IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i7p1290-d219885.html
   My bibliography  Save this article

Calculation of Distance Protection Settings in Mutually Coupled Transmission Lines: A Comparative Analysis

Author

Listed:
  • José de Jesús Jaramillo Serna

    (Departamento de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de Antioquia, calle 70 No 52-21, Medellín 050010, Colombia)

  • Jesús M. López-Lezama

    (Departamento de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de Antioquia, calle 70 No 52-21, Medellín 050010, Colombia)

Abstract

Protection of mutually coupled transmission lines poses special challenges to protection engineers, since the general principles outlining the expected performance of the protection elements are based on simplified versions of the issues affecting them. Distance protection elements are among the most commonly accepted methods for protecting mutually coupled transmission lines, however, the calculation of settings and the validation of their performance is a complex task, involving lots of simulations and analysis of results to come up with an acceptable solution. Though various methods have been proposed to deal with the problem of calculating the optimal settings of distance protection elements, there is still room for improvement, since these methods have been formulated for the general case considering simple transmission lines. In this paper, a variation of one of these methods is introduced to enhance the computing times and reliability of the solutions for the specific application of quadrilateral distance protection to mutually coupled transmission lines, through the characterization of the expected performance of the operating characteristic, and the formulation of the optimization problem and the solution method. The obtained results show the improvements in computing times and quality of the solution provided by the proposed algorithm.

