Coordinated Rotor-Side Control Strategy for Doubly-FedWind Turbine under Symmetrical and Asymmetrical Grid Faults

In order to solve the problems of rotor overvoltage,overcurrent and DC side voltage rise caused by grid voltage drops,a coordinated control strategy based on symmetrical and asymmetrical low voltage ride through of rotor side converter of the doubly-fed generator is proposed.When the power grid voltage drops symmetrically,the generator approximate equation under steady-state conditions is no longer applicable.Considering the dynamic process of stator current excitation,according to the change of stator flux and the depth of voltage drop,the system can dynamically provide reactive power support for parallel nodes and suppress the rise of DC side voltage and rotor over-current.When the grid voltage drops asymmetrically,the positive and negative sequence components are separated in the rotating coordinate system.The doubly fed generator model is established to suppress the rotor positive sequence current and negative sequence current respectively.At the same time,the output voltage limit of the converter is discussed,and the reference value is adjusted within the allowable output voltage range.In order to adapt to the occurrence of different types of power grid faults and complex operating conditions,a fast switching module of fault type detection and rotor control mode is designed to detect the type of power grid faults and voltage drop depth in real time and switch the rotor side control mode dynamically.Finally,the simulation model of the doubly fed wind turbine is constructed in Matlab/Simulink.The simulation results verify that the proposed control strategy can improve the low-voltage ride through performance of the system when dealing with the symmetrical and asymmetric voltage drop of the power grid and identify the power grid fault type and provide the correct control strategy.

DETECTION OF HUMAN PAPILLOMAVIRUS TYPES 16, 18 DNA RELATED SEQUENCES IN BRONCHOGENIC CARCINOMA BY POLYMERASE CHAIN REACTION

In studying the relationship between human papillomavirus (HPV) and bronchogenic carcinoma, 'high-risk' HPV 16, 18 DNA sequences were detected in samples from 50 lung cancer patients, 18 patients with benign pulmonary diseases and 4 fetal lung tissues by polymerase chain reaction (PCR) and dot-blot hybridization with biotin-labelled probes. The results showed that HPV 16, 18 DNA related sequences were found in 32% of lung cancer specimens, with 10 cases of HPV 16, 5 cases of HPV 18 and 1 case of both types. 48.15% (13 / 27) of squamous cell carcinomas were shown to be positive for HPV 16, 18 DNA. In addition, two adenocarcinomas and one small cell carcinoma were positive for HPV 16 DNA. No specimens from benign diseases tissues and fetal lung tissues showed positive results. These results suggest that primary bronchogenic carcinoma is related to HPV infection.

Discovering Typed Communities in Mobile Social Networks

Mobile social networks, which consist of mobile users who communicate with each other using cell phones are reflections of people＇s interactions in social lives. Discovering typed communities （e.g., family communities or corporate communities） in mobile social networks is a very promising problem. For example, it can help mobile operators to determine the target users for precision marketing. In this paper we propose discovering typed communities in mobile social networks by utilizing the labels of relationships between users. We use the user logs stored by mobile operators, including communication and user movement records, to collectively label all the relationships in a network, by employing an undirected probabilistic graphical model, i.e., conditional random fields. Then we use two methods to discover typed communities based on the results of relationship labeling： one is simply retaining or cutting relationships according to their labels, and the other is using sophisticated weighted community detection algorithms. The experimental results show that our proposed framework performs well in terms of the accuracy of typed community detection in mobile social networks.