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Revolutionizing Smart Grids with Big Data Analytics: A Case Study on Integrating Renewable Energy and Predicting Faults

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Data Analytics for Smart Grids Applications—A Key to Smart City Development

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 247))

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Abstract

Smart grids are contemporary and prevailing power set-ups that use progressive tools and techniques to monitor and accomplish electricity resource and mandate. The incorporation of renewable energy sources and the increasing demand for electricity make it essential to have a reliable and efficient smart grid system. Big data analytics has become a valuable tool in managing and optimizing smart grids. In this case study, we explore the purpose of big data analytics in managing a smart grid system. The case study takes place in a large city where the demand for electricity is high, and renewable energy sources are being integrated into the grid. The smart grid system has sensors and smart meters that collect data on electricity usage, voltage, and frequency. The data is processed using big data analytics techniques to optimize the smart grid system's performance. The first step in the case study is to identify patterns in electricity consumption. By analysing historical data, the researchers identify peak usage times and patterns in electricity consumption across different neighbourhoods. This information is used to optimize the distribution of electricity across the grid, reducing energy wastage and improving reliability. The next step is to use big data analytics to optimize the assimilation of renewable energy sources into the grid. The advanced and proposed Renewable energy sources such as solar and wind energy are intermittent and variable. By analysing real-time data on weather patterns, energy generation, and demand, the researchers can optimize the distribution of renewable energy across the grid, ensuring that the system remains stable and reliable. It further explores the use of big data analytics in predicting and detecting faults in the smart grid system. By analysing real-time data on voltage, frequency, and other parameters, the researchers can detect faults before they occur, reducing downtime and improving the reliability of the smart grid system. Finally, big data analytics has become an essential tool in managing and optimizing smart grid systems. This case study demonstrates how the big data analytics, and its applications can be used to progress the distribution of electricity, integrate renewable energy sources, and predict and detect faults in a smart grid system. By using big data-based analytics, we can create a more reliable, efficient, and sustainable and renewable electricity network.

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Author information

Authors and Affiliations

  1. Department of AI&ML, J.B. Institute of Engineering and Technology, Hyderabad, Telangana, India

    G. Arun Sampaul Thomas

  2. Department of EEE, Velammal Institute of Technology, Panchetti, Tiruvallur Dt, Tamilnadu, India

    S. Muthukaruppasamy

  3. Department of CSE, College of Engineering Guindy, Anna University, Chennai, Tamilnadu, India

    K. Saravanan

  4. School of Electrical and Computer Engineering, Dire Dawa University Technology Institute, Dire Dawa, Ethiopia

    Negasa Muleta

Authors
  1. G. Arun Sampaul Thomas
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  2. S. Muthukaruppasamy
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  3. K. Saravanan
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  4. Negasa Muleta
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Corresponding author

Correspondence to G. Arun Sampaul Thomas .

Editor information

Editors and Affiliations

  1. Delhi - NCR Campus, SRM Institute of Science and Technology, Ghaziabad, India

    Devendra Kumar Sharma

  2. Delhi - NCR Campus, SRM Institute of Science and Technology, Ghaziabad, India

    Rohit Sharma

  3. Dept. of Embedded Systems Engineering, Incheon National University, Incheon, Korea (Republic of)

    Gwanggil Jeon

  4. Computer Science&Engineering, GIET University, Gunupur, Gunupur, India

    Raghvendra Kumar

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Arun Sampaul Thomas, G., Muthukaruppasamy, S., Saravanan, K., Muleta, N. (2023). Revolutionizing Smart Grids with Big Data Analytics: A Case Study on Integrating Renewable Energy and Predicting Faults. In: Kumar Sharma, D., Sharma, R., Jeon, G., Kumar, R. (eds) Data Analytics for Smart Grids Applications—A Key to Smart City Development. Intelligent Systems Reference Library, vol 247. Springer, Cham. https://doi.org/10.1007/978-3-031-46092-0_11

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