Application of ANN technique based on μ-synthesis to load frequency control of interconnected power system

• Published in: International journal of electrical power & energy systems Vol. 28; no. 7; pp. 503 - 511
• Main Authors: Shayeghi, H., Shayanfar, H.A.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.09.2006; Elsevier
• Subjects: ANN; Applied sciences; Disturbances. Regulation. Protection; Electric, optical and optoelectronic circuits; Electrical engineering. Electrical power engineering; Electrical power engineering; Electronics; Exact sciences and technology; LFC; Neural networks; Power networks and lines; Power system control; Robust control; μ-synthesis; Robust control; LFC; ANN; μ-synthesis; Power system control; Performance evaluation; Waveform; Integral proportional control; Interconnected power system; Non linear control; Load frequency control; Neural network; Controller; Experimental result; Numerical simulation; System synthesis; Control method; Artificial intelligence; Comparative study
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2006.02.012

A nonlinear artificial neural networks (ANN) controller based on μ-synthesis for solution the load frequency control (LFC) problem is proposed in this paper. Power systems such as other industrial plants subject to some uncertainties and disturbances due to multivariable operating conditions and load changes. In order to take large modeling errors and minimize the effects of area load disturbances, the idea of μ-synthesis theory is being used for training ANN based LFC controller. This newly developed design strategy combines advantage of the ANN and μ-synthesis control techniques to achieve the desired level of robust performance for all admissible uncertainties and leads to a flexible controller with relatively simple structure, which can be useful in the real world complex power system. A two-area power system is considered as a test system to demonstrate the effectiveness of the proposed method in comparison with the conventional PI and μ-based robust controllers under various operating conditions and load changes. The simulation results show that the proposed ANN based controller achieves good robust performance even in the presence of generation rate constraints (GRC) and is superior to the other controllers.