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• A 2D Densenet does not need prior extraction of features for PQD signals by some mathematical method, which eliminates the 2-step based model and reduces computational burden. • Spatio-temporal environment characteristics are considered in a data driven based framework. A 2D Densenet solution allows for the reduction in the quantity of filters per convolutional layer of Densenet used to classify PQDs. • Due the densely connected layers, the 2D Densenet size is smaller leading it to a faster classification time. The fast and accurate diagnosis of power quality disturbances (PQD) aids in avoiding shutdowns and unnecessary procedures, concerning electric energy distribution systems. As such, a number of techniques have been tested and applied in order to reach this objective. Majority of the techniques applied are two-step based. On the first step, power quality disturbances features are extracted. Second step, considering features extracted, disturbance classification is implemented. Recently, relevant literature has presented data-driven signal processing-based approaches, as deep convolutional neural networks (DCNN), which can implement both processing steps while providing automated recognition of patterns and outliers in data. However, not considered by state-of-art, power quality disturbances are evolving in nature, while all possible regularities might not be represented in the dataset. In this work a 2 Dimension Densely Connected Convolutional Network (2D-DenseNet) framework is presented. Case study with synthetic disturbance events are analyzed. Easy-to-implement formulation, built on the 2D-DenseNet, without hard-to-design parameters, highlight potential aspects for real-life implementation. [ABSTRACT FROM AUTHOR]