IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i15p3974-d393452.html
   My bibliography  Save this article

Smart Grid Monitoring by Wireless Sensors Using Binary Logistic Regression

Author

Listed:
  • Hariprasath Manoharan

    (Department of Electronics and Communication Engineering, Audisankara College of Engineering and Technology, Gudur 524 101, India)

  • Yuvaraja Teekaraman

    (Faculty of Energy and Power Engineering, South Ural State University, Chelyabinsk 454 080, Russia)

  • Irina Kirpichnikova

    (Faculty of Energy and Power Engineering, South Ural State University, Chelyabinsk 454 080, Russia)

  • Ramya Kuppusamy

    (Department of Electrical & Electronics Engineering, Sri Sairam College of Engineering, Bangalore 562106, India)

  • Srete Nikolovski

    (Power Engineering Department, Faculty of Electrical Engineering, Computer Science and Information Technology, University of Osijek, 31000 Osijek, Croatia)

  • Hamid Reza Baghaee

    (Department of Electrical Engineering, Amirkabir University of Technology, Tehran 15875–4413, Iran)

Abstract

This article focuses on addressing the data aggregation faults caused by the Phasor Measuring Unit (PMU) by installing Wireless Sensor Networks (WSN) in the grid. All data that is monitored by PMU should be sent to the base station for further action. But the data that is sent from PMU does not reach the main server properly in many situations. To avoid this situation, a sensor-based technology has been introduced in the proposed method for sensing the values that are monitored by PMU. Also, the basic parameters that are necessary for determining optimal solutions like energy consumption, distance and cost have been calculated for wireless sensors, whereas, for PMU optimal placements with cost analysis have been restrained. For analyzing and improving the accuracy of the proposed method, an effective Binary Logistic Regression (BLR) algorithm has been integrated with an objective function. The sensor will report all measured PMU values to an Online Monitoring System (OMS). To examine the effectiveness of the proposed method, the examined values are visualized in MATLAB and results prove that the proposed method using BLR is more effective than existing methods in terms of all parametric values and the much improved results have been obtained at a rate of 81.2%.

Suggested Citation

  • Hariprasath Manoharan & Yuvaraja Teekaraman & Irina Kirpichnikova & Ramya Kuppusamy & Srete Nikolovski & Hamid Reza Baghaee, 2020. "Smart Grid Monitoring by Wireless Sensors Using Binary Logistic Regression," Energies, MDPI, vol. 13(15), pages 1-12, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:15:p:3974-:d:393452
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/15/3974/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/15/3974/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lai, Chun Sing & McCulloch, Malcolm D., 2017. "Levelized cost of electricity for solar photovoltaic and electrical energy storage," Applied Energy, Elsevier, vol. 190(C), pages 191-203.
    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. Renchu Guan & Aoqing Wang & Yanchun Liang & Jiasheng Fu & Xiaosong Han, 2022. "International Natural Gas Price Trends Prediction with Historical Prices and Related News," Energies, MDPI, vol. 15(10), pages 1-14, May.
    2. Fernanda Moura Quintão Silva & Menaouar Berrehil El Kattel & Igor Amariz Pires & Thales Alexandre Carvalho Maia, 2022. "Development of a Supervisory System Using Open-Source for a Power Micro-Grid Composed of a Photovoltaic (PV) Plant Connected to a Battery Energy Storage System and Loads," Energies, MDPI, vol. 15(22), pages 1-22, November.
    3. Loup-Noé Lévy & Jérémie Bosom & Guillaume Guerard & Soufian Ben Amor & Marc Bui & Hai Tran, 2022. "DevOps Model Appproach for Monitoring Smart Energy Systems," Energies, MDPI, vol. 15(15), pages 1-27, July.
    4. Adnan Khattak & Rasool Bukhsh & Sheraz Aslam & Ayman Yafoz & Omar Alghushairy & Raed Alsini, 2022. "A Hybrid Deep Learning-Based Model for Detection of Electricity Losses Using Big Data in Power Systems," Sustainability, MDPI, vol. 14(20), pages 1-20, October.

