Transmission system reliability evaluation based on one direction S-rough set theory

被引:0
|
作者
Yang X. [1 ]
Wang Y. [1 ]
Wang M. [1 ,2 ]
机构
[1] Institute of Water Resources and Hydro-electric Engineering, Xi'an University of Technology, Xi'an
[2] Guodian Nanjing Automation Go. Ltd., Nanjing
来源
| 1600年 / Electric Power Automation Equipment Press卷 / 36期
基金
中国国家自然科学基金;
关键词
F-decomposing law; Line failure rate; One direction S-rough set; Reliability evaluation; Weather factors;
D O I
10.16081/j.issn.1006-6047.2016.12.009
中图分类号
学科分类号
摘要
A method of transmission system reliability evaluation based on the one direction S-rough law and F-decomposing law is proposed to study the effects of complex weather on the reliability of transmission system. The one direction S-rough law is applied to deal with the uncertainty of failure rate and weather. A transmission line reliability model based on complex weather factors is established and the non-sequential Monte Carlo method is applied to evaluate the system reliability. F-decomposing law is applied to develop the interference indices and the effects of different weather combinations on them are analyzed. Analysis for IEEE 30-bus power generation and transmission system shows that, the component reliability parameter model with the consideration of weather factors predicts the transmission line failure rate accurately and the proposed reliability evaluation method based on the objective data (yearly days of different weather conditions) is immune to the influence of human subjective factors, which reveals the influencing law of weather factors on the system reliability and makes the evaluation results more objective. © 2016, Electric Power Automation Equipment Press. All right reserved.
引用
收藏
页码:57 / 62
页数:5
相关论文
共 20 条
  • [1] Liu Y., Zhou J., Incorporating weather effect in bulk power system reliability evaluation, Electric Power Automation Equipment, 23, 9, pp. 60-62, (2003)
  • [2] Li Y., Han J., Wang T., Ice monitoring system of overhead electrical power lines, Electric Power Automation Equipment, 29, 11, pp. 112-115, (2009)
  • [3] Li P., Ren Z., Guangzhou regional load analysis and short-term forecasting model desig, Electric Power Automation Equipment, 22, 8, pp. 50-53, (2002)
  • [4] Tang S., Zhang M., Li J., Et al., Review of blackout in Hainan on September 26th-cause and recommendations, Automation of Electric Power Systems, 30, 1, (2006)
  • [5] Pang Z., Li B., Yu Y., Et al., Study on operating modes of Hainan power grid during typhoon periods, Power System Technology, 31, 7, pp. 46-50, (2007)
  • [6] Greaves B., Collins J., Parkes J., Et al., Temporal forecast uncertainty for ramp events, Wind Engineering, 33, 4, pp. 309-319, (2009)
  • [7] Kamath C., Understanding wind ramp events through analysis of historical data, 2010 IEEE PES Transmission and Distribution Conference and Exposition, pp. 1-6, (2010)
  • [8] Zareipour H., Huang D., Rosehart W., Wind power ramp events classification and forecasting: a data mining approach, Power and Energy Society General Meeting, pp. 1-3, (2011)
  • [9] Zhang Y., Yuan D., Wang S., Selection of reliability original parameters in power system based on the fuzzy difference degree, Electric Power Automation Equipment, 29, 2, pp. 43-46, (2009)
  • [10] Zhu Y., Luo Y., Duan T., Et al., Online risk assessment based on real-time evaluation model of transmission line for static security of power system, Electric Power Automation Equipment, 34, 7, pp. 150-156, (2014)