Situational Awareness Enhancement in Transmission Lines using NI Based PMU
Kunja Bihari Swain1, Satya Sopan Mahato2, M Vamshi Krishna3, Murthy Cherukuri4
1Kunja Bihari Swain, Electronics and Communication Engineering, Centurion University of Technology and Management, Paralakhemundi, Odisha, India.
2Satya Sopan Mahato, Electronics and Communication Engineering, National Institute of Science and Technology, Berhampur, Odisha, India.
3M Vamshi Krishna, Electronics and Communication Engineering, Centurion University of Technology and Management, Paralakhemundi, Odisha ,India.
4Murthy Cherukuri*, Electrical and Electronics Engineering, National Institute of Science and Technology, Berhampur, Odisha, India.
Manuscript received on December 13, 2019. | Revised Manuscript received on December 25, 2019. | Manuscript published on January 10, 2020. | PP: 2987-2997 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8058019320/2020©BEIESP | DOI: 10.35940/ijitee.C8058.019320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Phasor Measurement Units (PMUs) are becoming prominent in enhancing the situational awareness in wide area power system monitoring, thereby playing a vital role in its protection and control. This paper focuses on enhancing the situational awareness of transmission line using National Instruments (NI) based PMU. The data measured by the virtual PMU is used for fault detection and fault classification. The detection and the classification in the LabVIEW platform are performed using the Fourier Transform and support vector machines (SVMs) respectively. The proposed methodology has been applied on a laboratory set up consisting of transmission line, three phase load and an NI based PMU. The enhanced situational awareness in the detection and classification of transmission line faults helps in restoration of the transmission line as quickly as possible and trigger wide area control actions to maintain power system stability against the disturbances created by a fault.
Keywords: Phasor Measurement Unit, Park’s Transform, Machine Learning, NI cRIO, Situational Awareness.
Scope of the Article: Machine Learning