Operating Optimization of Steam Turbine Unit Based on Big Data Parallel Association Rule Mining

This paper studies the problem of wide working conditions operational performance optimization of steam turbine units in coal-fired thermal power plants. A method based on big data association rule mining is proposed to establish relationships between a set of key controllable operating parameters and heat consumption rate of the units and then to determine optimal target values of the operating parameters. To deal with sparse data discretized from continuous unit operating data, a new association rule mining algorithm is proposed firstly. It utilizes a binary matrix to store data and introduces a search technique which combines linked list with pointers to mine frequent itemsets. These greatly eliminate the impact of sparsity of data i.e., large differences among transactions and scattered item distribution, on performance of the mining algorithm. Furthermore, in order to process large-scale data efficiently, a parallel implementation of the algorithm on Apache Spark platform is given. Finally, taking a steam turbine in a 600MW subcritical thermal power unit in China as an example, an overall optimization procedure including association rule mining and target value acquiring is presented. The mining results show that the target values calculated by the proposed optimization method can reduce heat consumption rate of the unit and improve economic benefits of the power plant significantly. Note to Practitioners—This research is motivated by the urgent requirement for energy saving and pollutant emission reducing in todays coal-fired power plants, especially in old thermal power plants. To achieve this objective, an effective method is to determine target values (also known as optimal values or benchmark values) of some key controllable operating parameters to realize operating optimization of the thermal power units. Existing target value determination approaches usually adopt the design values or the thermodynamic calculation values, which cannot truly represent the changes of unit operating characteristics. This research proposes a new big data-driven steam turbine unit target value determination method using association rule mining to reduce heat consumption rate of the unit. We not only design a new association rule mining algorithm but also give a parallel implementation of it on advanced big data distributed parallel computing framework Apache Spark, making the proposed optimization method efficiently mine large scale operating data. The proposed operating optimization method is based on the actual operating data of the unit. It has high reliability and good general applicability for different types of coal-fired power units. The optimization experiment on a steam turbine unit in China shows that the obtained target values are valuable for guiding optimization operation of the unit under wide working conditions. In the future, we will consider explicit pollutant emission in the optimization index and address multi-index operating optimization of the steam turbine unit.