准确有效的状态监测是提高风电机组可靠性和安全性的关键。近年来,基于深度神经网络(deepneural network,DNN)的智能化异常检测方法越来越受到人们的重视。针对实际工业中难以获得准确的有标签数据的问题,提出了一种基于无监督学习的深度小世界神经网络(deep small-world neural network,DSWNN)来检测风电机组的早期故障。在深度置信网络(deepbeliefnetwork,DBN)构建过程中,首先采用多个受限玻尔兹曼机(restricted Boltzmann machines,RBM)堆叠常规自动编码网络,并利用风机的无标签数据采集与监视控制(supervisory control and data acquisition,SCADA)数据进行预训练。然后,利用随机加边法将训练后的网络进行小世界特性转换,再利用最少的有标签数据对网络参数进行微调训练。此外,为了应付风速扰动并减少虚警,又提出了一种基于极值理论的自适应阈值作为异常判断准则。最后,通过2个风机异常检测的应用实例,并与DBN和DNN算法进行了对比,验证了该方法具有良好的有效性和准确性。
A small-world neural network has stronger generalization ability with high transfer efficiency than that of the regular neural networks.This paper presents two novel smallworld neural networks,the Watts-Strogatz small-world based on a BP neural network(WSBP)and a Newman-Watts smallworld neural network based on a BP neural network(NWBP),related to previous research of complex networks.The algorithms are developed separately by adopting WS and NW small-world networks as their topological structures,and their derivation and convergence criterion are progressively discussed.After that,the proposed models are subsequently tested by two typical nonlinear functions which confirm their significant improvement over the regular BP networks and other algorithms.Finally,a wind power prediction system is advanced to verify their generalization abilities,and show that the models are practically feasible and effective with improved accuracy and acceptable forecasting errors caused by wind fluctuation and randomness with a time scale up to 24 h.
