Data Mining and Machine Learning Technique in Contingency Analysis of Power System with UPFC

Authors

  • Ravi V. Angadi Department of Electrical and Electronics Engineering, Presidency University, Bengaluru, Karnataka, India
  • Suresh Babu Daram Department of Electrical and Electronics Engineering, Sree Vidyanikethan Engineering College, Tirupati, AP, India
  • P. S Venkataramu Department of Electrical and Electronics Engineering, Presidency University, Bengaluru, Karnataka, India
  • V Joshi Manohar Department of Electrical and Electronics Engineering, Presidency University, Bengaluru, Karnataka, India

DOI:

https://doi.org/10.13052/dgaej2156-3306.3751

Keywords:

Contingency analysis, LVSI, data mining, machine learning, severity prediction

Abstract

This paper explains how to predict the severity of the system by connecting with and without a unified power flow controller under different load and n-1 conditions by calculating the LVSI and by the application of data mining and machine learning techniques. A large amount of data will occur during the process of contingency analysis and it is necessary that how to get this to the system value to assess the severity of the system. With the help of data mining and machine learning techniques, analysis of data generated from the simulations for different load conditions are carried out and is used to estimate the severity of the line. In this, the IEEE 30 Bus System is used by calculating the line voltage stability index and the simulation work is carried out by using MATLAB and WEKA software.

Author Biographies

Ravi V. Angadi, Department of Electrical and Electronics Engineering, Presidency University, Bengaluru, Karnataka, India

Ravi V. Angadi was born in India on July 31, 1988. He received his B.E in Electrical & Electronics Engineering from VTU, Belagavi, Karnataka (India) in 2010, and M.Tech degree in Power Electronics from JNTUA, Anantapur (India) in 2014 and pursuing Ph.D at Presidency University, Bengaluru. He is currently working as an Assistant Professor in the Department of Electrical & Electronics Engineering at Presidency University, Bengaluru, Karnataka, (India). He has guided UG students’ projects sponsored by KSCST, DST and VTU-RGS and one project has been applied for Patent. He has participated in various International & National workshops, conferences, Project Expo. He has published many papers in National/ International journals/conferences. He was a Governing Council Member at SSCE, Bengaluru during the AY 2017-18. Mr. Ravi is a life member of IE (I) & ISTE, MIEEE.

Suresh Babu Daram, Department of Electrical and Electronics Engineering, Sree Vidyanikethan Engineering College, Tirupati, AP, India

Suresh Babu Daram was born in India on Jan 9, 1985. He received his B.Tech in Electrical & Electronics engineering from JNTU Hyderabad (India) in 2006, M.Tech in Power Systems Engg from Acharya Nagarjuna University (India) in 2009 and Ph.D in Power Systems from Visvesvaraya Technological University, Belgaum (India) in 2018. He was Assistant Professor in the Dept. of Electrical & Electronics at GGITM Bhopal from 2009–2015. Currently he is Professor in Dept. of Electrical & Electronics at Sree Vidyanikethan Engineering College, Tirupati (A.P), India. He has received Best Teacher Award from MPCST in 2014 and has best paper award in International Conference “Dr. M. H. Rashid Best paper award” in 2016, “National Conference best paper award” in 2016, “National Techno Conference best paper award” in 2020 . He has published more than 55 National/International Journal/conference papers/Book Chapters. His research interests include energy management systems, power system optimization, and voltage instability studies incorporating FACTS controllers’ power system security analysis, data analytics and machine learning. Dr. Suresh is a member of IEEE, AMIE (India), IAENG, CSTA, IACSIT,IRED and Student Member-ASTM.

P. S Venkataramu, Department of Electrical and Electronics Engineering, Presidency University, Bengaluru, Karnataka, India

P. S. Venkataramu received his Graduation in Electrical Engineering from the Institute of Engineers (India), M.Tech degree in Power Systems from Mysore University, India and Ph.D from Visvesvaraya Technological University, Belgaum, India. He was employed as an Electrical Engineer in the Goa state Electricity Department and worked for 15 years in various capacities. He was primarily involved in carrying power system operational and planning studies for the regional grid system. He was also a visiting faculty in the Goa college of Engineering. He worked as a faculty in various positions at School of Electrical Sciences, Vellore Institute of Technology, Vellore, India from 1997 to 2007. He was a Professor and founder Principal of Gyan Ganga Institute of Technology and Management, Bhopal, India from 2007 to 2015. He was Professor and Dean Internal Quality Assurance Cell, REVA University, Bangalore from 2015 to 2018. Currently he is working as Professor and Dean Academics in Presidency University, Bangalore. He has received many Best Teacher Awards and has many best paper awards. He has published more than 50 National/International Journal/conference papers/Book Chapters. His research interest includes AI application to power system and distribution system automation. Dr. Venkataramu is a Fellow of the IE (I), ISTE, Member of IEEE and System Society of India.

V Joshi Manohar, Department of Electrical and Electronics Engineering, Presidency University, Bengaluru, Karnataka, India

V. Joshi Manohar Currently working as Professor & HoD at Presidency University, Itgalpur, India. He received his Ph.D. in Electrical Drives from Jawaharlal Nehru Technological University, Anantapur, India in 2015. M. Tech in Power Electronics from VTU, Belgaum, KA, India in 2004 and B.Tech degree in Electrical & Electronics Engineering from Nagarjuna University, Guntur, AP, India, 2000. His research area includes the control of multi-level inverters using soft computing techniques. Reactive power compensation at low switching frequency and AI-based electrical drive control. He’s a Life Member of the ISTE and Fellow Institute of Engineers.

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Published

2022-05-25

How to Cite

Angadi, R. V. ., Daram, S. B. ., Venkataramu, P. S. ., & Manohar, V. J. . (2022). Data Mining and Machine Learning Technique in Contingency Analysis of Power System with UPFC. Distributed Generation &Amp; Alternative Energy Journal, 37(05), 1305–1328. https://doi.org/10.13052/dgaej2156-3306.3751

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