Optimization of induced voltage on a buried pipeline from HV power lines using grasshopper algorithm (GOA)
More details
Hide details
1
University of Amar Telidji LAGHOUAT
Submission date: 2021-01-03
Final revision date: 2021-03-24
Acceptance date: 2021-06-09
Online publication date: 2021-06-11
Publication date: 2021-06-11
Diagnostyka 2021;22(2):105-115
KEYWORDS
TOPICS
ABSTRACT
The buried metallic pipeline which parallels the HV power line is subject to induced voltages from the AC currents flowing in the conductors, these voltages can affect the operating personnel, pipeline associated equipment, and the pipeline integrity. This paper analyses the induced voltage and current on the buried pipeline running parallel to HV power lines. It also presents an optimization procedure of different parameters that affect the level of the induced voltage in the pipeline during normal operating conditions. A comparison study between the proposed optimization algorithms (GOA, GE, DE, and PSO) is done with a maximization of a given objective function. The simulation results establish that the GOA algorithm provides faster convergence and better solutions than the other optimization algorithms. Thus, the statistical analysis according to Friedman’s rank test confirmed the superiority of this proposed algorithm. Furthermore, the results show that the parameters optimization of the metallic pipeline is an effective approach to provide the best performance for mitigation which is generally sufficient to reduce the induced voltage experienced by the buried metallic pipeline to enforce the safety limit.
REFERENCES (46)
1.
Dawalibi F, Southey R. Analysis of electrical interference from power lines to gas pipelines part i: computational methods. IEEE Transactions on Power Delivery. 1989; 14(3).
https://doi.org/10.1109/61.326....
2.
Al-Badi AH, Al-Rizzo HM. Simulation of electromagnetic coupling on pipelines close to overhead transmission lines: A Parametric Study. Journal of Communications Software and Systems. 2005;1(2):116–125.
https://doi.org/10.24138/jcoms....
3.
Micu DD, Christoforidis GC, Czumbil L. AC interference on pipelines due to double circuit power lines: A detailed study. Electric Power Systems Research. 2013;103:1–8.
https://doi.org/10.1016/j.epsr....
4.
Chen M, Liu S, Zhu J, Xie C, Tian H, Li J. Effects and characteristics of ac interference on parallel underground pipelines caused by an AC electrified railway. Energies. 2018;11(9)1–24.
https://doi.org/10.3390/en1109....
5.
Gouda OE, El Dein AZ, El-Gabalawy MAH. Effect of electromagnetic field of overhead transmission lines on the metallic gas pipe-lines. Electr. Power Syst. Res. 2013; 103:129–136.
https://doi.org/10.1016/j.epsr....
6.
Daconti JR. Electrical risks in transmission line pipeline shared rights of way. Power Technology. International Symposium on Environmental Concerns in Rights of Way. 2004.
7.
Li Y, Dawalibi FP, Ma J. Electromagnetic interference caused by a power system network and a neighboring pipeline, Proceedings of the 62nd Annual Meeting of the American Power Conference, Chicago. 2000:311-316.
8.
CIGRE Working Group 36.02. Guide on the Influence of High Voltage AC Power Systems on Metallic Pipelines. CIGRE Technical Brochure no. 095. 1995.
9.
Australian New Zealand Standard, Electrical Hazards on Metallic Pipelines,Standards Australia, Standards New Zealand, (AS/NZS- 4853), (2000).
10.
NACE: Mitigation of Alternating Current and Lightning Effects on Metallic Structures and Corrosion Control Systems. Report No.: 21021-SG (2007).
11.
EN 50443: Effects of Electromagnetic Interference on Pipelines caused by High Voltage A.C. Railway Systems and/or High Voltage A.C. Power Supply Systems. CENELEC Report No.: ICS 33.040.20; 33.100.01 (2009).
12.
Saremi S, Mirjalili S, Lewis A. Grasshopper optimisation algorithm: Theory and application. Advances in Engineering Software. 2017; 105:30-47.
https://doi.org/10.1016/j.adve....
13.
Jeng-Shyang Pan. A multi-group grasshopper optimisation algorithm for application in capacitated vehicle routing problem. Data Science and Pattern Recognition, Ubiquitous International. 2020;4(1):41-55.
