Abstract
Hydraulic turbine governing system (HTGS) is essential equipment which regulates frequency and power of the power grids. In previous studies, optimal control of HTGS is always aiming at one single operation condition. The variation of operation conditions of HTGS is seldom considered. In this paper, multiobjective optimal function is proposed for HTGS under multiple operation conditions. In order to optimize the solution to the multiobjective problems, a novel multiobjective grey wolf optimizer algorithm with searching factor (sMOGWO) is also proposed with two improvements: adding searching step to search more no-domain solutions nearby the wolves and adjusting control parameters to keep exploration ability in later period. At first, the searching ability of the sMOGWO has been verified on several UF test problems by statistical analysis. And then, the sMOGWO is applied to optimize the solutions of the multiobjective problems of HTGS, while different algorithms are employed for comparison. The experimental results indicate that the sMOGWO is more effective algorithm and improves the control quality of the HTGS under multiple operation conditions.
Highlights
With the increase of people’s consciousness of environmental protection, more and more renewable energy has been applied to replace the traditional energy, such as wind power [1, 2] and solar power [3]
Some multiobjective optimization algorithms have been proposed and successfully applied in various applications in decades [19,20,21,22,23,24,25], such as nondominated sorting genetic algorithm II (NSGA-II) [19], multiobjective evolutionary algorithm based on decomposition (MOEA/D) [21], the strength Pareto evolutionary algorithm (PESA-II) [22], multiobjective particle swarm optimization (MOPSO) [23], and the multiobjective grey wolf optimizer (MOGWO) [25]
A novel MOGWO algorithm based on searching factor is proposed to optimize the multiobjective problem
Summary
With the increase of people’s consciousness of environmental protection, more and more renewable energy has been applied to replace the traditional energy, such as wind power [1, 2] and solar power [3]. Some multiobjective optimization algorithms have been proposed and successfully applied in various applications in decades [19,20,21,22,23,24,25], such as nondominated sorting genetic algorithm II (NSGA-II) [19], multiobjective evolutionary algorithm based on decomposition (MOEA/D) [21], the strength Pareto evolutionary algorithm (PESA-II) [22], multiobjective particle swarm optimization (MOPSO) [23], and the multiobjective grey wolf optimizer (MOGWO) [25].
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