Wind turbines condition monitoring and fault warning have important practical value for wind farms to reduce maintenance costs and improve operation levels. Due to the increase in the number of wind farms and turbines, the amount data of wind turbines have increased dramatically. This problem has caused a need for efficiency and accuracy in monitoring the operation condition of the turbine. In this paper, the idea of deep learning is introduced into the wind turbine condition monitoring. After selecting the variables by the method of adaptive elastic network, the Convolutional Neural Network (CNN) and the Long and Short Term Memory network (LSTM) are combined to establish the logical relationship between observed variables. Based on training data and hardware facilities, the method is used to process the temperature data of gearbox bearing. The purpose of artificial intelligence monitoring and over-temperature fault warning of high-speed side of bearing is realized efficiently and conveniently. Example analysis experiments verify the high practicability and generalization of the proposed method.

The new network paradigm of Software Defined Networking (SDN) has been widely adopted. Due to its inherent advantages, SDN has been widely used in various network fields such as data centers, WAN, enterprise, Optical Networks and energy Internet. Among them, SDN-based energy Internet systems are receiving more and more attention. But at the same time, some problems and challenges are gradually becoming more prominent. The SDN-based energy Internet is a distributed architecture for renewable energy, so the traditional centralized electric energy trading model will no longer apply. The blockchain has been rapidly developed and applied in various domains by virtue of its decentralization, coordinated autonomy, and non-tamperability. We propose an SDN-based energy Internet distributed energy trading scheme supported by blockchain technology. The proposed scheme achieves a reasonable match of the transaction objects under the premise of protecting privacy. Finally, we conducted a comprehensive, systematic security and applicability analysis of the proposed solution, further confirming that the system meets our design goals.