14.
El-Shorbagy MA, Ayoub AY. Integrating grasshopper optimization algorithm with local search for solving data clustering problems. International Journal of Computational Intelligence Systems. 2021;14(1): 783–793.
https://doi.org/10.2991/ijcis.....
15.
Talaat M, Hatata AY, Alsayyari Abdulaziz S, Alblawi Adel. A smart load management system based on the grasshopper optimization algorithm using the under-frequency load shedding approach. Energy journal. 2020;190(C).
https://doi.org/10.1016/j.ener....
16.
Steczek M, Jefimowski W, Szelag A. application of grasshopper optimization algorithmfor selective harmonics elimination in low-frequency voltage source inverter. Energies journal. 2020;13(23):6426.
https://doi.org/10.3390/en1323....
17.
Montano J, Tobón AF, Villegas JP, Durango M. Grasshopper optimization algorithm for parameter estimation of photovoltaic modules based on the single diode model, International Journal of Energy and Environmental Engineering. 2020; 11( 3):367–375.
https://doi.org/10.1007/s40095....
18.
Manpreet Kaur, Er. Ravinder Kumar. Overloading of transmission lines management by using grasshopper optimization algorithm. International Journal of Scientific & Engineering Research. 2018;9(6):1086-1091.
19.
Wu J, Wang H, Li N. Distributed trajectory optimization for multiple solar-powered UAVs target tracking in urban environment by adaptive grasshopper optimization algorithm. Aerospace Science and Technology. 2017;70: 497–510.
20.
Jumani TA, Mustafa MW, Rasid MM, Mirjat NH, Leghari ZH, Saeed MS. Optimal voltage and frequency control of an islanded microgrid using grasshopper optimization algorithm. Energies journal. 2018;11:3191.
https://doi.org/:10.3390/en111....
21.
Hangwei Feng, Hong Ni, Ran Zhao, Xiaoyong Zhu. An enhanced grasshopper optimization algorithm to the bin packing problem. Journal of Control Science and Engineering. 2020;2020:3894987.
https://doi.org/10.1155/2020/3....
22.
Luo Jie, Chen Huiling, Zhang Qian, Xu Yueting, Huang Hui, Zhao Xuehua. An improved grasshopper optimization algorithm with application to financial stress prediction, Applied Mathematical Modelling. 2018;64: 654-668.
https://doi.org/10.1016/j.apm.....
23.
Djekidel R, Mahi D. Calculation and analysis of inductive coupling effects for HV transmission lines on aerial pipelines. Przegląd Elektrotechniczny. 2014;90(9):151-156.
https://doi.org/10.12915/pe.20....
24.
Gupta A. A study on high voltage AC power transmission line electric and magnetic field coupling with nearby metallic pipelines. Indian Institute of Science, India, 2008 (M.Sc. thesis, Dept. Elect. Eng).
25.
Southey RD, Dawalibi FP. Computer modelling of ac interference problems for the most cost-effective solutions. NACE International. 1998;8:564.
26.
Tleis N. Power systems modelling and fault analysis theory and practice. Elsevier Ltd, 2008.
27.
Hyun-Goo Lee, Tae-Hyun Ha, Yoon-Cheol Ha, Jeong-Hyo Bae, Dae Kyeong Kim. Analysis of voltage induced by distribution lines on gas pipelines. lntematlonel Conference on Power System Technology. 2004:598-601.
https://doi.org/10.1109/ICPST.....
28.
Adedeji KB. Pipeline grounding condition: A control of pipe-to-soil potential for AC interference induced corrosion reduction. The 25th Southern African Universities Power Engineering Conference (SAUPEC), Stellenbosch, South Africa. 2017:577-58.
29.
Djekidel R, Bessedik SA, Spiteri P, Mahi P. Passive mitigation for magnetic coupling between HV power line and aerial pipeline using PSO algorithms optimization, Electric Power Systems Research. 2018;165:18-26.
https://doi.org/10.1016/j.epsr....
30.
Leslie Bortels, Marius Purcar. Manage Pipeline integrity by predicting and mitigating high voltage AC interference. Analele Universităţii din Oradea Fascicula de Energetică. 2009; 15:2:189-195.
31.
