Document Type : Research Paper


1 Assistant professor, Department of surveying, Tabriz University

2 MSC in Azad University


Extended Abstract
The construction of power transmission lines (PTLs) is one of the most important activities of the power industry of any nation in the development of power transmission network. Many technical, economic, environmental and social factors are involved in the issue of power transmission lines routing. These factors sometimes have co-directional and increasing effect and, in some cases, the effect is not co-directional and is even in opposite direction. Therefore, it is very important to determine the appropriate route for the power transmission lines which is in proportion with the needs and objectives of the project and to take the role of effective factors into consideration.
Materials & Methods
In this research, factors and criteria effective in power transmission lines routing were investigated in the form of three economic, access and maintenance objectives of the power transmission lines and adverse environmental effects. Given that, the problem is multi-objective, the criteria of more than one objective function must be optimized simultaneously. This data set includes Digital Elevation Model layers, land slope, villages, land use, roads, power transmission lines routes, geology, landslide, soil type, soil erosion, rivers and protected areas. In order to weight the factors and their combination, considering the features of each factor, the Fuzzy Analytic Hierarchy Process (FAHP) methods and the Weighted Linear Combination (WLC) have been used. FAHP is a very useful method for multiple-criteria decision-making in the fuzzy environment, which has provided substantial applications in recent years. Also, WLC is an analytical method which is used in the time of multi-criteria or more than one criterion decision-making. Any feature taken into consideration is called a criterion. Each criterion is weighted based on its significance. As previously mentioned, the issue of the power transmission lines routing is a multi-objective issue. Since there is no single solution to optimize each objective simultaneously, there is a set of Pareto optimal solutions. This solution is called non-dominated or Pareto optimal that the values of none of the objective functions can be improved without reducing the values of one or several other objectives. Considering the presented explanation, in order to determine the appropriate route with regard to the multi-objective problem, the Non-dominated Sorting Genetic Algorithm (NSGA-II) was used as an evolutionary multi-criteria decision-making method. The aforementioned model was used for the routing of 63 KV transmission lines between the two power stations of Shahid Salmani and Kiyasar in the city of Sari. The Arc GIS 10.3, Matlab and Google Earth were used in this research. Moreover, GIS was used for displaying, managing, analyzing and storing large and various datasets.
Discussion and Results
In order to solve the power transmission lines routing problem, different parts of this algorithm have been developed and expanded. In this research, an innovative operator was used for intersecting, mutating and non-dominated sorting. To determine the appropriate route based on the multi-objective, the algorithm must be run repeatedly and by using different parameters. In the present model, the objective functions are evaluated in every iteration in order to obtain the best result. In order to test and evaluate the efficiency of the algorithm, two samples of commonly used experiments in this field, i.e., repeatability test and parameter adjusting test, were used. The success rate of the repeatability and parameter adjusting tests for the presented model were 89% and 88%, respectively, which indicates the success of this model in both tests. The comparison of the best route generated from the proposed model with the existing power transmission line shows the improvement of the values of the objective functions, the reduction of 27 tension towers and 31 suspension towers and the reduction of about 6 km of the route length relative to the existing route of the power transmission line.
The Final Results of the Research Indicated that GIS capabilities can be properly integrated with the NSGA-II algorithm which, has appropriate capabilities such as, less computational complexity, high speed implementation of the algorithm and elitism process to solve the problem of routing the power transmission lines, and are exploited by applying appropriate changes in this algorithm, and by employing the FAHP method for any type of route and routing problem Particularly in the field of distribution, super-distribution and high voltage power lines. In future researches, in case of having more comprehensive information layers such as wind, temperature, precise geological information, agrology and technical and specialized calculations like tension and compression in the towers, combining the proposed method with dynamic programming algorithms in the tower locating sector and the possibility of taking various equipment in different environmental conditions into consideration can improve the process and quality of routing. Also, in the future works, it is possible to solve the problems of locating-routing of towers and passing the route of the transmission line by taking technical, environmental, social and economic factors into consideration, using modern methods or other methods for optimizing multi-objective evolutionary algorithms.


