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A Novel Adaptive Neural Network Constrained Control for Multi-Area Interconnected Power System with Hybrid Energy Storage

Xu, Dezhi, Liu, Jianxing, Yan, Xinggang, Yan, Wenxu (2017) A Novel Adaptive Neural Network Constrained Control for Multi-Area Interconnected Power System with Hybrid Energy Storage. IEEE Transactions on Industrial Electronics, 65 (8). pp. 6625-6634. ISSN 0278-0046. E-ISSN 1557-9948. (doi:10.1109/TIE.2017.2767544) (KAR id:64199)

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http://dx.doi.org/10.1109/TIE.2017.2767544

Abstract

This paper concentrates on the problem of control of a hybrid energy storage system (HESS) for an improved and optimized operation of load-frequency control (LFC) applications. The HESS consists of a supercapacitor served as the main power source, and a fuel cell served as the auxiliary power source. Firstly, a Hammerstein-type neural network (HNN) is proposed to identify the HESS system, which formulates the Hammerstein model with a nonlinear static gain in cascade with a linear dynamic block. It provides the model information for the controller to achieve the adaptive performance. Secondly, a feedforward neural network based on back-propagation training algorithm is designed to formulate the PID-type neural network (PIDNN), which is used for the adaptive control of HESS system. Meanwhile, a dynamic anti-windup signal is designed to solve the operational constraint of the HESS system. Then, an appropriate power reference signal for HESS can be generated. Thirdly, the stability and the convergence of the whole system are proved based on the Lyapunov stability theory. Finally, simulation experiments are followed through on a four-area interconnected power system to demonstrate the effectiveness of the proposed control scheme.

Item Type: Article
DOI/Identification number: 10.1109/TIE.2017.2767544
Uncontrolled keywords: Adaptive control, dynamic antiwindup, Hammerstein network identification, hybrid energy storage system (HESS), load-frequency control (LFC), proportional-integral-derivative (PID)-type neural network (PIDNN)
Subjects: T Technology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Xinggang Yan
Date Deposited: 01 Nov 2017 14:19 UTC
Last Modified: 16 Feb 2021 13:49 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/64199 (The current URI for this page, for reference purposes)
Yan, Xinggang: https://orcid.org/0000-0003-2217-8398
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