as well as malfunctioning of electric devices. This can severely damage industrial equipment, household appliances and commercial business plants. They also cause disturbance to other consumers and interference in nearby communication networks [1]. Besides that, the energy providers can sanction the injection of such disturbances on the grid. These disturbances concern frequency, amplitude, waveform and--in three-phase systems--symmetry of voltages and currents. Moreover, high energy demanding companies are becoming more sensitive to loss of profit margins due to power losses and plant sboutdowns caused by low PQ levels [2]. In order to adopt countermeasures and to define the origin of the phenomena, it is important to detect and classify these disturbances. This information indeed can be used to define the PQ level of a grid, to understand their behavior and to assess the responsibilities. The operation can be carried on by acquiring and processing the voltage and current signals of a power line. To do this, several techniques are currently exploited using machine learning algorithms [3]. Among the extensive inventories of deep learning algorithms, for the present work, the Long Short Term Memory (LSTM) and the Convolutional Neural Networks (CNN) are being used to detect and classify these disturbances [4; 5; 6]. Other algorithms are being used to address these problems like the Kalman Filter, Wavelet Transform and the Support Vector Machine (SVM). In [7], a Kalman Filter is used in an UPQC to extract the state components of the distorted supply voltage and load current. The algorithm can classify PQD internally enabling the conditioning of the PQ signals for power factor correction. The technique seems to work well with the detection of sag, swell and harmonic distortion, however it shows a certain lag between the disturbance starting condition and the detection [8]; furthermore, the algorithm is usually applied to a restricted number of disturbances. On the other hand, the wavelet transform is used as a tool for analyzing PQD as shown in [9]. The tool is very useful for the extraction of the signals features for learning algorithms like the SVM as shown in [10; 11; 12]. However, it does not perform disturbances detection by itself. The SVM showed interesting performances for the detection of a wide range of PQ disturbances and it is often used as a benchmark to assess the performances of other algorithms. The main disadvantage of the PQD detection techniques mentioned above is that, once the voltage and current waveforms are acquired, a preprocessing of the signal must be performed before feeding it to the algorithm. This usually consists of a signal features extraction. Deep learning algorithms solves this problem by implicitly applying a feature extraction for the classification of the signal. In other words, these algorithms could be fed with raw data and still make accurate classifications. This can help to speed up the identification and classification process especially in real time applications. For the training and the validation of deep learning algorithms, it is necessary an extensive dataset in order to avoid overfitting and obtain generalization. Unfortunately, it is not easy to obtain such datasets with experimental data. One reason is that performing on-field data sampling through measurement campaigns is time consuming, many of these disturbances indeed are sporadic, and it is not always possible to record an event with a desired amplitude and duration. For that reason, simulated voltage disturbances are used in order to create the dataset for training and validation. For further generalizing the dataset, data augmentation is used, since it has proven to be efficient in improving accuracy by reducing overfitting [13; 14]. This work explores different deep learning architectures which were trained and validated using simulated data and tested using experimental data.