1. Ahmadi, S., Ebadi, H., & Valadan, Z. (2008). A new method for path finding of power transmission lines in geospatial information system using raster networks and minimum of mean algorithm. World Applied Sciences Journal, 3(2), 269-277.
2. Bagli, S., Geneletti, D., & Orsi, F. (2011). Routing of power lines through least-cost path analysis and multicriteria evaluation to minimize environmental impacts. Environmental Impact Assessment Review, 31(3), 234-239.
3. Beheshtifar, S., Alimohammadi, A., Mansourian, A. (2011). power transmission lines routing with multiobjective optimization approach. Journal of Remote Sensing & GIS, 3(4).
4. Coello, C. A. C., Lamont, G. B., & Van Veldhuizen, D. A. (2007). Evolutionary algorithms for solving multiobjective problems (Vol. 5). New York: Springer.
5. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2), 182-197.
6. EROĞLU, H., & Aydin, M. (2015). Optimization of electrical power transmission lines routing using AHP, fuzzy AHP, and GIS. Turkish Journal of Electrical Engineering and Computer Science, 23(5), 1418.
7. Gill, R. S. (2005). Electric transmission line routing using a decision landscape based methodology.
8. Haupt, R. L., & Haupt, S. E. (2004). Practical genetic algorithms. John Wiley & Sons.
9. Kanoh, H., & Hara, K. (2008, July). Hybrid genetic algorithm for dynamic multi-objective route planning with predicted traffic in a real-world road network. In Proceedings of the 10th annual conference on Genetic and evolutionary computation (pp. 657-664). ACM.
10. Kishore, T. S., & Singal, S. K. (2014). Optimal economic planning of power transmission lines: A review. Renewable and Sustainable Energy Reviews, 39, 949-974.
11. Monteiro, C., Ramírez-Rosado, I. J., Miranda, V., Zorzano-Santamaría, P. J., García-Garrido, E., & Fernández-Jiménez, L. A. (2005). GIS spatial analysis applied to electric line routing optimization. IEEE transactions on Power Delivery, 20(2), 934-942.
12. Qiu, J., Bian, X., & Li, X. (2004, June). Intelligent design of power transmission lines based on 3D GIS. In Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on (Vol. 6, pp. 5125-5128). IEEE.
13. Rastegar, A., Mansourian, A., Talee, M., Yari, D., Beheshtifar, S. (2015). Power-transmission lines Routing using the NSGA-II algorithm. Journal of Remote Sensing & GIS, 6(24).
14. Rodolfo Mendes, de Lima, et al. “Least-cost path analysis and multi-criteria assessment for routing electricity transmission lines.” IET Generation, Transmission & Distribution 10.16 (2016): 4222-4230.
15. Saadatseresht, M., Mansourian, A., & Talee, M. (2009). Evacuation planning using multiobjective evolutionary optimization approach. European Journal of Operational Research, 198(1), 305-314.
16. Srinivas, N., & Deb, K. (1994). Multi-objective optimization using non-dominated sorting in genetic algorithms. Evolutionary computation, 2(3), 221-248.
17. Teegala, S. K., & Singal, S. K. (2016). Optimal costing of overhead power transmission lines using genetic algorithms. International Journal of Electrical Power & Energy Systems, 83, 298-308.
18. Thu Bui, L. (Ed.). (2008). Multi-Objective Optimization in Computational Intelligence: Theory and Practice: Theory and Practice. IGI Global.
19. Umashankar, s. (2014). Optimization on shortest path finding for underground cable transmission lines routing using Gis. Journal of theoretical & applied information technology, 65(3).
20. Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C. M., & Da Fonseca, V. G. (2003). Performance assessment of multi-objective optimizers: An analysis and review. IEEE Transactions on evolutionary computation, 7(2), 117-132.