A systematic experiment verification of Chaos Embedded Sliding Mode Extremum Seeking Control for maximum power point tracking and a method for detecting possible faults in small wind turbine systems in advance are proposed in this paper. The chaotic logistic map is used to replace the random function in the particle swarm optimization algorithm for faster searching the optimal control parameter U0. From the experimental results, it is verified that the Chaos Embedded Sliding Mode Extremum Seeking Control scheme has a better dynamic response than traditional Extremum Seeking Control scheme and Hill-Climbing Search scheme for maximum power point tracking. In the proposed scheme for fault detection, a chaotic synchronization method is used to transform the maximum power point tracking signal into a chaos synchronization error distribution diagram. It is then taken as the characteristic for fault diagnosis purposes. Finally, an extension theory pattern recognition technique is applied to diagnose the fault. Notably, the use of the chaotic dynamic errors as the fault diagnosis characteristic reduces the number of extracted features required, and therefore greatly reduces both the computation time and the hardware implementation cost. From the experimental results, it is shown that the fault diagnosis rate of the proposed method exceeds 98% not only in non-real-time but also in real-time of faults detection of the blades.
