## 1 Introduction  
Energy is one of the most basic requirements in human life and has become even more important in our advanced world. In recent years, in terms of technology, economics, and environmental issues, renewable energy sources have been considered more than ever. Endless energy resources such as wind and solar energies are clean and can reduce environmental impacts [1, 2]. Wind energy is one of the most important renewable energy sources and many countries are predicted to increase wind energy portion of their whole national energy supply to about twenty percent in the next decade [1, 2, 3]. Figure 1 shows wind farms development from 2017 to 2022 in the globe [2]. Compared to steam, hydro, and gas turbines used in traditional power plants, wind turbines (WTs) are usually operated in harsher environments and, therefore, have relatively higher failure rates. The faults in WTs can be classified into two categories: wear-out failures and temporary random faults. Wear-out failures are long-term and permanent events. Repairing or replacing a failed component needs additional costs and results in a loss of energy production. If a failed component is not identified and repaired or replaced in appropriate time, it may cause consequent failures of other components and even the entire WT system [4]. In [5], the authors reviewed non-destructive testing (NDT) for wind turbine inspections. It includes several techniques such as vision inspection [6], X-ray inspection, infrared thermography, and ultrasonic scanning that are used in different industries, to monitor the structures of materials, components or systems without causing damage to them. Real-time energy monitoring and its precise estimation can be very helpful for energy management and preventing unwanted failures in the power grid. In the emerging power networks, so-called smart grids (SGs) [6; 7; 8], advanced communication network infrastructures [8; 9; 10; 11; 12; 13; 14] along with two-way power flows provide appropriate conditions for precise services throughout the power generation chains. The energy exchange always requires accurate estimation of produced energy by distributed energy generators (DGs). Condition monitoring (CM) is widely deployed in the wind farms to ensure WT normal operation found on detecting failures and planning for operations and maintenance services [15]. It is a practical tool massively employed for the early event detection of faults/failures in the wind turbine farms in order to maximize productivity and minimize downtime of the system. In order to increase the lifetime of the WT farm and reduce the maintenance cost, well accurate predictions of system faults and failures are needed [6; 7]. In particular, in future grids, these predictions can be available based on advanced nondestructive testing (NDT) approaches such as intelligent Vision Inspection (VI). Integration of Sensor Networks, IoT, and industrial cameras can provide advanced structures to apply vision inspections (VI) for fault detection/diagnosis of wind turbine such as Mechanical Deformations, Surface Defects, Overheated Components in rotor blades, nacelles, slip rings, yaw drives, bearings, gearbox, generators, and transformers [6; 7]. Based on our best knowledge, there are not noticeable researches that are concentrated on the integration of these methods based on video mining. In this work, we present a powerful method for measuring the rotational speed of wind turbines that can be used along with other information to estimate the amount of energy produced by the wind farm [16]. Providing infrastructure requirements for our proposed system such as camera networks and communication devices, it is also possible to benefit more vision inspection tasks and NDTs, like those mentioned above, in just one system.This article is structured as follows:  
Figure 1: Cumulative wind energy market forecast by region 2018-2022 (GW) [2]. Related works are indicated in Section 2. In Section 3, our proposed model is presented in detail.