The hydrogen production technology by wind power is an effective mean to improve the utilization of wind energy and alleviate the problem of wind power curtailment. First, the basic principles and technical characteristics of the hydrogen production technology by wind power are briefly introduced. Then the history of the hydrogen production technology is reviewed, and on this basis, the hydrogen production system by wind power is elaborated in detail. In addition, the prospect of the application of the hydrogen production technology by wind power is analyzed and discussed. In the end, the key technology of the hydrogen production by wind power and the problems to be solved are comprehensively reviewed. The development of hydrogen production technology by wind power is analyzed from many aspects, which provides reference for future development of hydrogen production technology by wind power.

Abstract:Based on an analysis of the working principles of the hydraulic variable pitch system of a wind turbine, a novel Petri net model and reliability evaluation method are proposed. First, Petri net theory is adopted to build a model for each discrete state of the operation of the hydraulic pitch system of the wind turbine and at the same time a fault Petri net model is established. Then through qualitative analysis and quantitative calculations based on the fault Petri net, the system reliability indexes are obtained. During the qualitative analysis process, in order to more conveniently find the minimal cut sets of the fault Petri net, a Visual C++ 6.0-based algorithm is compiled and the minimal cut sets are tested correctly with another method. During the quantitative calculation process, the fault probability has been obtained from the equations according to the fault probability of libraries and transitions between different states. Not only does the proposed Petri net describe the structure, function and operation of the hydraulic pitch system with a graphic language, but the fault Petri net model can also clearly express the logical relations among faults. The novel Petri net model offers simple calculations and the prospect of broad applicability and the new reliability evaluation method provides an important reference for the performance evaluation of these systems.

In this paper, a sliding mode (SM)-based online fault compensation control scheme is investigated for modular reconfigurable robots (MRRs) with actuator failures via adaptive dynamic programming. It consists of a SM-based iterative controller, an adaptive robust term and an online fault compensator. For fault-free MRR systems, the SM surface-based Hamilton–Jacobi–Bellman equation is solved by online policy iteration algorithm. The adaptive robust term is added to guarantee the reachable condition of SM surface. For faulty MRR systems, the actuator failure is compensated online to avoid the fault detection and isolation mechanism. The closed-loop MRR system is guaranteed to be asymptotically stable under the developed fault compensation control scheme. Simulation results verify the effectiveness of the present fault compensation control approach.

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The electromagnetic piezoelectric hybrid-driven 3-degree-of-freedom motor is a new multi-degree-of-freedom motor. To further analyze the torque characteristics of the electromagnetic piezoelectric hybrid-drive 3-degree-of-freedom motor. First, the principle and basic structure of the hybrid-drive motor are introduced, and the displacement and pressure distribution of the stator-rotor contact surface are obtained by analytical method. Based on this, the torque model of the piezoelectric stator-drive motor is obtained. Then, the air-gap magnetic field model of the permanent magnet rotor is obtained by analytical method, and the electromagnetic stator-torque model is obtained. Finally, the torque model of the electromagnetic piezoelectric hybrid-drive 3-degree-of-freedom motor is established by vector synthesis. The effects of piezoelectric stator mounting position angle, stator-rotor contact materials, and preload on motor torque are analyzed by simulation. The advantages of electromagnetic piezoelectric hybrid drive are analyzed, and the rationality of the model is preliminarily verified. It lays the foundation for further optimization design and performance improvement of electromagnetic piezoelectric hybrid-drive 3-degree-of-freedom motor.

Preliminary diagnosis of clinical symptoms and gross lesions with subsequent histopathologic and PCR analyses revealed histomoniasis in 276 chicken flocks in Jiangsu Province, China, and surrounding areas from January 2012 to December 2015. Detailed statistical analysis was performed to explore the occurrence and epidemic characteristics of histomoniasis in chicken flocks. The results indicated that histomoniasis usually occurred in free-range flocks of local broilers and laying hens. Also, 2- to 3-mo-old chickens were most susceptible to infection, and adult chickens rarely developed infection. The morbidity rate of chickens was generally 10%-30%, with mortality rates of less than 10%. Moreover, histomoniasis is a seasonal disease, occurring most often from April to June, and the rate of coinfection with heterakids in the ceca of infected chicken was 50.8%. The symptoms of diseased chickens included mental fatigue, bowing of the head and wings, and yellowish green droppings, with bloody stool in very limited cases. Most of the pathologic changes were characteristic of the disease, but there were also some atypical lesions confirmed by laboratory techniques. In the current study, the histomoniasis epidemic was first investigated in Chinese chicken flocks, and the results provided a useful reference for further study of this disease.

Visual tracking has been an active area of research in computer vision. However, robust tracking is still a challenging task due to cluttered backgrounds, occlusions and pose variations in the real world. To improve the tracking robustness, this paper proposes a tracking method based on multi-cue adaptive fusion. In this method, multiple cues, such as color and shape, are fused to represent the target observation. When fusing multiple cues, fuzzy logic is adopted to dynamically adjust each cue weight in the observation according to its associated reliability in the past frame. In searching and tracking object, neural network algorithm is applied, which improves the searching efficiency. Experimental results show that the proposed method is robust to illumination changes, pose variations, partial occlusions, cluttered backgrounds and camera motion.

Classification is always the key point in the field of remote sensing. Fuzzy c-Means is a traditional clustering algorithm that has been widely used in fuzzy clustering. However, this algorithm usually has some weaknesses, such as the problems of falling into a local minimum, and it needs much time to accomplish the classification for a large number of data. In order to overcome these shortcomings and increase the classification accuracy, Gustafson-Kessel (GK) and Gath-Geva (GG) algorithms are proposed to improve the traditional FCM algorithm which adopts Euclidean distance norm in this paper. The experimental result shows that these two methods are able to detect clusters of varying shapes, sizes and densities which FCM cannot do. Moreover, they can improve the classification accuracy of remote sensing images.