【摘要】 为提高汽轮机低负荷下的运行效率，需要对主蒸汽压力进行优化。根据机组实际运行数据，采用支持向量机(SVM)算法建立了热耗率预测模型，并利用改进的灰狼优化(IGWO)算法优化SVM模型超参数；在此基础上，利用IGWO算法在低负荷下的可行压力区间进行寻优，得到了优化后的汽轮机滑压曲线，并且进行了实例验证。结果表明：利用IGWO算法优化的热耗率预测模型能够对低负荷下的热耗率进行准确预测；优化后机组在低负荷下的热耗率均有所下降，在负荷为223.83 MW时，热耗率降低了505.96 kJ/(kW·h),降低幅度最大。研究结果表明所提的优化方案可以有效提高汽轮机低负荷下的热经济性。更多还原

【Abstract】 To improve the operating efficiency of steam turbine under low loads, it is necessary to optimize the main steam pressure. A heat rate prediction model was established by support vector machine(SVM) algorithm based on actual operating data of a unit. The improved grey wolf optimization(IGWO) algorithm was used to optimize the hyperparameters of the SVM model. The IGWO algorithm was used to optimize the feasible pressure range under low loads, and the optimized steam turbine sliding pressure curve was obtained and verified by a practical example. Results show that, the heat rate prediction model optimized using the IGWO algorithm can accurately predict the heat rate under low loads. After optimization, the heat rate of the unit is decreased under low loads, especially when the load is 223.83 MW, the heat rate is decreased by 505.96 kJ/(kW·h), presenting the largest reduction. The optimization scheme proposed can effectively improve the thermal economy of steam turbine under low loads.更多还原