基于混合量子麻雀算法的过热汽温模型参数辨识

Parameter Identification of Superheated Steam Temperature Model Based on Hybrid Quantum Sparrow Algorithm

建立高精度的过热汽温模型便于控制器的设计和参数整定,从而提升汽温的控制品质。首先针对传统的麻雀优化算法在闭环数据驱动辨识中容易陷入局部最优,收敛速度慢的问题,提出了一种混合量子行为的麻雀优化算法(QSSA)。该算法在基本麻雀算法迭代一次后采用量子策略对劣势群体进行变异,加强空间搜索能力,提高辨识精度;对于较优势群体引入Lévy随机游走策略,增加种群多样性,克服量子策略迭代后期种群性减少,易陷入局部最优的问题。其次对比多种优化算法在基准函数测试中的优化结果,验证了所提算法的优越性。最后运用某600 MW超临界机组过热汽温的现场运行数据进行模型参数的QSSA辨识,表明了所提算法的有效性,为现场过热汽温控制系统的进一步优化提供了新的途径。

The establishment of a high-precision superheated steam temperature model is convenient for controller design and parameter tuning, so as to improve the control quality of steam temperature. Firstly, in order to solve the problem that the traditional sparrow optimization algorithm is easy to fall into local optimization and slow convergence in closed-loop data-driven identification, a sparrow optimization algorithm with mixed quantum behavior,(QSSA), is proposed. After one iteration of the basic sparrow algorithm, the algorithm uses the quantum strategy to mutate the inferior population to strengthen the spatial search ability and improve the identification accuracy;for the dominant population, Lévy random walk strategy is introduced to increase the population diversity and overcome the reduction of species in the later iteration of the quantum strategy, which is easy to fall into the problem of local optimization. Secondly, the superiority of the proposed algorithm is verified by comparing the optimization results of a variety of optimization algorithms in the benchmark function test. Finally, the QSSA identification of the model parameters is carried out by using the field operation data of the superheated steam temperature of a 600 MW supercritical unit, which shows the effectiveness of the proposed algorithm, and provides a new way for the further optimization of the field superheated steam temperature control system.