Design of Wind Turbine Parameter Monitoring and Early Warning Software System Based on Deep Learning Modeling. Issue 1 (1st January 2022)

• Record Type: Journal Article
• Authors: Wang, Jiechang; Chen, Zhannan; Guo, Jianfei; Dong, Yan; Yu, Jinglong; Zhou, Runan
• Is Part Of: Journal of physics. Volume 2179:Issue 1(2022)
• Publication Date: 2022-01-01
• Subjects: Condition monitoring -- Parameter early warning -- Deep learning -- Wind turbine.; Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2179/1/012041
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: This paper adopts deep learning self-encoding algorithm, using JAVA, C++ and python language mixed programming computing technology to realize wind turbine parameter monitoring and early warning and software system design and development. This paper describes the database design, process management mode, fault early warning model, alarm value processing and human-computer interaction interface of the system, explains the software operation process and workflow, as well as the early warning mechanism and algorithm of the system, and realizes the early fault early warning function of wind turbine operation parameters.