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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
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    References listed on IDEAS

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    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.
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    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.

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