电站锅炉燃烧系统案例推理自适应寻优方法及应用研究

以提升火电机组调峰调频灵活性,促进可再生能源消纳为目标,针对某火电机组运行过程中燃烧稳定性、经济性等问题展开研究。采用自适应遗传算法优化核函数参数和正规化参数,建立最小二乘支持向量机(LS-SVM)锅炉燃烧过程模型。在建立模型的基础上,采用自适应遗传算法离线建立优化案例库。进而从便于工程应用角度提出一种基于案例推理(CBR)寻优方法,结合主、客观因素利用遗传算法优化案例推理特征权重,提高了检索精度,并自适应地从庞大的案例库中检索出与目标案例相匹配的案例。应用CBR自适应寻优算法,在保证机组稳定燃烧的同时,兼顾锅炉燃烧效率和NO_x排放浓度,合理给出二、三次风门挡板开度指令及氧量定值,实现锅炉稳定经济燃烧。将系统整体运用到某350 MW燃煤发电机组,简化了优化计算的过程,寻优时间短,稳定性高,适合在线实时寻优。

Study on case based reasoning adaptive optimization method and its application in power plant boiler combustion system

In this paper, taking improving the flexibility of peak load regulation and frequency regulation of thermal power units and promoting the consumption of renewable energy sources as the target, the combustion stability and economy of a certain thermal power unit during operation is studied. The adaptive genetic algorithm is adopted to optimize the kernel function parameters and normalization parameters, and a least square support vector machine (LS SVM) boiler combustion process model is established. On the basis of the established LS SVM model, an off line optimized case base is established using the adaptive genetic algorithm. Then, from the perspective of facilitating engineering application, a case based reasoning (CBR) optimization method is proposed. In consideration of subjective and objective factors, the genetic algorithm is used to optimize the feature weight of CBR, which improves the retrieval accuracy and adaptively retrieves the case matching with the target case from the huge case base. The application of the CBR adaptive optimization algorithm ensures the stable combustion of the unit, and at the same time, considers the boiler combustion efficiency and the concentration of NOx emission. This algorithm reasonably gives the opening instructions of the secondary and tertiary valve baffles and the fixed value of oxygen, and realizes the economic combustion of the boiler. The system was applied to a certain 350MW coal fired generation unit, which simplifies the process of optimization calculation, shortens the optimization time and has high stability. The system is suitable for on line real time optimization.