A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 5 Issue 3
May  2018

IEEE/CAA Journal of Automatica Sinica

• JCR Impact Factor: 6.171, Top 11% (SCI Q1)
CiteScore: 11.2, Top 5% (Q1)
Google Scholar h5-index: 51， TOP 8
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Article Contents
Dianwei Qian and Guoliang Fan, "Neural-Network-Based Terminal Sliding Mode Control for Frequency Stabilization of Renewable Power Systems," IEEE/CAA J. Autom. Sinica, vol. 5, no. 3, pp. 706-717, Mar. 2018. doi: 10.1109/JAS.2018.7511078
 Citation: Dianwei Qian and Guoliang Fan, "Neural-Network-Based Terminal Sliding Mode Control for Frequency Stabilization of Renewable Power Systems," IEEE/CAA J. Autom. Sinica, vol. 5, no. 3, pp. 706-717, Mar. 2018.

# Neural-Network-Based Terminal Sliding Mode Control for Frequency Stabilization of Renewable Power Systems

##### doi: 10.1109/JAS.2018.7511078
Funds:

National Natural Science Foundation of China 60904008

National Natural Science Foundation of China 61273336

the Fundamental Research Funds for the Central Universities 2018MS025

the National Basic Research Program of China (973 Program) B1320133020

• This paper addresses a terminal sliding mode control (T-SMC) method for load frequency control (LFC) in renewable power systems with generation rate constraints (GRC). A two-area interconnected power system with wind turbines is taken into account for simulation studies. The terminal sliding mode controllers are assigned in each area to achieve the LFC goal. The increasing complexity of the nonlinear power system aggravates the effects of system uncertainties. Radial basis function neural networks (RBF NNs) are designed to approximate the entire uncertainties. The terminal sliding mode controllers and the RBF NNs work in parallel to solve the LFC problem for the renewable power system. Some simulation results illustrate the feasibility and validity of the presented scheme.

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沈阳化工大学材料科学与工程学院 沈阳 110142

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