Dynamic Thermal Rating of Transmission Line Based on Environmental Parameter Estimation
0
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
The transmission capacity of transmission lines is affected by environmental parameters such as ambienttemperature, wind speed, wind direction and so on. The environmental parameters can be measured by theinstalled measuring devices. However, it is impossible to install the environmental measuring devicesthroughout the line, especially considering economic cost of power grid. Taking into account the limitednumber of measuring devices and the distribution characteristics of environment parameters and transmissionlines, this paper first studies the environmental parameter estimating method of inverse distance weightedinterpolation and ordinary Kriging interpolation. Dynamic thermal rating of transmission lines based on IEEEstandard and CIGRE standard thermal equivalent equation is researched and the key parameters that affect theload capacity of overhead lines is identified. Finally, the distributed thermal rating of transmission line isrealized by using the data obtained from China meteorological data network. The cost of the environmentalmeasurement device is reduced, and the accuracy of dynamic rating is improved.
Abstract
The transmission capacity of transmission lines is affected by environmental parameters such as ambienttemperature, wind speed, wind direction and so on. The environmental parameters can be measured by theinstalled measuring devices. However, it is impossible to install the environmental measuring devicesthroughout the line, especially considering economic cost of power grid. Taking into account the limitednumber of measuring devices and the distribution characteristics of environment parameters and transmissionlines, this paper first studies the environmental parameter estimating method of inverse distance weightedinterpolation and ordinary Kriging interpolation. Dynamic thermal rating of transmission lines based on IEEEstandard and CIGRE standard thermal equivalent equation is researched and the key parameters that affect theload capacity of overhead lines is identified. Finally, the distributed thermal rating of transmission line isrealized by using the data obtained from China meteorological data network. The cost of the environmentalmeasurement device is reduced, and the accuracy of dynamic rating is improved.
참고문헌 (15)
- Y. Cong, "On the use of dynamic thermal-line ratings for improving operational tripping schemes", IEEE Transactions on Power Delivery, 31(4), pp.1891~1900 (2016)
- J. X. Li, "ArcGIS based kriging interpolation method and its application", Bulletin of Surveying and Mapping, 2013(9), pp.87~90 (2013)
- W. Cao, "A study on temperature interpolation methods based on GIS", Proceedings of 2009 17th International Conference on Geoinformatics, pp.1~5 (2009)
- D. Cai, "Interpolation of air temperature based on DEM over eastern region of Gansu", Journal of Arid Meteorology, 27(1), pp.10~17 (2009)
- W. R. Tobler, "A computer movie simulating urban growth in the Detroit region", Economic Geography, 46(Sup 1), pp.234~240 (1970)
- E. Oktavia, "Inverse distance weighting and kriging spatial interpolation for data center thermal monitoring", Proceedings of 2016 1st International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), pp.69~74 (2016)
- H. D. Khairnar, "Accuracy evaluation of Cartosat-1 DEM using different Interpolation techniques for Pune area", Proceedings of 2015 International Conference on Industrial Instrumentation and Control (ICIC), pp.203~206 (2015)
- Z. Li, "Intelligent optimization on power values for inverse distance weighting", Proceedings of 2013 International Conference on Information Science and Cloud Computing Companion, pp.370~375 (2013)
- Z. J. Liu, "Location fingerprint database construction algorithm based on Kriging spatial interpolation", Application Research of Computers, 33(10), pp.3139~3142 (2016)
- X. F. Yang, "Application of ordinary Kriging method in the interpolation for seawater temperature profile", Technical Acoustics, 10(5), pp.385~388 (2015)
- N. Yang, "Soil moisture estimation in Southeast Gansu with ordinary Kriging", Proceedings of the World Automation Congress 2012, pp.1~4 (2012)
- J. Bangay, "Comparison of IEEE and CIGRE methods for predicting thermal behaviour of powerlines and their relevance to distribution networks", Proceedings of 2015 IEEE Eindhoven PowerTech, pp.1~5 (2015)
- S. Sediva, "Comparison of GUM and Monte Carlo method for evaluation measurement uncertainty of indirect measurements", Proceedings of the 14th International Carpathian Control Conference (ICCC), pp.325~329 (2013)
- "IEEE Standard for Calculating the Current-Temperature Relationship of Bare Overhead Conductors", IEEE Standard 738-2012, ~ (2013)
- CIGRE, "The Thermal Behavior of Overhead Conductors (ELT_144_3)", GIGRE, ~ (1992)
