Abstract
The operation optimization for the cold end system is an efficient means to improve the economy of steam turbine units. To compensate for the inadequacy of the traditional mechanism analysis utilized in obtaining actual operating characteristics of the cold end system, the prediction model of the exhaust pressure was established on the basis of mechanism analysis combined with data from the operation process. An online adaptive updating strategy was introduced to guarantee the modeling accuracy. A discrete model of the cooling tower outlet water temperature (CTOWT) was constructed based on the operation data partitioned into different groups according to the pump operating mode change (POMC). Combining the above two models, the coupled model of the cold end system was therefore obtained. A model-based operation optimization system was then implemented for the cold end system in a coal-fired power plant. Experimental trials authenticate that the optimization suggestions provided by the system can effectively enhance the benefit of power generation.
Highlights
With large-scale renewable energy connected to the power grid, coal-fired power plants more frequently run in cycling load operation mode to compensate for the intermittency of renewable energy [1]
As a significant auxiliary system of steam turbine, the cold end system has direct impact on the economic operating of the units when the load varies in a wide range
Dataset in the previous section, least square support vector machine algorithm (LSSVM) algorithm was utilized to fit the relationship between the variation of cooling tower outlet water temperature (CTOWT) with the load, and the ambient parameters under different pump operating mode change (POMC), namely:
Summary
With large-scale renewable energy connected to the power grid, coal-fired power plants more frequently run in cycling load operation mode to compensate for the intermittency of renewable energy [1]. There has been quite a bit of attention attracted on the cold end system modeling methods and operation optimization strategies[4,5,6]. The implementation of operation optimization is founded on the conception of evaluating the impact of a specific pump combination change on the cold end system. A discrete model of CTOWT is constructed with the data partitioned by different pump operating mode changing way. Thereafter, the prediction model of exhaust pressure is further established on the basis of mechanism model of with least squares support vector machine (LSSVM) algorithm. Embedded with the coupled model, a cold end optimization system is developed to provide guidance for the actual operation
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