IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i1p255-d475263.html
   My bibliography  Save this article

Determining the Optimal Location and Number of Voltage Dip Monitoring Devices Using the Binary Bat Algorithm

Author

Listed:
  • Mario Šipoš

    (Croatian Military Academy “Dr. Franjo Tudjman”, 10000 Zagreb, Croatia)

  • Zvonimir Klaić

    (Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia)

  • Emmanuel Karlo Nyarko

    (Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia)

  • Krešimir Fekete

    (Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia)

Abstract

Voltage dips represent a significant power quality problem. The main cause of voltage dips and short-term interruptions is an electrical short circuit that occurs in transmission or distribution networks. Faults in the power system are stochastic by nature and the main cause of voltage dips. As faults in the transmission system can affect more customers than faults in the distribution system, to reduce the number of dips, it is not enough to invest in a small part of the transmission or distribution system. Only targeted investment in the whole (or a large part of the) power system will reduce voltage dips. Therefore, monitoring parts of the power system is very important. The ideal solution would be to cover the entire system so that a power quality (PQ) monitor is installed on each bus, but this method is not economically justified. This paper presents an advanced method for determining the optimal location and the optimal number of voltage dip measuring devices. The proposed algorithm uses a monitor reach area matrix created by short-circuit simulations, and the coefficient of the exposed area. Single-phase and three-phase short circuits are simulated in DIgSILENT software on the IEEE 39 bus test system, using international standard IEC 60909. After determining the monitor reach area matrix of all potential monitor positions, the binary bat algorithm with a coefficient of the exposed area of the system bus is used to minimize the proposed objective function, i.e., to determine the optimal location and number of measuring devices. Performance of the binary bat algorithm is compared to the mixed-integer linear programming algorithm solved by using the GNU Linear Programming Kit (GLPK).

Suggested Citation

  • Mario Šipoš & Zvonimir Klaić & Emmanuel Karlo Nyarko & Krešimir Fekete, 2021. "Determining the Optimal Location and Number of Voltage Dip Monitoring Devices Using the Binary Bat Algorithm," Energies, MDPI, vol. 14(1), pages 1-13, January.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:1:p:255-:d:475263
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/1/255/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/1/255/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Adarsh, B.R. & Raghunathan, T. & Jayabarathi, T. & Yang, Xin-She, 2016. "Economic dispatch using chaotic bat algorithm," Energy, Elsevier, vol. 96(C), pages 666-675.
    2. Haoyuan Sha & Fei Mei & Chenyu Zhang & Yi Pan & Jianyong Zheng, 2019. "Identification Method for Voltage Sags Based on K-means-Singular Value Decomposition and Least Squares Support Vector Machine," Energies, MDPI, vol. 12(6), pages 1-15, March.
    3. Ngo Minh Khoa & Le Van Dai, 2020. "Detection and Classification of Power Quality Disturbances in Power System Using Modified-Combination between the Stockwell Transform and Decision Tree Methods," Energies, MDPI, vol. 13(14), pages 1-30, July.
    4. Ferhat Ucar & Jose Cordova & Omer F. Alcin & Besir Dandil & Fikret Ata & Reza Arghandeh, 2019. "Bundle Extreme Learning Machine for Power Quality Analysis in Transmission Networks," Energies, MDPI, vol. 12(8), pages 1-26, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Michele Zanoni & Riccardo Chiumeo & Liliana Tenti & Massimo Volta, 2023. "What Else Do the Deep Learning Techniques Tell Us about Voltage Dips Validity? Regional-Level Assessments with the New QuEEN System Based on Real Network Configurations," Energies, MDPI, vol. 16(3), pages 1-24, January.

