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Neural Computing and Applications

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24-07-2020 | Original Article

Optimal design of load frequency active disturbance rejection control via double-chains quantum genetic algorithm

Authors: Zhaoyang Huang, Zengqiang Chen, Yuemin Zheng, Mingwei Sun, Qinglin Sun

Published in: Neural Computing and Applications | Issue 8/2021

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Abstract

This paper tackles the design of active disturbance rejection controllers (ADRC) for load frequency control (LFC) of multi-area interconnected power systems. For the first time, the double-chains quantum genetic algorithm plays a role in tuning optimization of the parameters for ADRC. Not only is the proposed approach applied to the two-area reheat thermal power system, but it is also elongated to two-interconnected multi-source areas comprising thermal, hydro and gas units, two-area nonlinear non-reheat thermal power system with governor dead band in addition to three-area nonlinear reheat thermal power system with generation rate constraints. Comparison with other modern heuristic optimization strategies recently published proves the effectiveness and superiority of this method. The simulation results show that this robust approach can greatly shorten the stabilization time of the power system and meet the requirements of LFC with minimum transient deviation as well as steady-state performance indicators, which is worthy of application and promotion.

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Title: Optimal design of load frequency active disturbance rejection control via double-chains quantum genetic algorithm
Authors: Zhaoyang Huang
Zengqiang Chen
Yuemin Zheng
Mingwei Sun
Qinglin Sun
Publication date: 24-07-2020
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 8/2021
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-020-05199-6

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