"基于卡尔曼滤波的多元信号融合及实时校正研究"

Keywords: calorific value of coal; real-time correction; Kalman filter; information fusion The application of Kalman filter in multi-signal fusion is a vital research topic that aims to obtain real-time and accurate signals reflecting the actual calorific value of coal. In this study, a method of information fusion based on Kalman filter is proposed, which combines the prediction-correction process of the Kalman filter with the laboratory analysis method to correct the calorific value calculated based on the load-pressure dynamic model. This approach effectively reduces static errors and improves the accuracy of the fusion results by minimizing dynamic errors. The effectiveness of the proposed method is validated using actual operating data from different operating conditions of power plants. The fused signals accurately reflect the changing trends of the actual calorific value of coal in real time. Furthermore, this method successfully addresses the challenges of model errors and fluctuations in coal supply, resulting in lower static errors of less than 3%. This meets the requirements of the field for both dynamic response speed and static accuracy of the signals. Overall, the application of Kalman filter in multi-signal fusion offers a promising solution for improving the accuracy and real-time responsiveness of signals related to the calorific value of coal. By integrating Kalman filter techniques with information fusion methods, this research provides a valuable contribution to the field of energy efficiency and resource management in power generation processes.