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A New Type‐3 Fuzzy PID for Energy Management in Microgrids

Author

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  • Weiping Fan
  • Ardashir Mohammadzadeh
  • Nasreen Kausar
  • Dragan Pamucar
  • Nasr Al Din Ide

Abstract

More recently, type‐3 (T3) fuzzy logic systems (FLSs) with better learning ability and uncertainty modeling have been presented. On other hand, the proportional‐integral‐derivative (PID) is commonly employed in most industrial control systems, because of its simplicity and efficiency. The measurement errors, nonlinearities, and uncertainties degrade the performance of conventional PIDs. In this study, for the first time, a new T3‐FLS‐based PID scheme with deep learning approach is introduced. In addition to rules, the parameters of fuzzy sets are also tuned such that a fast regulation efficiency is obtained. Unlike the most conventional approaches, the suggested tuning approach is done in an online scheme. Also, a nonsingleton fuzzification is suggested to reduce the effect of sensor errors. The proposed scheme is examined on a case‐study microgrid (MG), and its good frequency stabilization performance is demonstrated in various hard conditions such as variable load, unknown dynamics, and variation in renewable energy (RE) sources.

Suggested Citation

  • Weiping Fan & Ardashir Mohammadzadeh & Nasreen Kausar & Dragan Pamucar & Nasr Al Din Ide, 2022. "A New Type‐3 Fuzzy PID for Energy Management in Microgrids," Advances in Mathematical Physics, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:jnlamp:v:2022:y:2022:i:1:n:8737448
    DOI: 10.1155/2022/8737448
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    References listed on IDEAS

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    1. Mohamed Mokhtar & Mostafa I. Marei & Mariam A. Sameh & Mahmoud A. Attia, 2022. "An Adaptive Load Frequency Control for Power Systems with Renewable Energy Sources," Energies, MDPI, vol. 15(2), pages 1-22, January.
    2. Shangkun Deng & Chenguang Wang & Zhe Fu & Mingyue Wang, 2021. "An Intelligent System for Insider Trading Identification in Chinese Security Market," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 593-616, February.
    3. Khaizaran Abdulhussein Al Sumarmad & Nasri Sulaiman & Noor Izzri Abdul Wahab & Hashim Hizam, 2022. "Energy Management and Voltage Control in Microgrids Using Artificial Neural Networks, PID, and Fuzzy Logic Controllers," Energies, MDPI, vol. 15(1), pages 1-22, January.
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    1. Nasim Arabjazi & Mohsen Rostamy-Malkhalifeh & Farhad Hosseinzadeh Lotfi & Mohammad Hasan Behzadi, 2022. "Stability analysis with general fuzzy measure: An application to social security organizations," PLOS ONE, Public Library of Science, vol. 17(10), pages 1-24, October.

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