## Zidan Sun* , Zhijie Yan* , Likai Liang* , Ran Wei** and Wei Wang***## |

Cross validation | Circular | Spherical | Index | Gaussian | IDW |

Temperature MAE (ºC) | 0.4162 | 0.5008 | 0.4473 | 0.3120 | 1.264 |

Temperature RMSIE (ºC) | 1.5328 | 2.4277 | 1.5911 | 0.9948 | 6.514 |

Wind speed MAE (m/s) | 0.3896 | 0.3416 | 0.4332 | 0.4123 | 0.873 |

Wind speed RMSIE (m/s) | 1.5906 | 1.4604 | 1.7572 | 1.6240 | 3.645 |

Wind direction MAE (º) | 4.0951 | 4.3567 | 6.5569 | 6.1321 | 11.62 |

Wind direction RMSIE (º) | 12.343 | 13.558 | 15.449 | 14.986 | 25.09 |

According to the results of Table 1, it can be seen that MAE and RMSIE of the various variation function model (circular, spherical, exponential, and Gaussian) of the ordinary Kriging interpolation method are smaller than those of the inverse distance weighted interpolation method. Therefore, the general Kriging interpolation method is effective. It can be seen by comparison that the Gaussian variation function model should be chosen when estimating the ambient temperature. The spherical model should be selected when estimating the wind speed, and the circular model should be selected when estimating the wind direction.

Table 2.

Point | Longitude (E) | Latitude (N) | Temperature (ºC) | Wind speed (m/s) | Wind direction (º) |

1 | 109.676 | 37.989 | 22.31258 | 1.71582 | 156.3338 |

2 | 109.686 | 37.884 | 22.67912 | 2.14189 | 149.2344 |

3 | 109.729 | 37.744 | 23.01952 | 2.33556 | 143.2542 |

4 | 109.758 | 37.609 | 23.21038 | 2.26855 | 138.0481 |

5 | 109.792 | 37.470 | 23.42952 | 2.04048 | 131.4788 |

6 | 109.835 | 37.340 | 23.60655 | 1.84029 | 127.6865 |

7 | 109.864 | 37.244 | 23.64497 | 1.68302 | 125.5642 |

8 | 109.844 | 37.095 | 23.60006 | 1.58664 | 125.6270 |

9 | 109.820 | 36.970 | 23.35387 | 1.59052 | 124.9162 |

10 | 109.801 | 36.845 | 22.94237 | 1.58255 | 126.9266 |

11 | 109.792 | 36.671 | 22.68975 | 1.67703 | 136.2848 |

12 | 109.758 | 36.570 | 22.96728 | 1.94498 | 143.1490 |

13 | 109.739 | 36.416 | 23.63994 | 2.66577 | 155.6990 |

14 | 109.695 | 36.243 | 23.97541 | 3.58796 | 166.6059 |

15 | 109.614 | 36.113 | 23.72943 | 3.75889 | 174.8989 |

16 | 109.469 | 35.806 | 22.48813 | 1.522706 | 190.5641 |

17 | 109.404 | 35.666 | 21.78378 | 0.61640 | 193.0289 |

18 | 109.424 | 35.551 | 22.06700 | 0.53014 | 229.0574 |

19 | 109.457 | 35.435 | 22.58040 | 0.86832 | 257.9343 |

20 | 109.472 | 35.300 | 23.21345 | 1.47723 | 269.2503 |

21 | 109.515 | 35.166 | 23.27094 | 1.95238 | 284.4721 |

22 | 109.544 | 35.021 | 22.47640 | 2.07477 | 298.0574 |

23 | 109.577 | 34.882 | 20.53267 | 1.54755 | 299.1291 |

24 | 109.688 | 34.771 | 18.89304 | 1.05216 | 268.7144 |

25 | 109.823 | 34.685 | 19.47246 | 1.30290 | 239.6672 |

The overhead transmission line and surrounding environment parameters have spatial distribution characteristics. In order to obtain accurate environmental parameters, based on the geo-statistical analysis module of ARCGIS software, the environmental parameters of 25 points along the transmission line is estimated by ordinary Kriging interpolation method, which using the known data of a total of 324 measure points in Fig. 5. The results are shown in Table 2.

Based on the estimated environmental parameters at 25 points, this section uses the IEEE and CIGRE criteria to calculate the dynamic thermal ratings at 25 points throughout the transmission line. The results are shown in Table 7. And the minimum dynamic thermal rating value is determined as the dynamic thermal rating value of the whole line. The calculated results and the distance to Hengyu point are shown in Table 3 and Fig. 6.

