With the extensive development of wind power generation and the generalized application of intelligent monitoring technology, wind power monitoring data shows the big data characteristics of large volume, multi types and fast growth. In order to solve the two major problems with big data efficient storage and quick query, in this paper, the optimization method of big data storage is studied based on Hadoop platform. A Hash bucket algorithm considering wind power monitoring data correlation is proposed. The algorithm realizes the centralized storage of related data, so as to enhance the efficiency of data query and processing. On the basis of data storage optimization, the parallel association query for multi-source big data of wind power monitoring based on MapReduce is realized. Tests on a Hadoop platform show that, after optimization of hash bucket storage, the time of the multi-source data parallel association query is significantly shortened than traditional Hadoop method.