# Anomaly Detection of Wind Turbines Based on Deep Small-World Neural Network
## 5 Conclusions  
This paper presents a novel DSWNN model based on unsupervised learning for early anomaly detection and fault detection of wind turbines. The DSWNN model is a combination of deep auto-encoder network and small-world neural network, which are more accurate in simulating the dynamic behavior of wind turbines by working on a closer level of mimicking the working process of a natural brain. Analysis results of an actual case study confirm the following conclusions:  
1. The case study shows that the adaptive threshold can effectively track the prediction errors and reduce the false alarms. Therefore, the proposed adaptive threshold method based on the extreme value theory can be used in real-time monitoring of wind turbines to reduce the impact of wind speed fluctuations and external interference on the anomaly detection of the wind turbine. 2. Compared with the DBN and DNN algorithms, the proposed DSWNN model has better performance in error prediction, fault classification and learning ability, which benefits its unique small-world characteristics and unsupervised pre-training. Although it takes slightly longer on network training, it still has broad application prospects and research value. 3. In addition, the strategy combined with the DSWNN model and the adaptive threshold has been proven able to predict pitch system failures 3 h in advance, which can be used for subsequent anomaly detection and fault diagnosis of wind turbines. [PERSON] planned and supervised the whole project; [PERSON] developed the DSWNN algorithm, designed the criterion and performed the simulation and experiments; [PERSON] and [PERSON] contributed to discussing the results and writing the manuscript. [PERSON] and [PERSON] contributed to the application of adaptive threshold theory and English proofreading. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China, grant number 50776005, 51577008. **Conflicts of Interest:** The authors declare no conflict of interest. Figure 9: Training errors of the DSWNN, DBN, and DNN. * [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON]; [PERSON] Saliency detection based on deep cross CNN and non- interaction GrabCut. _Comput. Eng. Appl._**2017**, _53_[PERSON] 32-40. * [PERSON] et al. (2008) [PERSON]; [PERSON]; [PERSON] The recurrent temporal restricted Boltzmann machine. In Proceedings of the Twenty-Second Annual Conference on Neural Information Processing Systems, Vancouver, BC, Canada, 8-11 December 2008[PERSON]
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