A Data Compression and Reconstruction Method for Distribution Network Field Operation Based on Compressed Sensing and Greedy Algorithm - TANG Lijun; YANG Yingchun; ZHAO Xu; HAN Tianxi; YAO Linpeng (Journal of Harbin University of Science and Technology)
With the rapid development of distribution network, intelligent monitoring of personal safety of distribution network operations has become an urgent demand for the production and operation of distribution network. In order to solve the data transmission problem of the intelligent early warning system of illegal behavior in the distribution network, a data compression and reconstruction method for the distribution network field operation based on compressed sensing and greedy algorithm is proposed. Firstly, the multi- source and heterogeneous characteristics of data in the field operation scenario of distribution network are analyzed. Secondly, the observation matrix is constructed on the edge side, and the image and video stream data are sampled and compressed by compressed sensing technology, and transmitted to the cloud. Thirdly, the K-SVD dictionary learning algorithm is used in the cloud to obtain a suitable sparse transformation base, and the greedy algorithm based on regular orthogonal matching tracking is used to realize data reconstruction, which has low computational complexity and fast iteration speed to ensure the timeliness of data transmission. Finally, the proposed method can effectively compress and reconstruct the illegal data of the live image and video stream of the distribution network.