IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i7p1917-d159494.html
   My bibliography  Save this article

Optimal Micro-PMU Placement Using Mutual Information Theory in Distribution Networks

Author

Listed:
  • Zhi Wu

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Xiao Du

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Wei Gu

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Ping Ling

    (State Grid Shanghai Electric Power Co, Electric Power Research Institute, Shanghai 200090, China)

  • Jinsong Liu

    (State Grid Shanghai Electric Power Co, Electric Power Research Institute, Shanghai 200090, China)

  • Chen Fang

    (State Grid Shanghai Electric Power Co, Electric Power Research Institute, Shanghai 200090, China)

Abstract

Micro-phasor measurement unit (μPMU) is under fast development and becoming more and more important for application in future distribution networks. It is unrealistic and unaffordable to place all buses with μPMUs because of the high costs, leading to the necessity of determining optimal placement with minimal numbers of μPMUs in the distribution system. An optimal μPMU placement (OPP) based on the information entropy evaluation and node selection strategy (IENS) using greedy algorithm is presented in this paper. The uncertainties of distributed generations (DGs) and pseudo measurements are taken into consideration, and the two-point estimation method (2PEM) is utilized for solving stochastic state estimation problems. The set of buses selected by improved IENS, which can minimize the uncertainties of network and obtain system observability is considered as the optimal deployment of μPMUs. The proposed method utilizes the measurements of smart meters and pseudo measurements of load powers in the distribution systems to reduce the number of μPMUs and enhance the observability of the network. The results of the simulations prove the effectiveness of the proposed algorithm with the comparison of traditional topological methods for the OPP problem. The improved IENS method can obtain the optimal complete and incomplete μPMU placement in the distribution systems.

Suggested Citation

  • Zhi Wu & Xiao Du & Wei Gu & Ping Ling & Jinsong Liu & Chen Fang, 2018. "Optimal Micro-PMU Placement Using Mutual Information Theory in Distribution Networks," Energies, MDPI, vol. 11(7), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1917-:d:159494
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/7/1917/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/7/1917/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Michal Wydra, 2018. "Performance and Accuracy Investigation of the Two-Step Algorithm for Power System State and Line Temperature Estimation," Energies, MDPI, vol. 11(4), pages 1-20, April.
    2. Nazari-Heris, M. & Mohammadi-Ivatloo, B., 2015. "Application of heuristic algorithms to optimal PMU placement in electric power systems: An updated review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 214-228.
    3. Ou, Ting-Chia & Hong, Chih-Ming, 2014. "Dynamic operation and control of microgrid hybrid power systems," Energy, Elsevier, vol. 66(C), pages 314-323.
    4. Ting-Chia Ou & Kai-Hung Lu & Chiou-Jye Huang, 2017. "Improvement of Transient Stability in a Hybrid Power Multi-System Using a Designed NIDC (Novel Intelligent Damping Controller)," Energies, MDPI, vol. 10(4), pages 1-16, 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. Pedro C. Leal & Diogo M. V. P. Ferreira & Pedro M. S. Carvalho, 2022. "Data Analytics for Admittance Matrix Estimation of Poorly Monitored Distribution Grids," Energies, MDPI, vol. 15(23), pages 1-9, November.
    2. Henrique Pires Corrêa & Rafael Ribeiro de Carvalho Vaz & Flávio Henrique Teles Vieira & Sérgio Granato de Araújo, 2019. "Reliability Based Genetic Algorithm Applied to Allocation of Fiber Optics Links for Power Grid Automation," Energies, MDPI, vol. 12(11), pages 1-26, May.
    3. Francisco G. Montoya & Raúl Baños & Alfredo Alcayde & Francisco Manzano-Agugliaro, 2019. "Optimization Methods Applied to Power Systems," Energies, MDPI, vol. 12(12), pages 1-8, June.
    4. Xiaosheng Peng & Kai Cheng & Jianxun Lang & Zuowei Zhang & Tao Cai & Shanxu Duan, 2021. "Short-Term Wind Power Prediction for Wind Farm Clusters Based on SFFS Feature Selection and BLSTM Deep Learning," Energies, MDPI, vol. 14(7), pages 1-18, March.