Suggested Citation

  • José de Jesús Jaramillo Serna & Jesús M. López-Lezama, 2019. "Calculation of Distance Protection Settings in Mutually Coupled Transmission Lines: A Comparative Analysis," Energies, MDPI, vol. 12(7), pages 1-32, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1290-:d:219885
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/7/1290/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/7/1290/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bin Wang & Zhongping Yang & Fei Lin & Wei Zhao, 2014. "An Improved Genetic Algorithm for Optimal Stationary Energy Storage System Locating and Sizing," Energies, MDPI, vol. 7(10), pages 1-25, October.
    2. Seunghyeon Wang & Hyeonyong Hae & Juhyung Kim, 2018. "Development of Easily Accessible Electricity Consumption Model Using Open Data and GA-SVR," Energies, MDPI, vol. 11(2), pages 1-14, February.
    3. Ehsan Gholamalizadeh & Man-Hoe Kim, 2016. "Multi-Objective Optimization of a Solar Chimney Power Plant with Inclined Collector Roof Using Genetic Algorithm," Energies, MDPI, vol. 9(11), pages 1-14, November.
    4. Chao Li & Rongrong Zhai & Yongping Yang, 2017. "Optimization of a Heliostat Field Layout on Annual Basis Using a Hybrid Algorithm Combining Particle Swarm Optimization Algorithm and Genetic Algorithm," Energies, MDPI, vol. 10(11), pages 1-15, November.
    5. Tusongjiang Kari & Wensheng Gao & Ayiguzhali Tuluhong & Yilihamu Yaermaimaiti & Ziwei Zhang, 2018. "Mixed Kernel Function Support Vector Regression with Genetic Algorithm for Forecasting Dissolved Gas Content in Power Transformers," Energies, MDPI, vol. 11(9), pages 1-19, September.
    6. Liu, Yuan & Wang, RuiXue, 2016. "Study on network traffic forecast model of SVR optimized by GAFSA," Chaos, Solitons & Fractals, Elsevier, vol. 89(C), pages 153-159.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tusongjiang Kari & Wensheng Gao & Ayiguzhali Tuluhong & Yilihamu Yaermaimaiti & Ziwei Zhang, 2018. "Mixed Kernel Function Support Vector Regression with Genetic Algorithm for Forecasting Dissolved Gas Content in Power Transformers," Energies, MDPI, vol. 11(9), pages 1-19, September.
    2. Abdulelah Alkesaiberi & Fouzi Harrou & Ying Sun, 2022. "Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study," Energies, MDPI, vol. 15(7), pages 1-24, March.
    3. Regina Lamedica & Alessandro Ruvio & Laura Palagi & Nicola Mortelliti, 2020. "Optimal Siting and Sizing of Wayside Energy Storage Systems in a D.C. Railway Line," Energies, MDPI, vol. 13(23), pages 1-22, November.
    4. Huan Xia & Huaixin Chen & Zhongping Yang & Fei Lin & Bin Wang, 2015. "Optimal Energy Management, Location and Size for Stationary Energy Storage System in a Metro Line Based on Genetic Algorithm," Energies, MDPI, vol. 8(10), pages 1-23, October.
    5. Li, Chao & Zhai, Rongrong & Yang, Yongping & Patchigolla, Kumar & Oakey, John E. & Turner, Peter, 2019. "Annual performance analysis and optimization of a solar tower aided coal-fired power plant," Applied Energy, Elsevier, vol. 237(C), pages 440-456.
    6. Álvaro Jaramillo-Duque & Nicolás Muñoz-Galeano & José R. Ortiz-Castrillón & Jesús M. López-Lezama & Ricardo Albarracín-Sánchez, 2018. "Power Loss Minimization for Transformers Connected in Parallel with Taps Based on Power Chargeability Balance," Energies, MDPI, vol. 11(2), pages 1-12, February.
    7. Xiangbing Gao & Bo Jia & Gen Li & Xiaojing Ma, 2022. "Calorific Value Forecasting of Coal Gangue with Hybrid Kernel Function–Support Vector Regression and Genetic Algorithm," Energies, MDPI, vol. 15(18), pages 1-15, September.
    8. Chiou-Jye Huang & Ping-Huan Kuo, 2018. "A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems," Energies, MDPI, vol. 11(10), pages 1-20, October.
    9. Sun, Wei & Zhang, Junjian, 2022. "A novel carbon price prediction model based on optimized least square support vector machine combining characteristic-scale decomposition and phase space reconstruction," Energy, Elsevier, vol. 253(C).
    10. Shuai Su & Tao Tang & Yihui Wang, 2016. "Evaluation of Strategies to Reducing Traction Energy Consumption of Metro Systems Using an Optimal Train Control Simulation Model," Energies, MDPI, vol. 9(2), pages 1-19, February.
    11. Emad Abdelsalam & Feras Kafiah & Malek Alkasrawi & Ismael Al-Hinti & Ahmad Azzam, 2020. "Economic Study of Solar Chimney Power-Water Distillation Plant (SCPWDP)," Energies, MDPI, vol. 13(11), pages 1-14, June.
    12. Manuel J. Blanco & Victor Grigoriev & Kypros Milidonis & George Tsouloupas & Miguel Larrañeta & Manuel Silva, 2021. "Minimizing the Computational Effort to Optimize Solar Concentrators with the Open-Source Tools SunPATH and Tonatiuh++," Energies, MDPI, vol. 14(15), pages 1-20, July.
    13. Ping-Huan Kuo & Chiou-Jye Huang, 2018. "An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks," Sustainability, MDPI, vol. 10(4), pages 1-17, April.
    14. Sedighi, Ali Asghar & Deldoost, Zeynab & Karambasti, Bahram Mahjoob, 2020. "Effect of thermal energy storage layer porosity on performance of solar chimney power plant considering turbine pressure drop," Energy, Elsevier, vol. 194(C).
    15. Álvaro J. López-López & Ramón R. Pecharromán & Antonio Fernández-Cardador & Asunción P. Cucala, 2017. "Improving the Traffic Model to Be Used in the Optimisation of Mass Transit System Electrical Infrastructure," Energies, MDPI, vol. 10(8), pages 1-18, August.
    16. Khosravi, Soheil & Neshat, Elaheh & Saray, Rahim Khoshbakhti, 2023. "Thermodynamic analysis of a sorption-enhanced gasification process of municipal solid waste, integrated with concentrated solar power and thermal energy storage systems for co-generation of power and ," Renewable Energy, Elsevier, vol. 214(C), pages 140-153.
    17. David Roch-Dupré & Carlos Camacho-Gómez & Asunción P. Cucala & Silvia Jiménez-Fernández & Álvaro López-López & Antonio Portilla-Figueras & Ramón R. Pecharromán & Antonio Fernández-Cardador & Sancho Sa, 2021. "Optimal Location and Sizing of Energy Storage Systems in DC-Electrified Railway Lines Using a Coral Reefs Optimization Algorithm with Substrate Layers," Energies, MDPI, vol. 14(16), pages 1-19, August.
    18. Nawin Raj, 2022. "Prediction of Sea Level with Vertical Land Movement Correction Using Deep Learning," Mathematics, MDPI, vol. 10(23), pages 1-23, November.
    19. Fei Lin & Shihui Liu & Zhihong Yang & Yingying Zhao & Zhongping Yang & Hu Sun, 2016. "Multi-Train Energy Saving for Maximum Usage of Regenerative Energy by Dwell Time Optimization in Urban Rail Transit Using Genetic Algorithm," Energies, MDPI, vol. 9(3), pages 1-21, March.
    20. Ghimire, Sujan & Deo, Ravinesh C. & Raj, Nawin & Mi, Jianchun, 2019. "Wavelet-based 3-phase hybrid SVR model trained with satellite-derived predictors, particle swarm optimization and maximum overlap discrete wavelet transform for solar radiation prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1290-:d:219885. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.