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Fermat-curve based fuzzy inference system for the fuzzy logic controller performance optimization in load frequency control application

Author

Listed:
  • Hadi Vatankhah Ghadim

    (University of Tabriz)

  • Mehrdad Tarafdar Hagh

    (University of Tabriz)

  • Saeid Ghassem Zadeh

    (University of Tabriz)

Abstract

One of the main challenges in the security of energy supply in modern power systems is the frequency deviation. Appearance of an imbalance between the demand and supply of electrical energy is the main reason for any change in the frequency level of the grid. Thus, the Load Frequency Control (LFC) operation is usually performed automatically to restore the stability in the frequency level of the system. LFC has been studied with different controllers previously. However, this study concentrates on proposing a new configuration for the Fuzzy Logic Controller (FLC) to be implemented in the modeling of a test two-area power system under two different operational conditions and challenges to analyze its performance. A Third-Order Fermat Curve-based Fuzzy Inference System (TOFC-FIS) is designed for the FLC with the aim of optimizing the performance of type-I FLCs in the LFC application. The motive for this study was to mathematize the FIS of FLC to prepare a basis for further performance enhancement using optimization algorithms. Thus, the proposed FIS is optimized using a Neural Network (NN) to create an Adaptive Neuro-Fuzzy Inference System for the FLC in the studied scenarios. The results of typical and NN-trained TOFC-FIS-based FLC illustrate the considerable improvement in performance indexes of LFC in two-area power systems compared to both conventional and intelligent control methods.

Suggested Citation

  • Hadi Vatankhah Ghadim & Mehrdad Tarafdar Hagh & Saeid Ghassem Zadeh, 2023. "Fermat-curve based fuzzy inference system for the fuzzy logic controller performance optimization in load frequency control application," Fuzzy Optimization and Decision Making, Springer, vol. 22(4), pages 555-586, December.
    • Handle: RePEc:spr:fuzodm:v:22:y:2023:i:4:d:10.1007_s10700-022-09402-2
    DOI: 10.1007/s10700-022-09402-2
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    References listed on IDEAS

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    1. A. Javier Barragán & Juan M. Enrique & Antonio J. Calderón & José M. Andújar, 2018. "Discovering the dynamic behavior of unknown systems using fuzzy logic," Fuzzy Optimization and Decision Making, Springer, vol. 17(4), pages 421-445, December.
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    Cited by:

    1. Jian Luo & Yukai Zheng & Tao Hong & An Luo & Xueqi Yang, 2024. "Fuzzy support vector regressions for short-term load forecasting," Fuzzy Optimization and Decision Making, Springer, vol. 23(3), pages 363-385, September.

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