基于MSF技术的汽轮发电机状态估计

ESTIMATING THE CONDITIONS OF THE TURBINE-GENERATOR BY USING A MULTISENSOR FUSION

该文提出一个有效的基于径向基函数神经网络的模型和状态数据融合的汽轮发电机智能估计方法。文中阐述了其网络结构、学习算法、特征提取及综合决策方法。该模型同时利用了故障样本及专家经验知识,并通过不断学习新的样本获取新的知识,模型将越来越完善。仿真结果表明,该网络模型和信息融合方法是可行和有效的。

An efficient model based on radial basis function neural network and intelligent estimating method for data fusion of the turbine-generator is presented. The network structure, learning algorithm, feature extraction and synthetic decision are described. The model makes use of fault samples and original expert experience and knowledge, and it will be perfected increasingly by means of continuous learning. Simulation results show that the proposed network model and information fusion method are very feasible and efficient.