The nonuniform irradiation in the standard photovoltaic（PV） cells causes their relatively high series resistance,which results in a considerably lowered efficiency of PV cells.Currently the concentrator of uniform irradiation designed for concentrator photovoltaic is rare in China and lack sufficient theoretical research.In this paper,a systematic research on the solar reflective concentrator is conducted.A novel structure for a solar reflective concentrator is designed with the application of a flat mirror matrix to concentrate the sunlight for concentrator photovoltaic（CPV） systems.Sunlight beams are focused through the reflection of the mirror array on the solar cell to generate electricity.The concentrator is capable of producing much more uniform sunlight with a certain concentration ratio.The design scheme includes laying out the flat mirrors,optimizing the optical pathway and the parameters of each mirror.The prototype of the CPV system was installed at Nanjing,China.In the configuration of the prototype,it is composed of 24 pieces parallelogram flat mirrors,which are arranged into a total reflective array of 5 rows and 5 columns.In comparison with the parabolic trough concentrator,the experimental measurements verify such design has high efficiency.The concentrator model of a flat mirror matrix and the proposed new design method will lay a solid foundation for designing the concentrator of uniform irradiation.
SU ZhongyuanZHANG YaomingJIA MinpingSUN LiguoXU FeiyunWANG Jun
A novel method based on the improved Laplacian eigenmap algorithm for fault pattern classification is proposed. Via modifying the Laplacian eigenmap algorithm to replace Euclidean distance with kernel-based geometric distance in the neighbor graph construction, the method can preserve the consistency of local neighbor information and effectively extract the low-dimensional manifold features embedded in the high-dimensional nonlinear data sets. A nonlinear dimensionality reduction algorithm based on the improved Laplacian eigenmap is to directly learn high-dimensional fault signals and extract the intrinsic manifold features from them. The method greatly preserves the global geometry structure information embedded in the signals, and obviously improves the classification performance of fault pattern recognition. The experimental results on both simulation and engineering indicate the feasibility and effectiveness of the new method.
