# Sensor Fusion and State Estimation of IoT Enabled Wind Energy Conversion System
## 5 Performance Evaluations  
In this section, we present the performance of the proposed communication and fusion technique. We also compare the numerical results with a traditional fusion technique [18]. We built a simulation environment in Matlab to evaluate the tracking performance of the wind turbine's state. The parameters of the induction generator are shown in Table 2. We set the following values for the input parameters: the stator voltage in the \(d\) axis is \(v_{ds}=0.04\) Volt, the stator voltage in the \(q\) axis is \(v_{qs}=0.99\) Volt, the rotor voltage in the \(d\) axis is \(v_{dr}=0.02\) Volt, and the rotor voltage in the \(q\) axis is \(v_{qr}=0.206\) Volt. Note that the above parameters are adopted from the experimental setup reported in [28]. The process' noise co-variance is set to \(\mathbf{Q}=0.95\mathbf{I}_{\lambda}\) with constant matrix \(\Gamma=\mathbf{I}_{\lambda}\). For the following results, simulations were carried out for 150 steps with a step size of \(\Delta t=0.0001\) s.  
We present the estimation performance of the proposed scheme in Figures 5-8. Four sensors are used to measure the four current components of the induction generator with the following measurement matrices \(\mathbf{C}_{1}=[1~{}0~{}0~{}0]\), \(\mathbf{C}_{2}=[0~{}1~{}0~{}0]\), \(\mathbf{C}_{3}=[0~{}0~{}1~{}0]\), \(\mathbf{C}_{4}=[0~{}0~{}0~{}1]\). The measurement noise co-variances of the four sensors are set as 1.6, 1.2, 1.4, and 1.6, respectively. We consider a 32-bits uniform quantizer to map each component of the measured state. We use a rate \(\frac{1}{2}\)-\((q,a)=(4,4)\) repeat-accumulate code over each mapped 32 bit frame and hence each component is represented by a codeword of 64 bits. This codeword is then modulated with BPSK and sent over a wireless channel with noise standard deviation 0.7. From the results, we observe that the proposed fusion technique can track the state of the wind turbine very closely. As a benchmark, we also present results obtained from a traditional fusion algorithm [18]. We show that our proposed scheme can significantly outperform \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Base frequency & 10 Hz \\ Stator frequency & 15 Hz \\ Rotor frequency & 15 Hz \\ Resistance of stator & 0.004 \(\Omega\) \\ Resistance of rotor & 0.005 \(\Omega\) \\ Reactance of stator & 0.09 \(\Omega\) \\ Reactance of rotor & 0.08 \(\Omega\) \\ Magnetizing reactance & 3.95 \(\Omega\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Induction Generator Parameters  
Figure 4: Block diagram of proposed sensor fusion technique. the traditional estimation approach. In case of an error event due to the wireless transmission, it is expected that the measured state received by the remote receiver will be unreliable. While the traditional fusion algorithm treats all the measured states with the same importance, the proposed algorithm ignores the measured state and gives priority to the observation from the system dynamics. We notice that the traditional fusion algorithm fails to track the state (blue spikes in the figures), which are results of erroneous transmissions. We also investigate the impact of the wireless channel on the estimation performance of the proposed scheme. In Figure 9, we show the estimation performance while varying the channel noise standard deviation \(\sigma_{w}\). Note that in terms of noise standard deviation, the decoding threshold of the presented \((4,4)\)-RA code is \(0.885\). For \(\sigma_{w}=0.6\) and \(\sigma_{w}=0.7\), we observe similar estimation performance, which indicates that the wireless channel does not influence the estimation performance when the noise standard deviation is well below the threshold. However, when the noise standard deviation is close to the threshold (for \(\sigma_{w}=0.8\)), we observe inaccuracies in the estimation results. Figure 5: d-axis stator current estimation and error performance. Figure 6: q-axis stator current estimation and error performance.