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Optimized type-2 fuzzy frequency control for multi-area power systems

(2022) IEEE ACCESS. 10. p.6989-7002
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Abstract
The objective of this study is minimizing the frequency deviation due to the load variations and fluctuations of renewable energy resources. In this paper, a new type-2 fuzzy control (T2FLC) approach is presented for load frequency control (LFC) in power systems with multi-areas, demand response (DR), battery energy storage system (BESS), and wind farms. BESS is used to reduce the frequency deviations caused by wind energy, and DR is utilized to increase network stability due to fast load changes. The suggested T2FLC is online tuned based on the extended Kalman filter to improve the LFC accuracy in coordination of DR, BESS, and wind farms. The system dynamics are unknown, and the system Jacobian is extracted by online modeling with a simple multilayer perceptron neural network (MLP-NN). The designed LFC is evaluated through simulating on 10-machine New England 39-bus test system (NETS-39b) in four scenarios. Simulation results verifies the desired performance, indicating its superiority compared to a classical PI controllers, and type-1 fuzzy logic controllers (FLCs). The mean of improvement percentage is about 20%.
Keywords
electrical power systems, frequency control, Frequency control, Power system stability, Wind farms, Load modeling, Wind energy, Training, Fuzzy logic, Renewable energy, artificial intelligence, machine learning, type-2 adaptive neuro-fuzzy, extended Kalman filter, demand response, CONTROL STRATEGY, ENERGY-STORAGE, AGC, STABILITY

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MLA
Dokht Shakibjoo, Ali, et al. “Optimized Type-2 Fuzzy Frequency Control for Multi-Area Power Systems.” IEEE ACCESS, vol. 10, 2022, pp. 6989–7002, doi:10.1109/access.2021.3139259.
APA
Dokht Shakibjoo, A., Moradzadeh, M., ud Din, S., Mohammadzadeh, A., Mosavi, A. H., & Vandevelde, L. (2022). Optimized type-2 fuzzy frequency control for multi-area power systems. IEEE ACCESS, 10, 6989–7002. https://doi.org/10.1109/access.2021.3139259
Chicago author-date
Dokht Shakibjoo, Ali, Mohammad Moradzadeh, Sami ud Din, Ardashir Mohammadzadeh, Amir H. Mosavi, and Lieven Vandevelde. 2022. “Optimized Type-2 Fuzzy Frequency Control for Multi-Area Power Systems.” IEEE ACCESS 10: 6989–7002. https://doi.org/10.1109/access.2021.3139259.
Chicago author-date (all authors)
Dokht Shakibjoo, Ali, Mohammad Moradzadeh, Sami ud Din, Ardashir Mohammadzadeh, Amir H. Mosavi, and Lieven Vandevelde. 2022. “Optimized Type-2 Fuzzy Frequency Control for Multi-Area Power Systems.” IEEE ACCESS 10: 6989–7002. doi:10.1109/access.2021.3139259.
Vancouver
1.
Dokht Shakibjoo A, Moradzadeh M, ud Din S, Mohammadzadeh A, Mosavi AH, Vandevelde L. Optimized type-2 fuzzy frequency control for multi-area power systems. IEEE ACCESS. 2022;10:6989–7002.
IEEE
[1]
A. Dokht Shakibjoo, M. Moradzadeh, S. ud Din, A. Mohammadzadeh, A. H. Mosavi, and L. Vandevelde, “Optimized type-2 fuzzy frequency control for multi-area power systems,” IEEE ACCESS, vol. 10, pp. 6989–7002, 2022.
@article{8732497,
  abstract     = {{The objective of this study is minimizing the frequency deviation due to the load variations and fluctuations of renewable energy resources. In this paper, a new type-2 fuzzy control (T2FLC) approach is presented for load frequency control (LFC) in power systems with multi-areas, demand response (DR), battery energy storage system (BESS), and wind farms. BESS is used to reduce the frequency deviations caused by wind energy, and DR is utilized to increase network stability due to fast load changes. The suggested T2FLC is online tuned based on the extended Kalman filter to improve the LFC accuracy in coordination of DR, BESS, and wind farms. The system dynamics are unknown, and the system Jacobian is extracted by online modeling with a simple multilayer perceptron neural network (MLP-NN). The designed LFC is evaluated through simulating on 10-machine New England 39-bus test system (NETS-39b) in four scenarios. Simulation results verifies the desired performance, indicating its superiority compared to a classical PI controllers, and type-1 fuzzy logic controllers (FLCs). The mean of improvement percentage is about 20%.}},
  author       = {{Dokht Shakibjoo, Ali and Moradzadeh, Mohammad and ud Din, Sami and Mohammadzadeh, Ardashir and Mosavi, Amir H. and Vandevelde, Lieven}},
  issn         = {{2169-3536}},
  journal      = {{IEEE ACCESS}},
  keywords     = {{electrical power systems,frequency control,Frequency control,Power system stability,Wind farms,Load modeling,Wind energy,Training,Fuzzy logic,Renewable energy,artificial intelligence,machine learning,type-2 adaptive neuro-fuzzy,extended Kalman filter,demand response,CONTROL STRATEGY,ENERGY-STORAGE,AGC,STABILITY}},
  language     = {{eng}},
  pages        = {{6989--7002}},
  title        = {{Optimized type-2 fuzzy frequency control for multi-area power systems}},
  url          = {{http://doi.org/10.1109/access.2021.3139259}},
  volume       = {{10}},
  year         = {{2022}},
}

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