.1109/TNNLS.2016.2582798.
   Web of Science ®Google Scholar
• Zhao, W, T Tao, E Zio, et al. 2016. “A Novel Hybrid Method of Parameters Tuning in Support Vector Regression for Reliability Prediction:particle Swarm Optimization Combined with Analytical selection[J].” IEEE Transactions on Reliability 65 (3): 1393–1405. DOI:10.110/TR.2016.2515581.
   Google Scholar
• ZHAO, S, D ZENG, W WANG, et al. 2020. “Mutation Grey Wolf Elite PSO Balanced XGBoost for Radar Emitter Individual Identification Based on Measured signals[J].” Measurement 159: 107777.
   Web of Science ®Google Scholar