Identification of Shaft Centerline Orbit for Wind Power Units Based on Hopfield Neural Network Improved by Simulated Annealing | Zendy

Kun Ren | Zendy; Jihong Qu | Zendy

Open Access

Identification of Shaft Centerline Orbit for Wind Power Units Based on Hopfield Neural Network Improved by Simulated Annealing

Author(s) -

Kun Ren,

Jihong Qu

Publication year - 2014

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2014/571354

Subject(s) - hopfield network , artificial neural network , affine transformation , computer science , simulated annealing , orbit (dynamics) , identification (biology) , wind power , control theory (sociology) , artificial intelligence , algorithm , mathematics , engineering , control (management) , aerospace engineering , geometry , botany , biology , electrical engineering

In the maintenance system of wind power units, shaft centerline orbit is an important feature to diagnosis the status of the unit. This paper presents the diagnosis of the orbit as follows: acquire characters of orbit by the affine invariant moments, take this as the characteristic parameters of neural networks to construct the identification model, utilize Simulated Annealing (SA) Algorithm to optimize the weights matrix of Hopfield neural network, and then some typical faults were selected as examples to identify. Experiment’s results show that SA-Hopfield identification model performed better than the previous methods

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