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

Dianwei Qian; Guoliang Fan

doi:10.1109/JAS.2018.7511078

Volume 5 Issue 3

May 2018

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

CiteScore: 23.5, Top 2% (Q1)
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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. doi: 10.1109/JAS.2018.7511078

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

doi: 10.1109/JAS.2018.7511078

Dianwei Qian^{1
,
,},
Guoliang Fan^2
,

1.
School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
2.
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

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

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Abstract

Abstract

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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References(32)

References

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

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