Table 7.

Point | Longitude (E) | Latitude (N) | IEEE standard (A) | CIGRE standard (A) | Distance (km) |

1 | 109.676 | 37.989 | 913.9226 | 861.2586 | 0 |

2 | 109.686 | 37.884 | 1020.3153 | 974.0300 | 11.0 |

3 | 109.729 | 37.744 | 1089.3991 | 1052.5297 | 23.8 |

4 | 109.758 | 37.609 | 1133.9634 | 1096.0657 | 39.5 |

5 | 109.792 | 37.470 | 1155.5546 | 1107.8177 | 57.9 |

6 | 109.835 | 37.340 | 1155.2596 | 1104.5880 | 73.9 |

7 | 109.864 | 37.244 | 1134.9570 | 1089.4841 | 83.1 |

8 | 109.844 | 37.095 | 1229.1122 | 1189.4378 | 102.1 |

9 | 109.820 | 36.970 | 1233.2833 | 1193.5300 | 116.2 |

10 | 109.801 | 36.845 | 1234.7775 | 1195.4071 | 128.3 |

11 | 109.792 | 36.671 | 1243.8198 | 1200.6133 | 148.4 |

12 | 109.758 | 36.570 | 1276.3290 | 1225.2189 | 160.7 |

13 | 109.739 | 36.416 | 1340.3857 | 1326.8978 | 177.5 |

14 | 109.695 | 36.243 | 1386.1931 | 1412.5868 | 197.1 |

15 | 109.614 | 36.113 | 1470.9752 | 1509.8247 | 213.5 |

16 | 109.469 | 35.806 | 1110.9621 | 1070.9824 | 250.3 |

17 | 109.404 | 35.666 | 920.0518 | 889.3851 | 266.7 |

18 | 109.424 | 35.551 | 988.1336 | 964.6638 | 279.6 |

19 | 109.457 | 35.435 | 1092.5745 | 1067.4283 | 293.6 |

20 | 109.472 | 35.300 | 1202.5186 | 1167.0630 | 307.4 |

21 | 109.515 | 35.166 | 1264.6938 | 1213.7556 | 323.0 |

22 | 109.544 | 35.021 | 1248.0381 | 1203.9332 | 337.3 |

23 | 109.577 | 34.882 | 1179.6452 | 1140.5503 | 355.6 |

24 | 109.688 | 34.771 | 1132.0272 | 1104.8851 | 369.4 |

25 | 109.823 | 34.685 | 1224.0910 | 1196.1841 | 386.7 |

Table 3 and Fig. 6 shows that the dynamic thermal ratings at point 1 is 861.2586 A, which is calculated by the CIGRE standard. The wind speed at point 1 is 1.71582 m/s, the ambient temperature is 22.31258, and the angle between the wind direction and the line is about 2º, which can be seen in Table 2. Considering the ambient parameters, the dynamic thermal rating calculated by the CIGRE standard at point 1 is the smallest, so the dynamic thermal rating 861.2586 A at point 1 is taken as the dynamic thermal rating of the whole line.

In this paper, the estimating methods of the surrounding environment parameters of the transmission line is studied, the two calculation criteria of the dynamic thermal rating and the influence of the environment parameters on the line thermal load capacity are analyzed and quantified. The calculation and analysis are carried out according to the actual line based on IEEE and CIGRE standard. The key factors affecting the current carrying capacity of overhead lines are wind speed, ambient temperature and wind direction. Through the method of cross validation, it is proved that Kriging is an effective method to estimate the environmental parameters. According to the China meteorological data network, the dynamic thermal rating value considering the spatial distribution of environmental parameters and transmission line is realized. The study reduces the cost of the environmental measurement device, and improve the accuracy of dynamic rating throughout transmission lines.

He was born in Jiangsu province, China, in 1994. He received his B.E. degree in School of Software from Shandong University in 2017. He is currently pursuing his M.E. degree in electronics and communication engineering at Shandong University, China. His main research interests include power system operation and control.

He was born in Jiangsu province, China, in 1993. He received his B.E. degree in electrical engineering and automation from Yancheng Teachers University, China, in 2015. He is currently pursuing his M.E. degree in electronics and communication engineering at Shandong University, China. His current research interests include power system operation and control.

