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Ultra-short-term wind speed prediction is the key to ensure the implementation effect of wind turbine pitch angle feedforward control, and has an important impact on improving the environmental adaptability of wind turbines. In order to improve the prediction accuracy, an ultra-short-term wind speed prediction method based on an improved combined neural networks was proposed. In this method, BP neural network and long short-term memory (LSTM) neural network, which are suitable for time series prediction and have strong nonlinear learning ability, are selected for weighted combination to eliminate the large error that may exist in a single neural network. At the same time, to improve the combination effect, the differential evolution (DE) algorithm was used to optimize the combination weight. The method was applied to the ultra-short-term wind speed prediction of a wind farm. Compared with the results of single neural network prediction and equal weight combined neural networks prediction, the effectiveness of the proposed method in improving the prediction accuracy was verified.

In view of the characteristics of non-linearity， time delay and multiple disturbances of wind turbine pitch system， this paper proposes a wind turbine pitch compound control strategy. In the strategy， wind speed is introduced into the control system as a feedforward signal to dynamically compensate the pitch angle and improve the system response speed. In order to improve the robustness and the system's ability to suppress disturbances， a fuzzy PID （proportion-integral-derivative） controller is used. The differential evolution algorithm is adopted to optimize the initial parameters of PID， and the PID controller parameters are dynamically modified through fuzzy rules. The proposed strategy is applied to a 2 MW direct-drive wind turbine and compared with differential evolution-fuzzy PID control and fuzzy PID control. The results show that the proposed strategy can make the output power of the wind turbine more stable， which verifies the effectiveness of the proposed control strategy.

Nanotechnology is a new technology for observing and manipulating atoms and molecules in the range of 0.1–100 nm. The development of nanotechnology provides a new way for biomedicine. Based on the unique physical and chemical properties of nanomaterials, the novel biosensor based on bio-nanotechnology can be used for early diagnosis and therapy of tumors. Nowadays, the application of nanomedicine is mainly focused on the following points: tissue engineering and regenerative medicine, the accurate diagnosis and real-time monitoring of the disease, novel imaging and analysis techniques, and multifunctional drug delivery system and targeting therapy. Nanotechnology will provide effective means for accurate diagnosis and treatment of cancer. This article reviews the new progress of nanotechnology in cancer diagnosis and treatment.