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This paper examines the recent advances made in the field of Deep Learning (DL) methods for the automated identification of Partial Discharges (PD). PD activity is an indication of the state and operational conditions of electrical equipment systems. There are several techniques for on-line PD measurements, but the typical classification and recognition method is made off-line and involves an expert manually extracting appropriate features from raw data and then using these to diagnose PD type and severity. Many methods have been developed over the years, so that the appropriate features expertly extracted are used as input for Machine Learning (ML) algorithms. More recently, with the developments in computation and data storage, DL methods have been used for automated features extraction and classification. Several contributions have demonstrated that Deep Neural Networks (DNN) have better accuracy than the typical ML methods providing more efficient automated identification techniques. However, improvements could be made regarding the general applicability of the method, the data acquisition, and the optimal DNN structure.

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Energy policies along with technological advancements in power electronics have allowed a high penetration of photovoltaic installations. To normalize grid connections, in particular for solar photovoltaic (PV) installations, an arrangement of requirements have been set as a grid code to prove that this penetration can provide support and fully control during both steadystate and transient conditions of the grid. In this paper, a control system for a PV installation connected to the grid is developed to accomplish the requirements raised in the 2010 draft version of the Spanish grid code P.O. 12.2. Control loops for steady-state conditions as well as transient conditions (voltage dips, swells, and frequency control) have been implemented. Simulations have been performed using PSCAD R /EMTDC T M program and supported by field tests of an existing PV installation in Spain using the voltage dip generator.