Research on Integrated Data Acquisition Method of wind Power Generation based on Deep Learning - Qu, Ming-Fei; Ma, Dong-Bao (2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI), 2020)
The traditional integrated collection method of power generation data uses analog separation components, which are subject to many interference factors of charging and discharging, resulting in the problems of redundancy and data loss in the integrated collection of data. In order to solve the above problems, this paper studies the integrated data acquisition method of wind power generation based on deep learning. Use current sensor, voltage sensor, wind speed and direction meter and other sensing devices to collect data in the process of wind and solar power generation, and use SQL Server 2008 to establish a database to store the collected wind and solar power data. On this basis, deep learning is used to fill the missing data in the database, and then DTS tool is used to complete the data integration after filling. Through comparison experiments with traditional methods, it can be seen that the integrated acquisition method based on deep learning can effectively reduce data redundancy and lack, and has better reliability.