Smart Grid Monitoring by Wireless Sensors Using Binary Logistic Regression

Hariprasath Manoharan, Yuvaraja Teekaraman, Irina Kirpichnikova, Ramya Kuppusamy, Srete Nikolovski and Hamid Reza Baghaee
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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

Energies, 2020, vol. 13, issue 15, 1-12

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%.

Keywords: smart grids (intelligent networks); phasor machine learning; binary logistic regression; wireless network; Sensors (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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