- 1 Y. Cong, P. Regulski, P. Wall, M. Osborne, V. Terzija, "On the use of dynamic thermal-line ratings for improving operational tripping schemes,"
*IEEE Transactions on Power Delivery*, vol. 31, no. 4, pp. 1891-1900, 2016.doi:[[[10.1109/tpwrd.2015.2502999]]] - 2 J. X. Li, C. K. Li, Z. H. Yin, "ArcGIS based kriging interpolation method and its application,"
*Bulletin of Surveying and Mapping*, vol. 2013, no. 9, pp. 87-90, 2013.custom:[[[-]]] - 3 W. Cao, J. Hu, X. Yu, "A study on temperature interpolation methods based on GIS," in
*Proceedings of 2009 17th International Conference on Geoinformatics*, Fairfax, VA, 2009;pp. 1-5. doi:[[[10.1109/GEOINFORMATICS.2009.5293422]]] - 4 D. Cai, N. Guo, C. Li, "Interpolation of air temperature based on DEM over eastern region of Gansu,"
*Journal of Arid Meteorology*, vol. 27, no. 1, pp. 10-17, 2009.custom:[[[-]]] - 5 W. R. Tobler, "A computer movie simulating urban growth in the Detroit region,"
*Economic Geography*, vol. 46(Sup 1), pp. 234-240, 1970.doi:[[[10.2307/143141]]] - 6 E. Oktavia, I. W. Mustika, "Inverse distance weighting and kriging spatial interpolation for data center thermal monitoring," in
*Proceedings of 2016 1st International Conference on Information Technology*, Information Systems and Electrical Engineering (ICITISEE), Yogyakarta, Indonesia, 2016;pp. 69-74. doi:[[[10.1109/ICITISEE.2016.7803050]]] - 7 H. D. Khairnar, P. S. Shingare, S. Kale, "Accuracy evaluation of Cartosat-1 DEM using different Interpolation techniques for Pune area," in
*Proceedings of 2015 International Conference on Industrial Instrumentation and Control (ICIC)*, Pune, India, 2015;pp. 203-206. doi:[[[10.1109/IIC.2015.7150738]]] - 8 Z. Li, J. Gao, "Intelligent optimization on power values for inverse distance weighting," in
*Proceedings of 2013 International Conference on Information Science and Cloud Computing Companion*, Guangzhou, China, 2013;pp. 370-375. doi:[[[10.1109/ISCC-C.2013.81]]] - 9 Z. J. Liu, W. G. Guan, H. L. Hua, Z. D. Sun, "Location fingerprint database construction algorithm based on Kriging spatial interpolation,"
*Application Research of Computers*, vol. 33, no. 10, pp. 3139-3142, 2016.doi:[[[10.1109/SDF.2015.7347695]]] - 10 X. F. Yang, C. Q. Hu, "Application of ordinary Kriging method in the interpolation for seawater temperature profile,"
*Technical Acoustics*, vol. 10, no. 5, pp. 385-388, 2015.custom:[[[-]]] - 11 N. Yang, X. Feng, "Soil moisture estimation in Southeast Gansu with ordinary Kriging," in
*Proceedings of the World Automation Congress 2012*, Puerto Vallarta, Mexico, 2012;pp. 1-4. custom:[[[-]]] - 12 J. Bangay, M. Coleman, R. Batten, "Comparison of IEEE and CIGRE methods for predicting thermal behaviour of powerlines and their relevance to distribution networks," in
*Proceedings of 2015 IEEE Eindhoven PowerTech*, Eindhoven, The Netherlands, 2015;pp. 1-5. doi:[[[10.1109/PTC.2015.7232531]]] - 13 S. Sediva, M. Havlikova, "Comparison of GUM and Monte Carlo method for evaluation measurement uncertainty of indirect measurements," in
*Proceedings of the 14th International Carpathian Control Conference (ICCC)*, Rytro, Poland, 2013;pp. 325-329. doi:[[[10.1109/CarpathianCC.2013.6560563]]] - 14
*IEEE Standard for Calculating the Current-Temperature Relationship of Bare Overhead Conductors*, IEEE Standard 738-2012, 2013.doi:[[[10.1109/IEEESTD.2013.6692858]]] - 15
*CIGRE, The Thermal Behavior of Overhead Conductors (ELT_144_3)*, Paris: GIGRE, 1992.custom:[[[-]]]