In a real wind farm,complex airflow conditions result in complexities of wind speed and direction,with possibly significant intermittency and fluctuations.This problem can be alleviated if the wind speed distribution over a wind farm is known in advance.In this paper,a new method is proposed for real-time wind field reconstruction for large areas, based on the idea of a “virtual time”, i.e., a time span needed for an object to travel across a certain distance.The distribution of wind speed and direction can be acquired prior to its occurrence in the wind farm with refined spatial resolutions.A procedure is also developed to stabilize the solution process,and this stabilization leads to an optimal allocation of the wind speed sensors;this allocation is necessary for the efficient use of a limited number of sensors.The reconstruction algorithm has been substantially studied,and a mathematical quantity was correlated to the reconstruction error.This correlation enables us to obtain good reconstruction results by using the Greedy algorithm we proposed in this study.Simulation and experimental results demonstrated the strong feasibility of successful reconstructions by our proposed algorithm.Moreover,the sensor optimization scheme not only reduces the error significantly but also improves the efficiency of sensor applications;this improvement should apply to a wide range of conditions.

As a fast and non-intrusive measurement and visualization technique, Electrical Capacitance Tomography (ECT) is rapidly expanding its applications in the research on multiphase flow, fluidization, drying, combustion, and so on. However, the marked unevenness of the sensitivity maps sometimes causes unexpected effects in imaging reconstruction, particularly in three-dimension cases. To exploit the positive potential of this phenomenon, the authors proposed an image fusion method using the data from two units of ECT sensors in this study. This method is used in image fusion on the reconstructed images for a planar sensor and a cylindrical sensor. In contrast to the conventional fusion models that use fixed weight factors for two sources of data, our model forges weight functions that are set preference the strength of the sensitivity maps. The new algorithm is implemented by first extracting the characteristic information out of the ECT images using the Latent Low-Rank Representation and then performing a fusion algorithm with linear weight functions in preference to the significance of the sensitivity maps. The simulation results show that the algorithm effectively retains the advantages of the two units of sensors and mutually compensates the weak points of theirs, and significantly improves the reconstruction quality. The fusion image quality by the new method can response the real situation better in different heights. The results imply that the data fusion might to a significant extend amend the weakness of ECT caused by the uneven sensitivity maps.

Wind power has become one of the most important clean energy sources today, and the complex environment and physical structures lead to high operation and maintenance costs. Achieving rapid and precise wind turbine fault diagnosis can potentially decrease wind power’s operational and maintenance expenses. The conventional fault detection methods of the wind turbine system usually focus on one specific component, with multiple limitations, high computational complexity and low prediction efficiency. In this paper, a kind of feature extraction method integrated heatmap with MMSVM-RFE is applied on XGBoost model to diagnose overall wind turbine faults. The heatmap-MMSVM-RFE model presented in this paper fuses the advantages of the physical interpretability of the heatmap model with the mathematical statistics of the SVM model. A new criterion Ψ(α) is defined to assess whether the multi-faults model is comprehensively diagnosed. In the case study, the heatmap-MMSVM-RFE-XGBoost model has achieved the highest classification accuracy rate of 91%, which can broadly categorize faults. Compared to Random Forest and LightGBM, the integrated fault diagnosis model we propose improves Ψ(α) by 0.4% and 0.3%.

Temperature information has a certain significance in thermal energy systems, especially in gas combustion systems. Generally, measurements and numerical calculations are used to acquire temperature information, but both of these approaches have their limitations. Constrained by cost and conditions, measurement methods are difficult to use to reconstruct the temperature field. Numerical methods are able to estimate the temperature field; however, the calculation process in numerical methods is very complex, so these methods cannot be used in real time. For the purpose of solving these problems, a two-dimensional temperature field reconstruction method based on the proper orthogonal decomposition (POD) algorithm is proposed in this study. In the proposed method, the temperature field reconstruction task is transformed into an optimization problem. Theoretical analysis and simulations show that the proposed method is feasible. Gas combustion experiments were also performed to validate this method. Results indicate that the proposed method can yield a reliable reconstruction solution and can be applied to real-time applications.