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A Hybrid GA-Adaptive Particle Swarm Optimization Based Tuning of Unscented Kalman Filter for Harmonic Estimation

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Swarm, Evolutionary, and Memetic Computing (SEMCCO 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6466))

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Abstract

This paper proposes Hybrid Genetic Algorithm (GA)-Adaptive Particle Swarm Optimization (APSO) aided Unscented Kalman Filter (UKF) to estimate the harmonic components present in power system voltage/current waveforms. The initial choice of the process and measurement error covariance matrices Q and R (called tuning of the filter) plays a vital role in removal of noise. Hence, hybrid GA-APSO algorithm is used to estimate the error covariance matrices by minimizing the Root Mean Square Error(RMSE) of the UKF. Simulation results are presented to demonstrate the estimation accuracy is significantly improved in comparison with that of conventional UKF.

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References

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Author information

Authors and Affiliations

  1. Department of ECE, National Institute of Technology, Warangal, India

    Ravi Kumar Jatoth & Gogulamudi Anudeep Reddy

Authors
  1. Ravi Kumar Jatoth
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  2. Gogulamudi Anudeep Reddy
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Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology, New Delhi, India

    Bijaya Ketan Panigrahi

  2. Department of Electronics and Communication Engineering, Jadavpur University, 700032, Kolkata, West Bengal, India

    Swagatam Das

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore

    Ponnuthurai Nagaratnam Suganthan

  4. Department of Electrical and Electronics Engineering, SRM University, Chennai, Tamil Nadu, India

    Subhransu Sekhar Dash

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Jatoth, R.K., Reddy, G.A. (2010). A Hybrid GA-Adaptive Particle Swarm Optimization Based Tuning of Unscented Kalman Filter for Harmonic Estimation. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2010. Lecture Notes in Computer Science, vol 6466. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17563-3_46

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  • DOI: https://doi.org/10.1007/978-3-642-17563-3_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17562-6

  • Online ISBN: 978-3-642-17563-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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