The Application Research Of Association Rules In Large Data Mining And Optimal Operation Of Power Plant Boilers

The operation of power plants has produced massive historical data,which has the characteristic of big data and great resource value.Driven by the new energy system revolution,the data mining technologies are flourishing and used to analyze and extract the historical data to form the value of data energy,which can provide data support for the optimal operation of power plants.In the view of the optimized operation theme and for the purpose of realizing the utilization of power plant data resources,this paper analyzes the correlation of power plant data from the perspective of big data analysis and mining,excavates its relevance and applies it in optimizing operation and affirms the significance of data mining in power plant by comparing with traditional control methods.Combining the development of new energy system and the status of power plant optimal operation,and considering the characteristics of power plant data,this paper explores the feasibility of data mining in power plant and the purpose of mining firstly.Afterwards,it introduces the concept of data mining,explains the steps and functions of data mining,puts forward the integrating methods and the problems to be concerned in power plant data mining and explains the reasons for selecting the association rules algorithm.And then it introduces the theory of association rules in detail.Considering the limitations of the experience of collecting data in the past,feature selection is used for more data items,which can enrich data sources.Latterly it uses multidimensional quantitative rules to mine power plant data which is applied in optimal operation of power plant.In order to obtain more data information,use feature selection to filter the data,and then use the association rules algorithm.Taking the safety problem of heating surface of a power plant as an example,the strong correlation property with the thermal deviation of the heated surface is analyzed.The results obtained have a greater correlation with the results obtained through neural networks and optimization algorithms.The optimization of the baffle opening by using the association rules can effectively reduce the thermal deviation and improve the safety and economy of the power plant.Compared with the traditional data self-search optimization method,the optimization obtained by using the association rules from the data has the same obvious effect,which can produced a good thermal deviation optimization effect.It further affirms the resource value of power plant data and the practical significance of applying big data analysis technology to power plants.