A simple model for deep dynamic stall conditions

The present study is focused on modeling of dynamic stall behavior of a pitching airfoil. The deep stall regime is in particular considered. A model is proposed, which has a low implementation and computational complexity but yet is able to deal with different types of dynamic stall conditions, including those characterized by multiple vortex shedding at the airfoil leading edge. The proposed model is appraised against an extensive data set of experimental (α,C_L) curves for NACA0012. The results of an existing widely used model, having comparable complexity, are also shown for comparison. The proposed model is able to well reproduce not only the classic curves of deep dynamic stall but also the curves characterized by lift oscillations at high angles of attack due to the shedding of multiple vortices. Furthermore, the model appears to be robust to variations of its parameters from the optimal values and of the airfoil geometry. Finally, the model is successfully implemented in a commercial CFD software and applied to the simulation of a vertical axis wind turbine within the actuator cylinder approach. The accuracy of the prediction of the turbine power coefficient in the whole rotation cycle is very good for the optimal working condition of the turbine, for which the model parameters were calibrated. Fairly good accuracy is also obtained in significantly different working conditions without any further calibration.     

Design structure matrix (DSM) method application to issue of modeling and analyzing the fault tree of a wind energy asset

The perpetual energy production of a wind farm could be accomplished (under proper weather conditions) if no failures occurred. But even the best possible design, manufacturing, and maintenance of a system cannot eliminate the failure possibility. In order to understand and minimize the system failures, the most crucial components of the wind turbines, which are prone to failures, should be identified. Moreover, it is essential to determine and classify the criticality of the system failures according to the impact of these failure events on wind turbine safety. The present study is processing the failure data from a wind farm and uses the Fault Tree Analysis as a baseline for applying the Design Structure Matrix technique to reveal the failure and risk interactions between wind turbine subsystems. Based on the analysis performed and by introducing new importance measures, the “readiness to fail” of a subsystem in conjunction with the “failure riskiness” can determine the “failure criticality.” The value of the failure criticality can define the frame within which interventions could be done. The arising interventions could be applied either to the whole system or could be focused in specified pairs of wind turbine subsystems. In conclusion, the method analyzed in the present research can be effectively applied by the wind turbine manufacturers and the wind farm operators as an operation framework, which can lead to a limited (as possible) design‐out maintenance cost, failures' minimization, and safety maximization for the whole wind turbine system.     

Automated classification of simulated wind field patterns from multiphysics ensemble forecasts

In this study, we have proposed an automated classification approach to identify meaningful patterns in wind field data. Utilizing an extensive simulated wind database, we have demonstrated that the proposed approach can identify low‐level jets, near‐uniform profiles, and other patterns in a reliable manner. We have studied the dependence of these wind profile patterns on locations (eg, offshore vs onshore), seasons, and diurnal cycles. Furthermore, we have found that the probability distributions of some of the patterns depend on the underlying planetary boundary layer schemes in a significant way. The future potential of the proposed approach in wind resource assessment and, more generally, in mesoscale model parameterization improvement is touched upon in this paper.     

基于降噪时序深度学习网络的风电功率短期预测方法

利用风电场历史功率数据预测未来一段时间内的风功率,对保障电网安全稳定运行具有重要的意义。本文提出一种基于奇异谱分析SSA(singular spectrum analysis)和长短时记忆LSTM(long-short term memory net⁃work)网络的时序特征预测框架用于短期风功率的预测。首先通过SSA对历史风功率原始数据进行降噪处理,然后经过数据转换之后,以LSTM网络为基础进行预测模型的训练,最后通过某风电场提供的两个风机的历史功率数据进行验证。实验结果表明,奇异谱分析对风电场的历史数据具有良好的降噪性,SSA+LSTM模型在测试数据上取得了较好的预测性能,能够有效进行短期风功率的预测。     

对荷载规范中旋转壳顶的风载体型系数的研究

采用CFD数值模拟方法,研究了旋转壳顶在不同矢跨比、长宽比等参数影响下的风压分布特性,并得到风载体型系数,对现行风荷载规范作了一定的补充。     

光纤光栅感温火灾探测报警系统在原油罐区的应用

通过对现有罐区火灾自动报警技术的认真考察、对比,在石西集中处理站原油罐区安装了光纤光栅感温火灾探测报警系统.该报警系统的传感信号是光的波长变化,传感器及其探测系统是靠光缆连接而进入被测现场,它具有高安全性、高可靠性、高绝缘性、高抗电磁干扰性、防潮耐蚀、寿命长等其他传统火灾探测报警系统无法比拟的优越性能.阐述了其系统构成和运行效果.该系统在石西集中处理站运用效果良好.     

鄱阳湖区单退圩区水土资源利用的调研

通过对鄱阳湖区单退圩垸的水土资源利用、避洪农业开展的现状及存在的主要问题进行实地调研,提出了单退圩区农业产业结构调整的建议.     

江阴长江公路大桥钢箱梁体受力分析

介绍了对大桥的风向、风力和交通流量的测试依据和内容，分析了风向、风力的测试结果以及梁体在风力及交通流量等作用下的受力情况，指出钢箱梁体主要受到的力是风力。     

基于MATLAB的风能自校正自调整PID模糊控制器仿真研究

为了提高风力发电系统综合系统性能,在传统风力发电技术的基础上,对变速风力发电机组的自校正自调整PID模糊控制进行了详细的分析和研究,从而利用模糊控制和PID控制相结合的技术,取得了很好的效果。从MATLAB仿真结果看出系统精度及效率很高而稳态误差较小,鲁棒性较好。     

用增量的累积量方法测量大气风场

为了在非平稳、非线性条件下能得到真实风场的良好估计,提出了一种用空间分布天线（SA）雷达信号的增量累积量测量大气风场参数的方法。该方法基于结构函数方法,利用高阶累计量在信息处理中的特性,用零延迟的增量累计量求解平均水平风速等具体参量，并在大气散射模型基础上对增量累积量方法进行了数值模拟,得出了同理论分析一致的结果.分析和计算表明,该方法完全适用于局地平稳条件，可以减小风场参量估计对天线间距的敏感度；阶数k≥3时,对高斯噪声有很强抑制;同时可以得到风场参量的高阶估计，这是普通的二阶方法（如全相关分析、全谱分析）不具备的。