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Use of Kalman Filter and Its Variants in State Estimation: A Review

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Artificial Intelligence for a Sustainable Industry 4.0

Abstract

The exponential increase in demand for energy globally has created an urge to increase the production and its efficient supply. Microgrids are one such solution to meet the energy needs of a growing population. Microgrids are stand-alone distributed systems [DS] which are based on renewable energy for better environmental stability. These are highly reliable and cheap, hence are widely being used. With an increase in the popularity of microgrids, the increasing need for effective monitoring and control of the grids is required. This can be achieved through state estimation [SE]. This brings out the need for distributed system state estimation [DSSE]. One of the ways to perform DSSE is through Kalman filter [KF]. This paper focuses on Kalman filter and its variants. The authors move further to discuss all the possible ways to use the variants and to compare them to suggest the ideal filter for various requirements.

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Acknowledgments

We are indebted to Abhinandan A. J, Bhoomika C. M, and Anjanakumari B. T. of School of ECE, REVA University, Bangalore, for their valuable inputs and enduring support during the work. We would also like to express our deep gratitude to REVA University for their unconditional support in carrying this work.

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Authors and Affiliations

  1. School of Electrical and Electronics Engineering, REVA University, Bengaluru, Karnataka, India

    Neha Kumari & Rohan Kulkarni

  2. School of Multidisciplinary Studies, REVA University, Bengaluru, Karnataka, India

    Mohammed Riyaz Ahmed

  3. SCSE Galgotias University, Greater Noida, Uttar Pradesh, India

    Naresh Kumar

Authors
  1. Neha Kumari
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  2. Rohan Kulkarni
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  3. Mohammed Riyaz Ahmed
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  4. Naresh Kumar
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Editor information

Editors and Affiliations

  1. G L Bajaj Institute of Technology and Management, Gr. Noida, India

    Shashank Awasthi

  2. University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Las Palmas, Spain

    Carlos M. Travieso-González

  3. National Institute of Technology (NIT), Durgapur, India

    Goutam Sanyal

  4. G L Bajaj Institute of Technology and Management, Greater Noida, UP, India

    Dinesh Kumar Singh

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Kumari, N., Kulkarni, R., Ahmed, M.R., Kumar, N. (2021). Use of Kalman Filter and Its Variants in State Estimation: A Review. In: Awasthi, S., Travieso-González, C.M., Sanyal, G., Kumar Singh, D. (eds) Artificial Intelligence for a Sustainable Industry 4.0. Springer, Cham. https://doi.org/10.1007/978-3-030-77070-9_13

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  • DOI: https://doi.org/10.1007/978-3-030-77070-9_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77069-3

  • Online ISBN: 978-3-030-77070-9

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