# Sensor Fusion and State Estimation of IoT Enabled Wind Energy Conversion System
###### Abstract  
The use of renewable energy has increased dramatically over the past couple of decades. Wind farms, consisting of wind turbines, play a vital role in the generation of renewable energy. For monitoring and maintenance purposes, a wind turbine has a variety of sensors to measure the state of the turbine. Sensor measurements are transmitted to a control center, which is located away from the wind farm, for monitoring and maintenance purposes. It is therefore desirable to ensure reliable wireless communication between the wind turbines and the control center while integrating the observations from different sensors. In this paper, we propose an IoT based communication framework for the purpose of reliable communication between wind turbines and control center. The communication framework is based on repeat-accumulate coded communication to enhance reliability. A fusion algorithm is proposed to exploit the observations from multiple sensors while taking into consideration the unpredictable nature of the wireless channel. The numerical results show that the proposed scheme can closely predict the state of a wind turbine. We also show that the proposed scheme significantly outperforms traditional estimation schemes. Wind energy; Sensor fusion; State estimation; Internet of Things (IoT); Renewable energy 12.15.  
estimation of wind turbine rotor state and wind-induced load state was presented in [6]. Considering nonlinear observers, a wind turbine dynamic state and parameter estimation technique was described in [8]. In [9], a semi-empirical method for wind turbine state estimation was proposed. In conjunction with the Newton-Raphson method, a Kalman filter based estimation technique was described in [10] to estimate the aerodynamic torque acting on the rotor of the wind turbine. To overcome drawbacks due to the decoupling effects, an extended state estimation method was proposed in [11], where the models for the different wind turbine technologies were incorporated. A type of non-linear state estimator called particle filter was utilized in [12] to estimate the wind turbine parameters: tower top displacement, tower stop velocity and the rotor speed. More recently, authors in [13; 14; 15; 16] studied dynamic state estimation of doubly fed induction generator (DFIG) based wind turbine and permanent magnet synchronous generator (PMSG) based wind turbine. However, most of the previous work did not take into account the impact of an unreliable wireless channel (between the wind turbines and the remote controller) on the state estimation of the wind turbine. Moreover, IoT based network can play a vital role in enabling a reliable and real-time state estimation and controlling of wind turbines. Although the potentials of IoT were discussed in a few studies [3; 4; 5; 17], not much is known regarding wireless communication framework of IoT based state estimation of wind turbine. On the other hand, the embedded sensors in a wind turbine measure different components of the turbine independently. Thus, an appropriate sensor fusion algorithm is required to combine the readings of different sensors for accurate state estimation. Note that previous research, including [18; 19; 20; 21; 22; 23; 24] considered sensor fusion techniques for generic observable plants. However, to the best of our knowledge, no work has considered sensor fusion techniques in the context of turbine-based wind energy systems. To address these shortcomings, this paper studies sensor fusion and state estimation of wind turbines over a wireless IoT network. We present a communication framework for wind turbines and remote IoT components. To achieve a reliable communication between the wind turbines and the remote observer, we present a repeat-accumulate coded transmission scheme over a wireless channel. To exploit different observations from the sensors, we propose a Kalman filter based sensor fusion technique while taking into consideration the error events caused by the wireless channel.