一种基于DE-ENN的烟气轮机运行状态潜在风险识别方法

A Potential Risk Status Pattern Recognition Method for the Operation of Flue Gas Turbine Based on DE-ENN

针对烟气轮机在运行中潜在风险难以快速准确识别的问题,提出了一种基于差分进化—可拓神经网络(DE-ENN)的风险识别方法.该方法根据可拓学的基本理论对烟气轮机的运行模型进行拓展分析,构建物元模型,确定特征向量和潜在风险等级;接着将差分进化思想引入到可拓神经网络中,以解决学习速率和加权系数难以确定的问题,进而提出了完整的DE-ENN算法,并用UCI标准数据集进行测试,验证了该算法的有效性.最后将该算法应用于烟机运行模型的潜在风险识别,实验结果表明该方法不仅结构简单、运行时间短、预测准确率高,而且还具有出色的泛化能力.

To accurately and quickly identify the potential risk status patterns of flue gas turbines,we propose a novel potential-risk-status-pattern recognition method based on the differential evolution-extension neural network（ DE-ENN）. Through a theoretical analysis of the operation of the flue gas turbine,we establish a matter-element model of the flue gas turbine to determine the feature vectors and potential risk levels. Furthermore,we apply the differential evolution theory to extension neural networks and propose a solution to automatically tune the learning rate and weighted coefficients. We then present a new complete DE-ENN algorithm. In addition,we test a number of UCI standard data sets to verify the effectiveness of the proposed method. Finally,we apply the DE-ENN to the potential-risk-status-pattern recognition of the flue gas turbine. The experimental results show that the proposed DE-ENN has the advantages of requiring less learning time,higher accuracy,and less memory consumption. Moreover,the results also show that the method has better generalization ability.