    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. Wenjian Hu & Mingxing Zhu & Huaying Zhang, 2022. "Application of Block Sparse Bayesian Learning in Power Quality Steady-State Data Compression," Energies, MDPI, vol. 15(7), pages 1-17, March.
    2. Wei Sun & Junjian Zhang, 2020. "Carbon Price Prediction Based on Ensemble Empirical Mode Decomposition and Extreme Learning Machine Optimized by Improved Bat Algorithm Considering Energy Price Factors," Energies, MDPI, vol. 13(13), pages 1-22, July.
    3. Naila & Shaikh Saaqib Haroon & Shahzad Hassan & Salman Amin & Intisar Ali Sajjad & Asad Waqar & Muhammad Aamir & Muneeb Yaqoob & Imtiaz Alam, 2018. "Multiple Fuel Machines Power Economic Dispatch Using Stud Differential Evolution," Energies, MDPI, vol. 11(6), pages 1-20, May.
    4. K. Padmanathan & N. Kamalakannan & P. Sanjeevikumar & F. Blaabjerg & J. B. Holm-Nielsen & G. Uma & R. Arul & R. Rajesh & A. Srinivasan & J. Baskaran, 2019. "Conceptual Framework of Antecedents to Trends on Permanent Magnet Synchronous Generators for Wind Energy Conversion Systems," Energies, MDPI, vol. 12(13), pages 1-39, July.
    5. Jianzhong Xu & Fu Yan & Kumchol Yun & Lifei Su & Fengshu Li & Jun Guan, 2019. "Noninferior Solution Grey Wolf Optimizer with an Independent Local Search Mechanism for Solving Economic Load Dispatch Problems," Energies, MDPI, vol. 12(12), pages 1-26, June.
    6. Vikram Kumar Kamboj & Challa Leela Kumari & Sarbjeet Kaur Bath & Deepak Prashar & Mamoon Rashid & Sultan S. Alshamrani & Ahmed Saeed AlGhamdi, 2022. "A Cost-Effective Solution for Non-Convex Economic Load Dispatch Problems in Power Systems Using Slime Mould Algorithm," Sustainability, MDPI, vol. 14(5), pages 1-36, February.
    7. Modiri-Delshad, Mostafa & Aghay Kaboli, S. Hr. & Taslimi-Renani, Ehsan & Rahim, Nasrudin Abd, 2016. "Backtracking search algorithm for solving economic dispatch problems with valve-point effects and multiple fuel options," Energy, Elsevier, vol. 116(P1), pages 637-649.
    8. Artvin-Darien Gonzalez-Abreu & Roque-Alfredo Osornio-Rios & Arturo-Yosimar Jaen-Cuellar & Miguel Delgado-Prieto & Jose-Alfonso Antonino-Daviu & Athanasios Karlis, 2022. "Advances in Power Quality Analysis Techniques for Electrical Machines and Drives: A Review," Energies, MDPI, vol. 15(5), pages 1-26, March.
    9. Biswas, Partha P. & Suganthan, P.N. & Qu, B.Y. & Amaratunga, Gehan A.J., 2018. "Multiobjective economic-environmental power dispatch with stochastic wind-solar-small hydro power," Energy, Elsevier, vol. 150(C), pages 1039-1057.
    10. Al-Bahrani, Loau Tawfak & Chandra Patra, Jagdish, 2018. "Multi-gradient PSO algorithm for optimization of multimodal, discontinuous and non-convex fuel cost function of thermal generating units under various power constraints in smart power grid," Energy, Elsevier, vol. 147(C), pages 1070-1091.
    11. Kheshti, Mostafa & Kang, Xiaoning & Bie, Zhaohong & Jiao, Zaibin & Wang, Xiuli, 2017. "An effective Lightning Flash Algorithm solution to large scale non-convex economic dispatch with valve-point and multiple fuel options on generation units," Energy, Elsevier, vol. 129(C), pages 1-15.
    12. Zou, Dexuan & Li, Steven & Wang, Gai-Ge & Li, Zongyan & Ouyang, Haibin, 2016. "An improved differential evolution algorithm for the economic load dispatch problems with or without valve-point effects," Applied Energy, Elsevier, vol. 181(C), pages 375-390.
    13. Artvin-Darien Gonzalez-Abreu & Miguel Delgado-Prieto & Roque-Alfredo Osornio-Rios & Juan-Jose Saucedo-Dorantes & Rene-de-Jesus Romero-Troncoso, 2021. "A Novel Deep Learning-Based Diagnosis Method Applied to Power Quality Disturbances," Energies, MDPI, vol. 14(10), pages 1-17, May.
    14. Mohammad Lotfi Akbarabadi & Reza Sirjani, 2023. "Achieving Sustainability and Cost-Effectiveness in Power Generation: Multi-Objective Dispatch of Solar, Wind, and Hydro Units," Sustainability, MDPI, vol. 15(3), pages 1-33, January.
    15. Xu, Shengping & Xiong, Guojiang & Mohamed, Ali Wagdy & Bouchekara, Houssem R.E.H., 2022. "Forgetting velocity based improved comprehensive learning particle swarm optimization for non-convex economic dispatch problems with valve-point effects and multi-fuel options," Energy, Elsevier, vol. 256(C).
    16. Faisal Tariq & Salem Alelyani & Ghulam Abbas & Ayman Qahmash & Mohammad Rashid Hussain, 2020. "Solving Renewables-Integrated Economic Load Dispatch Problem by Variant of Metaheuristic Bat-Inspired Algorithm," Energies, MDPI, vol. 13(23), pages 1-36, November.
    17. Singh, Nirbhow Jap & Dhillon, J.S. & Kothari, D.P., 2017. "Surrogate worth trade-off method for multi-objective thermal power load dispatch," Energy, Elsevier, vol. 138(C), pages 1112-1123.
    18. Ren, Hao & Li, Jun & Chen, Huiling & Li, ChenYang, 2021. "Adaptive levy-assisted salp swarm algorithm: Analysis and optimization case studies," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 181(C), pages 380-409.
    19. Thanh Long Duong & Phuong Duy Nguyen & Van-Duc Phan & Dieu Ngoc Vo & Thang Trung Nguyen, 2019. "Optimal Load Dispatch in Competitive Electricity Market by Using Different Models of Hopfield Lagrange Network," Energies, MDPI, vol. 12(15), pages 1-24, July.
    20. Zakarya Oubrahim & Yassine Amirat & Mohamed Benbouzid & Mohammed Ouassaid, 2023. "Power Quality Disturbances Characterization Using Signal Processing and Pattern Recognition Techniques: A Comprehensive Review," Energies, MDPI, vol. 16(6), pages 1-41, March.

    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:14:y:2021:i:1:p:255-:d:475263. 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.