Data Detection And Compression Methods Of Power System Based On Compression Sensing

With the enlargement of the power grids, development of informationization and construction of smart grid, power quality issues have increasingly captured considerable attention from both utility companies and their customers. The detection and identification research of power quality disturbances could provide a basis for executing effective control strategies to solve electric pollution and improve power quality. The research and application of power quality data compression is of great importance for reducing the data storage burden and improving the transfer efficiency. Due to the limitation of Shannon sampling theorem, the existing signal acquisition and detection methods will lead to mass of sampled data and bring heavy burden during storage and transmission.In recent years, compressed sensing (CS) has attracted considerable attention. Compressed sensing using non-adaptive linear projection to maintain the original structure of the signal, far below the Nyquist frequency of sampling can perfectly reconstruct the original signal by the numerical optimization problem. Several key issues, such as harmonics detection, power quality disturbance classification and data compression based on compressed sensing theory are studied and analyzed in the dissertation.To solve complexity and redundancy of detected harmonic by the traditional signal processing mode, the compressed sensing matching pursuit harmonics detection method is presented. At first, original harmonics was sampled and compressed simultaneously based on compressed sampling theorem. Then harmonics components could be solved by a compressed sensing matching pursuit harmonics detection algorithm, which directly processes the sampling values obtained from compressed sensing signal, without reconstructing the signal itself. Compared with the traditional mode, the new method merged sampling and compression to one step and skipped the uncompress process and can directly detect the interested base wave and harmonic components from the small amount of signal sampling points from the original harmonic compressed signal.In order to resolve the disadvantage of feature extraction relying on object category and complex classification algorithm, a new approach combining random dimensionality reduction projection (RDRP) with sparse representation classification (SRC) is proposed to classify power quality disturbances. Random matrix dimensionality reduction projection feature extraction method is extremely efficient to generate and independent of the training dataset. Compared with support vector machine, the SRC algorithm doesn’t need combination of two-class classifiers for multiclass classification. Simulation and experiment results show that the proposed RDRP-based SRC method has a high classification accuracy rate under noisy circumstance.A power quality data compressive sampling and adaptive matching pursuit reconstruction algorithm based on compressed sampling theorem is presented to reduce power quality data storage/transfer burden and improve the real time property of power quality real-time monitoring system. At first, original power quality data was sampled and compressed simultaneously based on compressed sampling theorem. The sampling feature points obtained from random matrix projection method is extremely efficient to generate and independent of object characteristics. Then the proposed adaptive matching pursuit reconstruction algorithm could control the accuracy of reconstruction by both the adaptive process which chooses the candidate set automatically and the regularization process which gets the atoms in the final support set although the sparsity of the original signal is unknown. The proposed method breaks through the traditional framework of data compression by merging compression into sampling and could reconstruct the original power quality data from the small amount of signal sampling points from the compressed signal. It can not only reduce hardware requirements, but also increase the efficiency of compression.