    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. Pengfei Wang & Jialiang Yi & Mansoureh Zangiabadi & Pádraig Lyons & Phil Taylor, 2017. "Evaluation of Voltage Control Approaches for Future Smart Distribution Networks," Energies, MDPI, vol. 10(8), pages 1-17, August.
    2. Yongsheng Cao & Guanglin Zhang & Demin Li & Lin Wang & Zongpeng Li, 2018. "Online Energy Management and Heterogeneous Task Scheduling for Smart Communities with Residential Cogeneration and Renewable Energy," Energies, MDPI, vol. 11(8), pages 1-20, August.
    3. Thai-Thanh Nguyen & Hyeong-Jun Yoo & Hak-Man Kim & Huy Nguyen-Duc, 2018. "Direct Phase Angle and Voltage Amplitude Model Predictive Control of a Power Converter for Microgrid Applications," Energies, MDPI, vol. 11(9), pages 1-21, August.
    4. Jinming Jiang & Xindong Wei & Weijun Gao & Soichiro Kuroki & Zhonghui Liu, 2018. "Reliability and Maintenance Prioritization Analysis of Combined Cooling, Heating and Power Systems," Energies, MDPI, vol. 11(6), pages 1-24, June.
    5. Hongyue Li & Xihuai Wang & Jianmei Xiao, 2018. "Differential Evolution-Based Load Frequency Robust Control for Micro-Grids with Energy Storage Systems," Energies, MDPI, vol. 11(7), pages 1-19, June.
    6. Il-Seok Choi & Akhtar Hussain & Van-Hai Bui & Hak-Man Kim, 2018. "A Multi-Agent System-Based Approach for Optimal Operation of Building Microgrids with Rooftop Greenhouse," Energies, MDPI, vol. 11(7), pages 1-24, July.
    7. Chen, J.J. & Zhao, Y.L. & Peng, K. & Wu, P.Z., 2017. "Optimal trade-off planning for wind-solar power day-ahead scheduling under uncertainties," Energy, Elsevier, vol. 141(C), pages 1969-1981.
    8. Alenka Hren & Franc Mihalič, 2018. "An Improved SPWM-Based Control with Over-Modulation Strategy of the Third Harmonic Elimination for a Single-Phase Inverter," Energies, MDPI, vol. 11(4), pages 1-20, April.
    9. Geng, Zhiqiang & Li, Yanan & Han, Yongming & Zhu, Qunxiong, 2018. "A novel self-organizing cosine similarity learning network: An application to production prediction of petrochemical systems," Energy, Elsevier, vol. 142(C), pages 400-410.
    10. Gao, Zhikun & Yu, Junqi & Zhao, Anjun & Hu, Qun & Yang, Siyuan, 2022. "A hybrid method of cooling load forecasting for large commercial building based on extreme learning machine," Energy, Elsevier, vol. 238(PC).
    11. Amirreza Naderipour & Zulkurnain Abdul-Malek & Mohammad Reza Miveh & Mohammad Jafar Hadidian Moghaddam & Akhtar Kalam & Foad. H. Gandoman, 2018. "A Harmonic Compensation Strategy in a Grid-Connected Photovoltaic System Using Zero-Sequence Control," Energies, MDPI, vol. 11(10), pages 1-18, October.
    12. Qian Liu & Rui Wang & Yan Zhang & Guohua Wu & Jianmai Shi, 2018. "An Optimal and Distributed Demand Response Strategy for Energy Internet Management," Energies, MDPI, vol. 11(1), pages 1-16, January.
    13. Pouria Sheikhahmadi & Ramyar Mafakheri & Salah Bahramara & Maziar Yazdani Damavandi & João P. S. Catalão, 2018. "Risk-Based Two-Stage Stochastic Optimization Problem of Micro-Grid Operation with Renewables and Incentive-Based Demand Response Programs," Energies, MDPI, vol. 11(3), pages 1-17, March.
    14. Athila Quaresma Santos & Zheng Ma & Casper Gellert Olsen & Bo Nørregaard Jørgensen, 2018. "Framework for Microgrid Design Using Social, Economic, and Technical Analysis," Energies, MDPI, vol. 11(10), pages 1-22, October.
    15. Mohamed A. Hassan & Muhammed Y. Worku & Abdelfattah A. Eladl & Mohammed A. Abido, 2021. "Dynamic Stability Performance of Autonomous Microgrid Involving High Penetration Level of Constant Power Loads," Mathematics, MDPI, vol. 9(9), pages 1-23, April.
    16. Majid Mehrasa & Edris Pouresmaeil & Bahram Pournazarian & Amir Sepehr & Mousa Marzband & João P. S. Catalão, 2018. "Synchronous Resonant Control Technique to Address Power Grid Instability Problems Due to High Renewables Penetration," Energies, MDPI, vol. 11(9), pages 1-18, September.
    17. Mamun, A. & Sivasubramaniam, A. & Fathy, H.K., 2018. "Collective learning of lithium-ion aging model parameters for battery health-conscious demand response in datacenters," Energy, Elsevier, vol. 154(C), pages 80-95.
    18. Ghasemi, Ahmad, 2018. "Coordination of pumped-storage unit and irrigation system with intermittent wind generation for intelligent energy management of an agricultural microgrid," Energy, Elsevier, vol. 142(C), pages 1-13.
    19. Reza Sirjani, 2017. "Optimal Capacitor Placement in Wind Farms by Considering Harmonics Using Discrete Lightning Search Algorithm," Sustainability, MDPI, vol. 9(9), pages 1-20, September.
    20. Mohamed A. Tolba & Hegazy Rezk & Vladimir Tulsky & Ahmed A. Zaki Diab & Almoataz Y. Abdelaziz & Artem Vanin, 2018. "Impact of Optimum Allocation of Renewable Distributed Generations on Distribution Networks Based on Different Optimization Algorithms," Energies, MDPI, vol. 11(1), pages 1-33, January.

    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:11:y:2018:i:7:p:1917-:d:159494. 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.