Vision inspection and monitoring of wind turbine farms in emerging smart grids

Abstract

Todays, Smart Grids as the goal of next generation power grid system span wide and new aspects of power generation from distributed and bulk power generators to the end-user utilities. There are many advantages to develop these complex and multilayer system of systems such as increasing agility, reliability, efficiency, privacy, security for both Energy and ICT sections in smart grid architecture. In emerging smart grids, the communication infrastructures play main role in grid development and as a result multimedia applications are more practical for the future power systems. In this work, we introduce our method for monitoring and inspection of Wind Turbine (WT) farms in smart grids. In our proposed system, a thermal vision camera is embedded on a wireless sensor node for each WT to capture appropriate images and send video streams to the coordinator. It gets video frames to perform machine Vision Inspection (VI) and monitoring purposes. In our constructed model, turbine blade velocity estimation is targeted by detecting two important landmarks in the image that are named hub and blade. By tracking the blade in the consecutive frames and based on proposed scoring function, we can estimate the velocity of the turbine blade. Obtained results clearly indicate that accurate hub and blade positions extraction lead to error free estimation of turbine blade velocity.

Image Transmission over Cognitive Radio Networks for Smart Grid Applications

“…In order to increase the lifetime of WTs and reduce the maintenance cost, it is essential to have accurate prediction of system faults and failures using advanced Non Destructive Tests (NDTs) methods during in-service operation [31]. These systems usually benefits smart sensors with artificial intelligence and machine learning techniques to perform advance inspections [5,[32][33][34][35][36].…”

“…They deployed multimedia sensors for solar panels that can predict the amount of energy produced [42]. In [5], a new idea based on computer vision and a wireless camera network was introduced to estimate wind turbine velocity. This information can be used for energy predictions in DGs.…”

“…To estimate the average time overhead due to the retransmission, the user arrival model must be applied. Considering the Poisson arrival model with the arrival rate λ p for primary users and subsequently taking into account the time interval exponential distribution, the probability function P(X i ≥ x) is expressed in terms of the following Equations (4) and (5) [60,72,84,85]:…”

“…In these networks, a two-way path is considered for both Energy and Data flows [1][2][3][4]. These two flows are not only independent but also have mutual functional effects [5]. Smart grids utilize modern communication networks [6][7][8][9], cyber security measures [4], computational intelligence and efficient information systems throughout the energy production supply chain including distributed generation (DG) resources [10], transmission lines, substations, distribution lines, and end-users as an integrated complex system [11,12].…”

“…With the development of smart grids, many new applications are also developing [5]. One of the most important issues in the development of smart grids is communication networks which should transmit a wide range of information.…”