## 5 Conclusions and Future Work  
Nowadays, the generation of electric power from natural resources such as wind and solar is of great salience due to their clean and renewable characteristics. Smart grid technology has been targeted to respond efficiently to the increasing electric power demand on these resources. In this study, we introduced our novel system to estimate the angular velocity of the WT based on computer vision, deep learning, and video mining. The Single Shot MultiBox Detector (SSD) as our proposed object detection algorithm should be run to locate the hub position. We deployed the special version of SSD, which is called SSD7. It is a smaller 7-layer version that had been trained from scratch in our implementations. The simulation results showed that it was capable enough to solve the object detection tasks. As a result, the accuracy of about 99.5% achieved during the run time/Prediction Time per Image (PTPI) was just 0.01500 s. Then, at the appropriate angle and distance from the hub center, our proposed binary classification algorithms such as DenseNet, SqueezeNet, ResNet50, and InceptionV3 should be deployed to distinguish between a blade and non-blade object. The results obtained indicate that DenseNet (Densely Connected Convolutional Networks) fine-tuned model,  
Figure 14: Final results of the wind turbine velocity estimation. (**a**) Comparison of different models with different accuracies. (**b**) Accurate estimation of the velocity based on DenseNet with \(accuracy=99\%\). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & \(V_{B}\) & \(\hat{V}_{B}\) & **Average Accuracy** \\ \hline [PERSON]’s correlation coefficient + Gabor Wavelet Transform [6] & 68.25 & 50 & 73.26\% \\ Proposed (Densnet + SSD7) & 68.25 & 68.22 & 99.05\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the average accuracy of the velocity estimation algorithm found on DB3. which was trained on our dataset with 10,000 images, can achieve the highest validation accuracy, about 99.12%, among all other classifiers. After executing the classification algorithm, the blade probability is recorded for the current frame. Then, this process is repeated for all frames in a specified time window, and it can be used to estimate the angular speed. The comparison between our proposed model and [6] indicates that our new algorithm can achieve about 99.05% accuracy. Besides, The run time (Prediction Time per Image (PTPI)) for SSD7 algorithm varied from 13 ms to 22 ms depending on different conditions. The prediction time for the highest accuracy (confidence threshold = 0.30, \(\text{IoU}=~{}0.50\), \(\text{ACC}=99.5\%\)) is about 15 ms. In our study, the prediction time of the DenseNet was also about 16 ms. Considering the video frame rate to be 30 fps, it means that inter-frame time spacing is about \(1/30=0.0334\) s (33.4 ms). As a result, it needs 15 ms+16 ms = 31 ms for the processing time, and it is well below the 33.4 ms as the inter-frame time spacing. This shows that our approach is definitely practical for real world applications. Demand response management systems need to have accurate predictions of energy power continuously. The proposed angular velocity approach along with other information such as pitch angle and yaw angle can be used further to estimate the wind farm energy production. Besides, historical figures of wind power production are not accessible when a new farm is going to generate power. Wind power is a function of weather variables, and it is likely that weather patterns of the new station are similar to some existing operational wind farms. It will thus be interesting to investigate how the forecast/prediction models of the existing wind farms can be adapted to generate a prediction model for new stations. This intelligent allocation based on our predicted information may lead to saving money in new projects, which would be the interesting research to do in future work. The yaw system of wind turbines is the component responsible for the orientation of the wind.