融合多特征参数的电力工程数据应用智能算法设计研究

Research on intelligent algorithm design of power engineering data application based on multi characteristic parameters fusion

为了提升电力工程数据分析的效率及准确度,文中开展了融合多特征参数的电力工程数据应用智能算法设计研究。在考虑多种电力工程特征参数的基础上,建立了基于线性判别分析与遗传算法优化极限学习机的电力工程数据分析模型。其通过对原始参数进行线性判别分析进而得到主要参数,不仅消除了原始参数的相关性,还降低了参数的维度。同时采用遗传算法优化了极限学习机的输入权值与阈值,再对电力工程数据的分析模型加以训练。仿真分析结果表明,所提模型在电力工程数据分析上的计算速度快且准确度较高,可以辅助电力工程施工进行决策、及时落实管控措施,避免事后评估所带来的损失。

In order to improve the efficiency and accuracy of power engineering data analysis,the design of intelligent algorithm for power engineering data fusion with multi characteristic parameters is carried out in this paper.Considering a variety of power engineering characteristic parameters,a power engineering data analysis model based on linear discriminant analysis and genetic algorithm optimization limit learning machine is established.The model obtains the comprehensive parameters by linear discriminant analysis of the original parameters,which not only eliminates the relativity of the original parameters,but also reduces the dimension of the original parameters.The input weight and threshold of limit learning machine are optimized by genetic algorithm to train the power engineering data analysis model.The simulation results show that the proposed model has faster calculation speed and higher accuracy in power engineering data analysis,can assist power engineering construction decision-making,timely implement control measures and avoid losses caused by post evaluation.