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.