    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. Koecklin, Manuel Tong & Longoria, Genaro & Fitiwi, Desta Z. & DeCarolis, Joseph F. & Curtis, John, 2021. "Public acceptance of renewable electricity generation and transmission network developments: Insights from Ireland," Energy Policy, Elsevier, vol. 151(C).
    2. Tong Koecklin, Manuel & Fitiwi, Desta & de Carolis, Joseph F. & Curtis, John, 2020. "Renewable electricity generation and transmission network developments in light of public opposition: Insights from Ireland," Papers WP653, Economic and Social Research Institute (ESRI).
    3. Fuquan Zhao & Feiqi Liu & Han Hao & Zongwei Liu, 2020. "Carbon Emission Reduction Strategy for Energy Users in China," Sustainability, MDPI, vol. 12(16), pages 1-19, August.
    4. Donovin D. Lewis & Aron Patrick & Evan S. Jones & Rosemary E. Alden & Abdullah Al Hadi & Malcolm D. McCulloch & Dan M. Ionel, 2023. "Decarbonization Analysis for Thermal Generation and Regionally Integrated Large-Scale Renewables Based on Minutely Optimal Dispatch with a Kentucky Case Study," Energies, MDPI, vol. 16(4), pages 1-23, February.
    5. Mostafavi Tehrani, S. Saeed & Shoraka, Yashar & Nithyanandam, Karthik & Taylor, Robert A., 2019. "Shell-and-tube or packed bed thermal energy storage systems integrated with a concentrated solar power: A techno-economic comparison of sensible and latent heat systems," Applied Energy, Elsevier, vol. 238(C), pages 887-910.
    6. Zhang, Yijie & Ma, Tao & Elia Campana, Pietro & Yamaguchi, Yohei & Dai, Yanjun, 2020. "A techno-economic sizing method for grid-connected household photovoltaic battery systems," Applied Energy, Elsevier, vol. 269(C).
    7. Ahsan, Syed M. & Khan, Hassan A. & Hassan, Naveed-ul & Arif, Syed M. & Lie, Tek-Tjing, 2020. "Optimized power dispatch for solar photovoltaic-storage system with multiple buildings in bilateral contracts," Applied Energy, Elsevier, vol. 273(C).
    8. Gul, Eid & Baldinelli, Giorgio & Bartocci, Pietro & Shamim, Tariq & Domenighini, Piergiovanni & Cotana, Franco & Wang, Jinwen & Fantozzi, Francesco & Bianchi, Francesco, 2023. "Transition toward net zero emissions - Integration and optimization of renewable energy sources: Solar, hydro, and biomass with the local grid station in central Italy," Renewable Energy, Elsevier, vol. 207(C), pages 672-686.
    9. Lai, Chun Sing & Locatelli, Giorgio, 2021. "Economic and financial appraisal of novel large-scale energy storage technologies," Energy, Elsevier, vol. 214(C).
    10. Ga-Eun Jung & Hae-Jin Sung & Minh-Chau Dinh & Minwon Park & Hyunkyoung Shin, 2021. "A Comparative Analysis of Economics of PMSG and SCSG Floating Offshore Wind Farms," Energies, MDPI, vol. 14(5), pages 1-18, March.
    11. Zhong, Like & Yao, Erren & Zou, Hansen & Xi, Guang, 2022. "Thermodynamic and economic analysis of a directly solar-driven power-to-methane system by detailed distributed parameter method," Applied Energy, Elsevier, vol. 312(C).
    12. Chen, Huadong & Wang, Can & Cai, Wenjia & Wang, Jianhui, 2018. "Simulating the impact of investment preference on low-carbon transition in power sector," Applied Energy, Elsevier, vol. 217(C), pages 440-455.
    13. Ioannis E. Kosmadakis & Costas Elmasides, 2021. "A Sizing Method for PV–Battery–Generator Systems for Off-Grid Applications Based on the LCOE," Energies, MDPI, vol. 14(7), pages 1-29, April.
    14. Gul, Eid & Baldinelli, Giorgio & Bartocci, Pietro & Bianchi, Francesco & Domenghini, Piergiovanni & Cotana, Franco & Wang, Jinwen, 2022. "A techno-economic analysis of a solar PV and DC battery storage system for a community energy sharing," Energy, Elsevier, vol. 244(PB).
    15. Joshua M. Pearce & Nelson Sommerfeldt, 2021. "Economics of Grid-Tied Solar Photovoltaic Systems Coupled to Heat Pumps: The Case of Northern Climates of the U.S. and Canada," Energies, MDPI, vol. 14(4), pages 1-17, February.
    16. Shuxin Mao & Sha Qiu & Tao Li & Mingfang Tang & Hongbing Deng & Hua Zheng, 2020. "Using Characteristic Energy to Study Rural Ethnic Minorities’ Household Energy Consumption and Its Impact Factors in Chongqing, China," Sustainability, MDPI, vol. 12(17), pages 1-14, August.
    17. Gonzalez-Moreno, A. & Marcos, J. & de la Parra, I. & Marroyo, L., 2022. "A PV ramp-rate control strategy to extend battery lifespan using forecasting," Applied Energy, Elsevier, vol. 323(C).
    18. Mignacca, B. & Locatelli, G., 2020. "Economics and finance of Small Modular Reactors: A systematic review and research agenda," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
    19. Henni, Sarah & Staudt, Philipp & Weinhardt, Christof, 2021. "A sharing economy for residential communities with PV-coupled battery storage: Benefits, pricing and participant matching," Applied Energy, Elsevier, vol. 301(C).
    20. Daniel Lugo-Laguna & Angel Arcos-Vargas & Fernando Nuñez-Hernandez, 2021. "A European Assessment of the Solar Energy Cost: Key Factors and Optimal Technology," Sustainability, MDPI, vol. 13(6), pages 1-25, 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:13:y:2020:i:15:p:3974-:d:393452. 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.