IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i9p1690-d228295.html
   My bibliography  Save this article

An Adaptive Particle Swarm Optimization Algorithm Based on Guiding Strategy and Its Application in Reactive Power Optimization

Author

Listed:
  • Fengli Jiang

    (College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China)

  • Yichi Zhang

    (School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

  • Yu Zhang

    (Anhui Provincial Laboratory of New Energy Utilization and Energy Conservation, Hefei University Technology, Hefei 230009, China)

  • Xiaomeng Liu

    (College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China)

  • Chunling Chen

    (College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China)

Abstract

An improved adaptive particle swarm algorithm with guiding strategy (GSAPSO) was proposed, and it was applied to solve the reactive power optimization (RPO). Four kinds of particles containing the main particles, double central particles, cooperative particles and chaos particles were introduced into the population of the developed algorithm, which was to decrease the randomness and promote search efficiency through guiding particle position updating. Moreover, the cluster focus distance-changing rate was responsible for dynamically adjusting inertia weight. Then the convergence rate and accuracy of this algorithm would be elevated by four functions, which would test effectively the proposed. Finally, the optimized algorithm was verified on the RPO of the IEEE 30-bus power system. The performance of PSO, Random weight particle swarm optimization (WPSO) and Linearly decreasing weight of the particle swarm optimization algorithm (LDWPSO) were identified as the referential information, the proposed GSAPSO was more efficient from the comparison. Calculation results demonstrated that higher quality solutions were obtained and convergence rate and accuracy was significantly higher with regard to the GSAPSO algorithm.

Suggested Citation

  • Fengli Jiang & Yichi Zhang & Yu Zhang & Xiaomeng Liu & Chunling Chen, 2019. "An Adaptive Particle Swarm Optimization Algorithm Based on Guiding Strategy and Its Application in Reactive Power Optimization," Energies, MDPI, vol. 12(9), pages 1-14, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:9:p:1690-:d:228295
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/9/1690/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/9/1690/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Niknam, Taher & Firouzi, Bahman Bahmani & Ostadi, Amir, 2010. "A new fuzzy adaptive particle swarm optimization for daily Volt/Var control in distribution networks considering distributed generators," Applied Energy, Elsevier, vol. 87(6), pages 1919-1928, June.
    2. Malekpour, Ahmad Reza & Tabatabaei, Sajad & Niknam, Taher, 2012. "Probabilistic approach to multi-objective Volt/Var control of distribution system considering hybrid fuel cell and wind energy sources using Improved Shuffled Frog Leaping Algorithm," Renewable Energy, Elsevier, vol. 39(1), pages 228-240.
    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. Peng Cheng & Zhiyu Xu & Ruiye Li & Chao Shi, 2022. "A Hybrid Taguchi Particle Swarm Optimization Algorithm for Reactive Power Optimization of Deep-Water Semi-Submersible Platforms with New Energy Sources," Energies, MDPI, vol. 15(13), pages 1-16, June.
    2. Park, Sung-Won & Son, Sung-Yong, 2020. "Interaction-based virtual power plant operation methodology for distribution system operator’s voltage management," Applied Energy, Elsevier, vol. 271(C).
    3. Seok-Il Go & Sang-Yun Yun & Seon-Ju Ahn & Joon-Ho Choi, 2020. "Voltage and Reactive Power Optimization Using a Simplified Linear Equations at Distribution Networks with DG," Energies, MDPI, vol. 13(13), pages 1-23, June.
    4. Ovidiu Ivanov & Bogdan-Constantin Neagu & Gheorghe Grigoras & Mihai Gavrilas, 2019. "Optimal Capacitor Bank Allocation in Electricity Distribution Networks Using Metaheuristic Algorithms," Energies, MDPI, vol. 12(22), pages 1-36, November.
    5. Hao He & Jia Li & Weizhe Zhao & Boyang Li & Yalong Li, 2022. "Reactive Power and Voltage Optimization of New-Energy Grid Based on the Improved Flower Pollination Algorithm," Energies, MDPI, vol. 15(10), pages 1-12, May.

