Analysis and Design of Wind Turbine Monitoring System Based on Edge Computing

Abstract

INTRODUCTION: A wind turbine data analysis method based on the combination of Hadoop and edge computing is proposed. OBJECTIVES: Solve the wind turbine health status monitoring system large data, time extension, energy consumption and other problems. METHODS: By analysing the technical requirements and business processes of the system, the overall framework of the system was designed and a deep reinforcement learning algorithm based on big data was proposed. RESULTS: It solves the problem of insufficient computing resources as well as energy consumption and latency problems occurring in the data analysis layer, solves the problems in WTG task offloading, and improves the computational offloading efficiency of the edge nodes to complete the collection, storage, and analysis of WTG data. CONCLUSION: The data analysis and experimental simulation platform is built through Python, and the results show that the application of Hadoop and the edge computing offloading strategy based on the DDPG algorithm to the system improves the system's quality of service and computational performance, and the method is applicable to the distributed storage and analysis of the device in the massive monitoring data.