基于LSSVM和SA-BBO算法的循环水系统优化

火力发电厂循环水系统的优化对确定凝汽器最佳真空,以及提高火电厂整体效率、节能降耗,意义重大.以某电厂2台600 MW机组循环水系统为研究对象,建立了基于最小二乘支持向量机（LSSVM）的汽轮机功率预测模型.该模型解决了神经网络中的局部极小值问题,取得了很好的预测效果,泛化能力强.在此基础上,建立了以利润最大化为目标函数,综合考虑循环水泵功耗、汽轮机组功率微增以及煤、电能量价值市场差异的凝汽器真空优化模型.实际应用中,该模型计算时,首先以一定周期从实时数据库中读取电厂运行的最新工况信息,并进行数据预处理和稳态判别;再采用模拟退火生物地理学优化混合算法（SA-BBO）对凝汽器真空优化模型进行寻优,得出了不同工况下的凝汽器最佳真空、循环水泵的最优组合运行方式及优化收益.将寻优结果制成循环水系统的优化组合图,可以指导现场运行调节.

Optimization of circulating water system based on LSSVM and SA- BBO algorithms

The optimization of circulating water system in a thermal power plant holds a great significance for determining the optimum vacuum degree of a condenser and improving total efficiency of the plant to save energy. Therefore, a prediction model for steam turbine output power is established based on least squares support vector machine （LSSVM） by considering the circulating water system of two 600 MW steam turbine units in a particular thermal power plant. There is no local minimum in the model and excellent prediction results can be achieved for a variety of problems. Then, an optimization model for the vacuum degree in the condenser is developed based on maximization of the profit by ta- king circulating pump shaft power, turbine power increment, the price difference of coal and electricity on the market into account. Initially, the recorded operational parameters of the plant over a period of time are input into the model, a data pre-processing is conducted on the parameters and their stability is identified. Then the vacuum degree is optimized phy-based optimization algorithms （ SA - BBO） to by means of the simulated annealing and biogeogra- obtain the optimum vacuum degree, best combination of the operational parameters of the condenser and circulating pump under various operating conditions. The optimized results have been made into an optimized combinations chart to guide the operaion and regulation of steam turbines.