Research And Implementation Of Wind Plant Data Compression And Processing

With the large-scale development and utilization of wind energy in our country,wind power develops rapidly,so the storage,transmission and management of the substantial quantities of wind plant data produced is becoming a major problem. Data compression technology is effective to solve the problem,and how to compress and process the data effecitively to reduce the storage space and transmission time has also been the new topic for the power researches to explore constantly.The paper collects and organizes the east and west wind data sets provided by the National Renewable Energy Laboratory(NREL),the two data sets are referred to as point data sets and gridded data sets because of their different locations.Firstly,the paper analyse the data sets’characteristics and compressibility based on the Shannon entropy and the probability distribution characteristics;and then pre-process the wind speed data and the wind power data of the two kinds of data using proper methods respectively.After that,transform the one-dimensional data to two-dimensional data to take full advantage of the correlation between the data.Before compressing the data,the paper optimizes the parameters(wavelet decomposition level and code block size) of the algorithms.Finally,compress the pre-processed data using different lossless and lossy compression methods based on the Matlab platform,and the results indicates that the proposed modified JPEG2000 compression algorithm can make better performance.When we compress the wind power data and the point wind speed data sets using the lossless compression algorithm proposed in this paper,we can get the roughly equivalent results compared with BZIP2.But,when we compress the wind speed data of the gridded data sets,the compression ratio is 14% higher than using BZIP2.For JPEG2000 lossy compression algorithm,the normed mean square error obtained by that is about 30% of the one-dimensional wavelet compression algorithm when we compress the wind speed data of point dataset.Besides that, we can get better results when compressing the gridded wind speed data sets.Algorithms the paper proposed adequately exploit wind speed-to-wind power relationships,and the temporal and spatial correlations in the data.The Shannon entropy of wind power and speed data is computed to gain insight on the uncertainty of wind power and speed and to benchmark performance of the compression algorithms.Compression of the data sets can mitigate the chanllenges about their management,storagy and transmission without affecting their using.