基于数据挖掘的火电机组能耗特征提取
[Abstract]:Under the severe situation that the supply of coal resources is increasingly tight and the coal price is rising, it is an important foundation for realizing the energy-saving operation of the units to strengthen the excavation of the energy consumption characteristics of coal-fired generating units. It is also an important direction to realize the energy saving of power system and a key problem to ensure the sustainable development of our country's economy. Therefore, it is of great significance to do a good job of energy-saving work of thermal power units. The main work of this paper is as follows: firstly, the important indexes which affect the unit energy consumption are excavated by the function of reducing dimension by factor analysis method. On the basis of the historical operation data of thermal power generating units, a group of parameters which reflect the operating state of the units and which are closely related to the coal consumption of generating units are selected. The common factor analysis model of unit energy consumption index is established, and the number of common factors extracted is verified, and the special factors which are often neglected in the application of factor analysis are considered. The Heywood phenomenon is indirectly judged by the estimated value of the variance of special factors, and the advantages and disadvantages of the fitted common factor model of energy consumption are further quantitatively judged. In order to more clearly describe the relationship between the original variables and a few common factors, the purpose of describing the information structure of the raw data by using a few common factors is realized by calculating the factor scores by the regression method, and the purpose of reducing the dimension is achieved. Secondly, in the process of target value mining of thermal power units, the weighted fuzzy C-means clustering method in data mining technology is used to solve the target values of important operating controllable parameters of unit energy consumption. Considering that the sample points of different unit energy consumption indexes have different effects on classification, the contribution of different attribute indexes to clustering will be different in the process of unit parameter mining. The paper puts forward that the different energy consumption indexes of the unit are given different weights at first, and then the target value is excavated, and the target value is more reasonable. Finally, in order to improve the objectivity and scientific nature of energy consumption assessment of thermal power units, the paper proposes to use AHP and entropy weight method to determine the weight value of each index in unit energy consumption evaluation, which overcomes the shortcoming of single weighting method. Make the determination of weight more reasonable. On this basis, the two-stage fuzzy comprehensive evaluation method is used to evaluate the energy consumption level of the unit, and the weighted two-stage fuzzy comprehensive evaluation results are obtained by comparing the subjective weight (AHP). The analysis of examples shows that the evaluation results obtained by this method are more perfect and accurate, and more in line with the actual situation of energy consumption level of thermal power units. Through the analysis of the real time working conditions of thermal power units, the energy consumption characteristics of the units are extracted, and the results are in accordance with the theoretical knowledge. The validity and practicability of the proposed model and problem analysis ideas are verified. It lays a foundation for energy-saving optimization and adjustment of thermal power units.
【学位授予单位】:长沙理工大学
【学位级别】:硕士
【学位授予年份】:2014
【分类号】:TM621;TP311.13
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