    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. Samimi, Abouzar & Nikzad, Mehdi & Siano, Pierluigi, 2017. "Scenario-based stochastic framework for coupled active and reactive power market in smart distribution systems with demand response programs," Renewable Energy, Elsevier, vol. 109(C), pages 22-40.
    2. Mohagheghi, Erfan & Gabash, Aouss & Alramlawi, Mansour & Li, Pu, 2018. "Real-time optimal power flow with reactive power dispatch of wind stations using a reconciliation algorithm," Renewable Energy, Elsevier, vol. 126(C), pages 509-523.
    3. Zhao, Yincheng & Zhang, Guozhou & Hu, Weihao & Huang, Qi & Chen, Zhe & Blaabjerg, Frede, 2023. "Meta-learning based voltage control strategy for emergency faults of active distribution networks," Applied Energy, Elsevier, vol. 349(C).
    4. Mortazi, Mohammad & Moradi, Ahmad & Khosravi, Mohsen, 2020. "Simultaneous optimization of transformer tap changer and network capacitors to improve the distribution system’s static security considering distributed generation sources," MPRA Paper 109052, University Library of Munich, Germany, revised 01 Jul 2020.
    5. Glotić, Arnel & Zamuda, Aleš, 2015. "Short-term combined economic and emission hydrothermal optimization by surrogate differential evolution," Applied Energy, Elsevier, vol. 141(C), pages 42-56.
    6. Piotr Kacejko & Paweł Pijarski, 2021. "Optimal Voltage Control in MV Network with Distributed Generation," Energies, MDPI, vol. 14(2), pages 1-19, January.
    7. Dashti, Reza & Afsharnia, Saeed & Ghasemi, Hassan, 2010. "A new long term load management model for asset governance of electrical distribution systems," Applied Energy, Elsevier, vol. 87(12), pages 3661-3667, December.
    8. Dan Wang & Qing’e Hu & Jia Tang & Hongjie Jia & Yun Li & Shuang Gao & Menghua Fan, 2017. "A Kriging Model Based Optimization of Active Distribution Networks Considering Loss Reduction and Voltage Profile Improvement," Energies, MDPI, vol. 10(12), pages 1-19, December.
    9. Zhang, Zhengfa & da Silva, Filipe Faria & Guo, Yifei & Bak, Claus Leth & Chen, Zhe, 2021. "Double-layer stochastic model predictive voltage control in active distribution networks with high penetration of renewables," Applied Energy, Elsevier, vol. 302(C).
    10. Manbachi, Moein & Farhangi, Hassan & Palizban, Ali & Arzanpour, Siamak, 2016. "Smart grid adaptive energy conservation and optimization engine utilizing Particle Swarm Optimization and Fuzzification," Applied Energy, Elsevier, vol. 174(C), pages 69-79.
    11. Ahmed M. Nassef & Mohammad Ali Abdelkareem & Hussein M. Maghrabie & Ahmad Baroutaji, 2023. "Review of Metaheuristic Optimization Algorithms for Power Systems Problems," Sustainability, MDPI, vol. 15(12), pages 1-27, June.
    12. Nikzad, Mehdi & Samimi, Abouzar, 2021. "Integration of designing price-based demand response models into a stochastic bi-level scheduling of multiple energy carrier microgrids considering energy storage systems," Applied Energy, Elsevier, vol. 282(PA).
    13. Martinez-Rojas, Marcela & Sumper, Andreas & Gomis-Bellmunt, Oriol & Sudrià-Andreu, Antoni, 2011. "Reactive power dispatch in wind farms using particle swarm optimization technique and feasible solutions search," Applied Energy, Elsevier, vol. 88(12), pages 4678-4686.
    14. Grimm, Veronika & Grübel, Julia & Rückel, Bastian & Sölch, Christian & Zöttl, Gregor, 2020. "Storage investment and network expansion in distribution networks: The impact of regulatory frameworks," Applied Energy, Elsevier, vol. 262(C).
    15. Malekpour, Ahmad Reza & Niknam, Taher, 2011. "A probabilistic multi-objective daily Volt/Var control at distribution networks including renewable energy sources," Energy, Elsevier, vol. 36(5), pages 3477-3488.
    16. Suganthi, L. & Iniyan, S. & Samuel, Anand A., 2015. "Applications of fuzzy logic in renewable energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 585-607.
    17. Yu-Shan Cheng & Yi-Hua Liu & Holger C. Hesse & Maik Naumann & Cong Nam Truong & Andreas Jossen, 2018. "A PSO-Optimized Fuzzy Logic Control-Based Charging Method for Individual Household Battery Storage Systems within a Community," Energies, MDPI, vol. 11(2), pages 1-18, February.
    18. Tengku Juhana Tengku Hashim & Azah Mohamed, 2017. "Optimal coordinated voltage control in active distribution networks using backtracking search algorithm," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-20, October.
    19. Mahmud, Nasif & Zahedi, A., 2016. "Review of control strategies for voltage regulation of the smart distribution network with high penetration of renewable distributed generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 582-595.
    20. Niknam, Taher & Azizipanah-Abarghooee, Rasoul & Narimani, Mohammad Rasoul, 2012. "An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation," Applied Energy, Elsevier, vol. 99(C), pages 455-470.

    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:9:p:1690-:d:228295. 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.