## 7 Conclusions  
This work investigates the effectiveness of various deep learning architectures for Power Quality disturbances detection and classification. To do this, it is imperative to study the mechanism of these algorithms to extract the unique features of each disturbance and obtaining an efficient and accurate classification. The training and validation of deep learning architectures depend on a large number of data to better generalize the classification results. A Matlab/Simulink model has been designed and implemented in order to generate these disturbances. To improve the classification performances of the strategies under evaluation and converge to a generalized result, the data in the simulated dataset was augmented. Using the resulting datasets the authors have proposed a comparison among the LSTM, the CNN and a joint architecture that uses both the LSTM and CNN. All of the architectures were trained and validated using the augmented datasets and then tested using experimental data.  
Figure 14: Voltage signal with an interruption measured on the test bench (top plot). From top to bottom, the classification performances of each architecture: LSTM, CNN, LSTM-CNN and the LSTM-CNN with adjusted hyperparameters.  
Figure 15: Voltage signal with an sag measured on the test bench (top plot). From top to bottom, the classification performances of each architecture: LSTM, CNN, LSTM-CNN and the LSTM-CNN with adjusted hyperparameters.  
Concerning the experimental validation of the algorithms, it has been possible to generate an experimental dataset of the interruption and of the sag disturbances. The two datasets were processed by exploiting the four previously mentioned architectures. The first signal contained a train of interruptions and the second signal a train of sags. All of the four architectures successfully classified the sag signal. There were some discrepancies between the architectures while classifying the signal containing interruptions. Again, the LSTM-CNN with adjusted hyperparameters proved to be superior in classifying the disturbances.  
These results show that it is possible to train deep learning architectures with simulated data and operate disturbance identification on experimental data. The transient disturbance appears to be hardly detectable for all of the architectures under evaluation, mainly due to the small duration of the disturbance. The architecture that best performed while classifying the transient disturbance was the LSTM-CNN with adjusted hyperparameters. Furthermore, concerning the classifications of other disturbances, the LSTM-CNN with adjusted hyperparameters was the most performing one, both considering the simulated and the experimental datasets.  
Data curation, C.I.G. and L.P.; Formal analysis, C.I.G.; Funding acquisition, F.G., L.P. and G.T.; Methodology, A.L., F.G. and G.T.; Software, C.I.G.; Supervision, A.L.; Validation, L.P. and G.T.; Writing--original draft, C.I.G. and M.C.P.; Writing--review and editing, M.C.P. All authors have read and agreed to the published version of the manuscript.  
E-CUBE(E\({}^{3}\)) Project, Energy Exchange and Efficiency, project funded by POR FESR 2014-2020 ASSE 1, AZIONE 1.1.5.  
The authors declare no conflict of interest.