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.