Abstract
In this paper, an improved ant lion optimization (IALO) algorithm for parameter identification of hydraulic turbine governing system (HTGS) is proposed. In the proposed algorithm, the search space is explored by the ant lion optimization first, and then the domain is searched by the particle swarm optimization (PSO) in each iteration cycle. A chaotic mutation operation namely Logistics map is introduced for the elite to break out of the local optimum. In mutation operation, a serial-parallel combined method is developed to increase the diversity of mutant population. When the proposed IALO algorithm is applied in the parameter identification of HTGS, the comparative simulation results show that the proposed IALO algorithm has the highest accuracy among different optimization algorithms, and the proposed IALO algorithm has a good convergence characteristic and high stability.
Highlights
The accuracy of system parameters is the important foundation of engineering designs and applications
The hydraulic turbine governing system (HTGS) is simulated in MATLAB, and the proposed improved ant lion optimization (IALO) is applied to identify five key parameters of simulated system, which are Tw, Te, f, Ta, and eg
The structure and model of HTGS are respectively described in Appendix A Figures A1 and A2
Summary
The accuracy of system parameters is the important foundation of engineering designs and applications. Like other stochastic algorithms, the phenomenon of prematurity and local optimum may arise for ALO, especially in complex or large scale problems [13]. To eliminate these drawbacks, some new algorithms have been reported. Liu et al [18] proposed an improved artificial fish swarm (IAFS) algorithm that was incorporated with the ant colony optimization (ACO) and got good identification results for hydroelectric turbine-conduit. A serial-parallel combined method to gain mutant particles is proposed This can increase the mutation population diversity without additional mutation times. The simulation results show that IALO is an effective method with a high accuracy of parameter estimation. The model and structure of HTGS is shown in Appendix A
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