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

Kun Ren,Jihong Qu

doi:10.1155/2014/571354

Abstract

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.

Highlights

Because of the operating environment and the structure of wind turbines are very complex
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 some typical faults were selected as examples to identify
Because of this nonlinear transmission capacity of neurons, neural network has been widely used in pattern recognition and the parameter fitting in recent years

Summary

Introduction

Because of the operating environment and the structure of wind turbines are very complex. Because of this nonlinear transmission capacity of neurons, neural network has been widely used in pattern recognition and the parameter fitting in recent years. Hopfield network [16–19] is a kind of interconnection networks, and it introduces the concept of energy function which is similar to cutting Lyapunov function; the topological structure of the neural network (represented by the connection matrix) corresponds to optimal questions (described by the objective function) and converts it into neural network evolution of dynamical systems. The use of artificial neural networks often points to a gradual steady to solve many problems. The evolution of Hopfield neural network is a computational associative memory or the process of solving optimization problems.

Discrete Hu Invariant Moments

Hopfield Neural Network and Simulated Annealing

Experimental Results

Conclusion