Fault Diagnosis of Wind Turbine Blades Based on Chaotic System and Extension Neural Network

Abstract

We propose a chaos synchronization detection method combined with an extension neural network to diagnose the state of wind turbine blades. On the basis of a large-scale wind power generation system architecture, a 100 W small-scale wind power generation system simulation platform was first constructed and then a programmable logic controller (PLC) collected vibration sensor information. Through Ethernet and IEC 61850 communication protocols, the measured vibration signals were synchronously transmitted to a remote human-machine interface constructed by LabVIEW to facilitate remote real-time monitoring and analysis. We examined the identification of four different states of wind turbine blades: the normal state, blade rupture, blade screw fly-off, and abnormal blade inclination angle. On the basis of vibration signals in different states, a dynamic error scatter diagram was constructed by the chaos synchronization detection method, and chaos eye coordinates were extracted as eigenvalues for the identification of various state models. Finally, through the extension neural network, the four different states were identified. The measured results show that the proposed method can identify the states of wind turbine blades, and the identification accuracy rate of the proposed method was as high as 88.75%. Therefore, the proposed method effectively detects abnormal vibration signals of wind turbines and identifies different types of blade faults in real time.

A high‐accuracy and robust diagnostic tool for gearbox faults in wind turbines

“…Besides, it can be applied to supervised learning with a range of feature values and also to address the issues of continuous and discrete classification. It must be pointed out that an ENN can only recognise and then classify numerical data, while a clear advantage thereof is that it can be trained efficiently using an extended relational method [26][27][28].…”

Real-Time Monitoring of Wind Turbine Bearing Using Simple Neural Network on Raspberry Pi

“…There are a number of defect detection systems for wind turbine generators. These methods include spatiotemporal attention-based long short-term memory auto-encoder networks [2], marker-tracking for immediate rotational speed measurement [3], chaotic system and extension neural network fault diagnostics [4], time-varying models with augmented observers [5], deep learning approaches for sensor data prediction and fault diagnosis [6], sensor selection algorithms for real-time fault detection [7], enhanced variational mode algorithm fault diagnosis [8], image texture analysis for fault detection and classification [9], and cost-sensitive algorithms for online fault detection [10].…”