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optimal imc-pid controller design for large-scale power systems via EDE algorithm-based model approximation method

TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2021年第1期43卷 59-77页

作者： Vasu, G. Sivakumar, M. Ramalingaraju, M. Raghu Engn Coll Dept EEE Dakamarri India Gudlavalleru Engn Coll Dept EEE Gudlavalleu Andhra Pradesh India JNTUK UCEK Dept EEE Kakinada Andhra Pradesh India

In this paper, the authors propose an optimal imc-pid controller design for the Load Frequency Control (LFC) of large-scale power system via model approximation method. The model approximation method uses the Enhanced Differential Evolution (EDE) algorithm to determine an optimal Reduced Order Model (ROM) for the considered large-scale power system by minimizing the performance measure called Integral Square Error (ISE) between their step responses. Later, the LFC design is carried out using an optimal ROM instead of processing with the large-scale power system model. Thus, this simplifies the design, reduces the computational efforts and also helps in determining the lower order controller. An optimal imc design methodology is proposed by minimizing ISE between the actual output and the reference input responses of the large-scale power system using EDE algorithm. Further, pid controller gains are acquired by least square model matching with the optimal imc transfer function. The proposed imc-pid controller design allows a satisfied reference input tracking performance, robustness in disturbance rejection and improves the dynamic stability of the power system. The proposed method is validated by applying it to a single area power system of third-order SISO model and also extended to a centralized two-area thermal-thermal non-reheated power system of a seventh-order MIMO model. The simulation results and the comparison of error performance indices show the efficacy of the proposed method over the significant methods available in the literature.

关键词： Load frequency control model approximation optimal imc-pid controller design Enhanced Differential Evolution algorithm

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