Mafarja M, Aljarah I, Faris H, Hammouri AI, Al-Zoubi AM. Mirjalili S. Binary grasshopper optimisation algorithm approaches for feature selection problems. Expert Systems with Applications. 2019;117:267-286.
https://doi.org/10.1016/j.eswa....
32.
Mafarja M, Aljarah I, Heidari AA, Hammouri AI, Faris H, Al-Zoubi AM, Mirjalili A. Evolutionary population dynamics and grasshopper optimization approaches for feature selection problems, Knowledge-Based Systems. 2017;145:25-45.
https://doi.org/10.1016/j.knos....
33.
Ran Zhao, Hong Ni, Hangwei Feng, Xiaoyong Zhu, Yaqin Song. An improved Grasshopper optimization algorithm for task scheduling problems, International Journal of Innovative Computing, Information and Control. 2019;15(5): 1967–1987.
https://doi.org/10.24507/ijici....
34.
Hadeel Tariq Ibrahim, Wamidh Jalil Mazher, Osman N. Ucan, Oguz Bayat. A grasshopper optimizer approach for feature selection and optimizing SVM parameters utilizing real biomedical data sets, Neural Computing and Applications. 2019;31(2):5965–5974.
https://doi.org/10.1007/s00521....
35.
Hicham Deghbouch, Fatima Debbat. A hybrid bees algorithm with grasshopper optimization algorithm for optimal deployment of wireless sensor networks. Inteligencia Artificial Journal. 2021;24(67):18-35.
https://doi.org/10.4114/intart....
36.
Pan JS, Wang X, Chu SC, Nguyen TT. A multi-group grasshopper optimisation algorithm for application in capacitated vehicle routing problem. Data Science and Pattern Recognition. 2020;4(1):41-56.
37.
Djekidel R, Chouca A, Hadjadj A. Efficiency of some optimisation approaches with the charge simulation method for calculating the electric field under extra high voltage power lines. IET Generation, Transmission & Distribution. 2017; 11(17):4167-4174.
https://doi.org/10.1049/iet-gt....
38.
Chandrasekar K, Ramana NV. Performance Comparison of GA, DE, PSO and SA Approaches in Enhancement of Total Transfer Capability using FACTS Devices. Journal of Electrical Engineering and Technology. 2012;7(4):493-500.
https://doi.org/10.5370/JEET.2....
39.
Ibrahim Ahmed Saleh, Asmaa H. AL_Bayati, Kifaa Hadi Thanoon. Measure the software quality based on grasshopper optimization algorithm. International Journal of Computing and Digital Systems. 2020;10: 2-8.
40.
Ahmed A. Ewees A, Mohamed Abd Elaziz , Essam H. Houssein. Improved grasshopper optimization algorithm using opposition-based learning, Expert Systems With Applications. 2018;112:156–172.
https://doi.org/10.1016/j.eswa....
41.
Haoran Zhao, Huiru Zhao, Sen Guo. Short-term wind electric power forecasting using a novel multi-stage intelligent algorithm. Sustainability Journal, 10(3). 2018:881.
https://doi.org/10.3390/su1003....
42.
Hastie T, Tibshirani R, Friedman J. The elements of statistical learning, data mining, inference, and prediction. Springer, New York, 2001:533.
https://doi.org/10.1002/sim.16....
43.
Joaquin D, Salvador G, Daniel M, Francisco, H. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation. 2011;1(1):3-18.
https://doi.org/10.1016/j.swev....
44.
Moayedi H, Nguyen H, Kok Foong L. Nonlinear evolutionary swarm intelligence of grasshopper optimization algorithm and gray wolf optimization for weight adjustment of neural network. Engineering with Computers. 2021;37:1265–1275.
https://doi.org/10.1007/s00366....
45.
Derrac J, García S, Molina D, Herrera F. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation. 2011;1(1): 3–18.
https://doi.org/10.1016/j.swev....
46.
Akram Seifi, Mohammad Ehteram, Vijay P. Singh, Amir Mosav, Modeling and uncertainty analysis of ground water level using six evolutionary optimization algorithms hybridized with ANFIS, SVM, and ANN. Sustainability Journal. 2020; 12(10):4023.
https://doi.org/10.3390/su1210....