Journal of Advanced Marine Engineering and Technology

The Korean Society of Marine Engineering (한국마린엔지니어링학회)
권호 표지

Design of an Adaptive Neuro-Fuzzy Inference Precompensator for Load Frequency Control of Two-Area Power Systems

2지역 전력계통의 부하주파수 제어를 위한 적응 뉴로 퍼지추론 보상기 설계

  • 정형환 (동아대학교 공대 전기전자컴퓨터공학부) ;
  • 정문규 (동아대학교 공대 전기전자컴퓨터공학부) ;
  • 한길만 (동아대학교 공대 전기전자컴퓨터공학부)
  • Published : 2000.03.01
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we design an adaptive neuro-fuzzy inference system(ANFIS) precompensator for load frequency control of 2-area power systems. While proportional integral derivative (PID) controllers are used in power systems, they may have some problems because of high nonlinearities of the power systems. So, a neuro-fuzzy-based precompensation scheme is incorporated with a convectional PID controller to obtain robustness to the nonlinearities. The proposed precompensation technique can be easily implemented by adding a precompensator to an existing PID controller. The applied neruo-fuzzy inference system precompensator uses a hybrid learning algorithm. This algorithm is to use both a gradient descent method to optimize the premise parameters and a least squares method to solve for the consequent parameters. Simulation results show that the proposed control technique is superior to a conventional Ziegler-Nichols PID controller in dynamic responses about load disturbances.

Keywords

References

  1. IEEE Trans. Power App. and Syst. v.Pas-80 no.4 The Megawatt-Frequency Control Problem : A New Approach Via Optimal Control Theory Fosha, C.E.;Elgerd, O.I.
  2. IEEE Trans. Power App. and Syst. v.Pas-89 no.4 Optimum Megawatt-Frequency Control of Multiarea Electric Energy Systems Elgerd, O.I.;Fosha, C.E.
  3. Power System Stability and Control Kundur, P.
  4. Neural Networks v.7 no.1 Application of Neural Networks to Load-Frequency Control in Power Systems Beaufays, F.;A M, Y.;Widow, B.
  5. IEEE Trans. Power Syst. v.13 no.2 On Robust Control Analysis and Design for Load Frequency Regulation Stankovic, A.M.;Tadmor, G.;Sakharuk, T.A.
  6. Power System Analysis Saadat, H.
  7. 1999 IEEE International Fuzzy System Conference Application of Fuzzy PID Controller for Stabilization of Power System Chung, H.H.;Lee, J.T.;Joo, S.M.;Kim, S.H.;Chung, M.K.
  8. IEEE Trans. Syst. Cyber. v.23 no.3 ANFIS : Adaptive-Network-Based Fuzzy Inference System Jang, J.-S.R.
  9. IEEE Proceeding v.83 no.3 Neuro-Fuzzy Modeling and Control Jang, J.-S.R;Sun, C.-T.
  10. Fuzzy Logic Toolbox for Use with MATLAB Gulley, N.;Jang, J.-S.R.
  11. MATLAB Supplement to Fuzzy and Neural Approaches in Engineering Hines, J.W.
  12. IEEE Trans. Cont. Syst. Tech. v.2 no.4 Fuzzy Precompensated PID Controllers Kim, J.H.;Kim, K.C.;Chong, E.K.P.
  13. Fuzzy Sets and Systems v.92 Fuzzy-PID Hybrid Control : Automatic Rule Generation Using Genetic Algorithm Cho, H.J.;Cho, K.B.;Wang, B H.
  14. IEEE Trans. Industrial Electronics v.37 no.5 Incremental Fuzzy Expert PID Control Tzafestas, S.G.;Papanikpolopouios, N.P.
  15. Preprints of the IFAC/IFIC/IMACS Internet. Symp. on Artificial Intelligence in Real-Time Control Laboratory Evaluation of Fuzzy Controllers Babuska, R.;Horacek, P.
  16. Fuzzy Sets and Systems v.24 Fuzzy Self-Tuning of PID Controllers He, S. Z.;Tan, S.;Xu, F.L.
  17. Proc. IFAC Workshop on Adaptive Control of Chemical Processes Automatic Tuning of PID Controller Based on Dominant Pole Design Hagglund, T.;Astrom, K.J.
  18. IEE Proc.-D v.138 no.2 Refinements of the Ziegler-Nichols Tuning Formula Hang, C.C.;Astrom, K.J.;Ho, W.K.