Research on Optimization of Sliding Pressure Curve of 300MW Unit Based on Data Mining

Under the background of new power system, the flexibility transformation of coal-fired power plants and the lowcarbon transformation of coal-fired power plants have brought about problems such as the decline of unit economy, the drastic change of load, and the difficulty in applying for unit characteristic test conditions. This paper proposes a method of unit sliding pressure curve optimization based on data mining. The specific steps are as follows: first, collect historical data and delete historical data processing. Then, the fuzzy clustering algorithm is used to preliminarily divide the data with the ambient temperature as the clustering center. Afterward, for each temperature interval data, the maximum boiler efficiency and the minimum coal consumption of the unit are taken as the optimization objectives to optimize and simplify. Finally, the sliding pressure curve model corresponding to each temperature interval is obtained. After calculation, the method has the characteristics of simple operation, good real-time and stable operation, and has certain reference significance.