Load frequency control by neural-network-based integral sliding mode for nonlinear power systems with wind turbines

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• 作者：Dianwei Qian^a ; ^{dianwei.qian@ncepu.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Shiwen Tong^bAuthor Vitae ; Hong Liu^aAuthor Vitae ; Xiangjie Liu^aAuthor Vitae
• 关键词：Load frequency control ; Integral sliding mode control ; Neural networks ; Power systems ; Wind turbine
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：15 January 2016
• 年：2016
• 卷：173
• 期：part_P3
• 页码：875-885
• 全文大小：2019 K

文摘

Load frequency control (LFC) plays an important role in maintaining constant frequency in order to ensure the reliability of power systems. With the large-scale development of sustainable but intermittent sources such as wind and solar, such intermittency challenges the LFC problem. Moreover, the generation rate constraint (GRC) of power systems also complexes the LFC problem. Concerning the constraint, this paper addresses an integral sliding mode control (I-SMC) method for power systems with wind turbines. Since the intermittency of wind farms and the linearization of GRC deteriorate the uncertainties of power systems, sliding-mode-based neural networks are designed to approximate the uncertainties. Weight update formulas of the neural networks are derived from the Lyapunov direct method. The neural-network-based integral sliding mode controller is employed to achieve the LFC problem. By this scheme, not only are the update formulas obtained, but also the control system possesses the asymptotic stability. The simulation results by an interconnected power system illustrate the feasibility and validity of the presented method.