Multi-Source Heterogeneous Data Fusion Analysis Platform for Thermal Power Plants

Abstract

With the acceleration of intelligent transformation of energy system, the monitoring of equipment operation status and optimization of production process in thermal power plants face the challenge of multi-source heterogeneous data integration. In view of the heterogeneous characteristics of physical sensor data, including temperature, vibration and pressure that generated by boilers, steam turbines and other key equipment and real-time working condition data of SCADA system, this paper proposes a multi-source heterogeneous data fusion and analysis platform for thermal power plants based on edge computing and deep learning. By constructing a multi-level fusion architecture, the platform adopts dynamic weight allocation strategy and 5D digital twin model to realize the collaborative analysis of physical sensor data, simulation calculation results and expert knowledge. The data fusion module combines Kalman filter, wavelet transform and Bayesian estimation method to solve the problem of data time series alignment and dimension difference. Simulation results show that the data fusion accuracy can be improved to more than 98%, and the calculation delay can be controlled within 500 ms. The data analysis module integrates Dymola simulation model and AERMOD pollutant diffusion model, supports the cascade analysis of boiler combustion efficiency prediction and flue gas emission monitoring, system response time is less than 2 seconds, and data consistency verification accuracy reaches 99.5%.