In the last decade, the use of new technologies in the reconstruction of body tissues has greatly developed. Utilising stem cell technology, nanotechnology and scaffolding design has created new opportunities in tissue regeneration. The use of accurate engineering design in the creation of scaffolds, including 3D printers, has been widely considered. Three-dimensional printers, especially high precision bio-printers, have opened up a new way in the design of 3D tissue engineering scaffolds. In this article, a review of the latest applications of this technology in this promising area has been addressed.
Component damage and failure in sensor or actuator will cause system faults, resulting in reduced or lost performance. This topic has become an interesting and challenging issue that if we know the effect of faults on variables, control schemes can be designed to reduce the effect of faults on the system. Therefore, the quantification of the effect of the faults is very valuable in improving the performance of wind turbines. In this paper, the effect of some important faults is investigated in the three parts: rotor, drive, and power on the output power and the measured variables of wind turbines in the partial and full load regions using the Monte Carlo method.Then, the severity of these faults on the output power is ranked in the two regions by importance factor (IF). Also, due to the fact that some faults cause structural stress due to severe changes in the angle of the blades or their asymmetry, so the stresses caused by faults are discussed. The results show that these faults severity on the output power and the measured variables of wind turbines is different in the two regions. Our conclusion provides an opportunity for wind power plant operators and investors to improve plant performance and scheduled repairs by knowing the effect of faults on wind turbine variables. Knowing effect of the faults or the severity of each of them can increase the operating time of the wind turbine and reduce costs.
The dynamics of the wind turbine behaviour and identifying the factors that change turbine performance are very complex and challenging. Quantifying the impact of these factors on improving the wind turbine performance is invaluable. Many attempts have been made to describe the behaviour of the wind turbine variables. Sensitivity analysis and Monte Carlo simulation are methods to identify important parameters affecting model
behavior. Here, with these methods, the authors investigate the effect of error in sensors on output power and measured variables of the wind turbine in partial load region and full load region. Then, using the importance factor, the effect of error in the different sensors on the output power of the wind turbine is ranked in the two operating regions, so that, in practice, the sensors that have the greatest impact on the output power are designed as redundant.
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