Today, power generation from clean and renewable resources such as wind and solar is of great salience. Smart grid technology efficiently responds to the increasing demand for electric power. Intelligent monitoring, control, and maintenance of wind energy facilities are indispensable to increase the performance and efficiency of smart grids (SGs). Integration of state-of-the-art machine learning algorithms and vision sensor networks approaches pave the way toward enhancing the wind farms’ performance. The generating power in a wind turbine farm is the most critical parameter that should be measured accurately. Produced power is highly related to weather patterns, and a new farm in a near area is also likely to have similar energy generation. Therefore, accurate and perpetual prediction models of the existing wind farms can be led to develop new stations with lower costs. The paper aims to estimate the angular velocity of turbine blades using vision sensors and signal processing. The high wind in the wind farm can cause the camera to vibrate in successive frames, and the noise in the input images can also strengthen the problem. Thanks to couples of solid computer vision algorithms, including FAST (Features from Accelerated Segment Test), SIFT (Scale-Invariant Feature Transform), SURF (Speeded Up Robust Features), BF (Brute-Force), FLANN (Fast Library for Approximate Nearest Neighbors), AE (Autoencoder), and SVM (support vector machines), this paper accurately localizes the Hub and track the presence of the Blade in consecutive frames of a video stream. The simulation results show that determining the hub location and the blade presence in sequential frames results in an accurate estimation of wind turbine angular velocity with 95.36% accuracy.

中文翻译：

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使用机器学习和计算机视觉远程估计智能电网中风力涡轮机的角速度

如今，利用风能和太阳能等清洁可再生资源进行发电变得非常重要。智能电网技术有效地响应了日益增长的电力需求。风能设施的智能监测、控制和维护对于提高智能电网（SG）的性能和效率必不可少。最先进的机器学习算法和视觉传感器网络方法的集成为提高风电场的性能铺平了道路。风力发电机组的发电功率是需要准确测量的最关键参数。发电量与天气模式高度相关，附近地区的新农场也可能产生类似的能源。因此，可以通过对现有风电场的准确且永久的预测模型来以较低的成本开发新的风电场。本文旨在利用视觉传感器和信号处理来估计涡轮叶片的角速度。风电场中的大风会导致相机在连续帧中振动，并且输入图像中的噪声也会加剧该问题。得益于多种可靠的计算机视觉算法，包括 FAST（加速分段测试的特征）、SIFT（尺度不变特征变换）、SURF（加速鲁棒特征）、BF（暴力）、FLANN（近似最近点的快速库） Neighbors）、AE（自动编码器）和 SVM（支持向量机），本文准确地定位集线器并跟踪视频流的连续帧中刀片的存在。仿真结果表明，确定顺序帧中的轮毂位置和叶片存在可以准确估计风力涡轮机角速度，准确度为 95.36%。