## 1 Introduction  
In the last three decades, the penetration of renewable energy into the power grid network has increased. This gives rise to power quality (PQ) issues. The same is true with increasing uptake of wind turbines, solar energy, and energy storage systems [1; 2]. These disturbances greatly affect the safe and economical operations of smart grid networks and decrease the lifetime and power conversion efficiency and reliability of grid-connected renewable energy systems. Therefore, PQ analysis, including disturbance recognition and classification, is a crucial task to provide adequate information for further remedial actions. For instance, faulty power equipment can be discovered so that predictive maintenance can be scheduled in time. If power quality disturbance signals are detected, the control strategy of power converters can also be optimized to avoid catastrophic failures [3; 4]. In essence, PQ refers to multifarious electromagnetic phenomena that cause deviations in voltage and current from their ideal waveforms, which are known as PQ disturbances (PQDs). The presence of PQDs can be divided into sags, swells, interruptions, oscillations, flickers, harmonics, notches, spikes, and their combinations, as per international standards such as EN 50160 [5], IEC 61000 [6], and IEEE-1159 [7]. The conventional methods of PQD recognition and classification contain two steps: feature extraction and classification. The feature extraction methods are mostly based on signal processing techniques, such as Fourier transform (FT) [8], short time Fourier transform (STFT) [9; 10], wavelet transform (WT) [11; 12; 13], S-transform (ST) [14; 15], empirical mode decomposition (EMD) [16; 17; 18], independent component analysis (ICA) [19], and variational mode decomposition (VMD) [20; 21; 22]. These methods are used to extract features from different types of PQDs. Then, the disturbance classification is conducted by a specific rule according to the features. In the literature, typical methods are decision tree (DT) [23; 24], artificial neural network (ANN) [25], probabilistic neural network (PNN) [26; 27], and support vector machine (SVM) [28; 29]. In [3], a knowledge-based waveform analysis method is used to inform utilities of the condition and health of individual components in the power system. It is focused on component issues influenced by power quality problems. A generic waveform abnormality detection method is adopted in [4]. In this paper, a Kullback-Leibler divergence (KLD) is developed as a threshold to detect the abnormality of the grid voltage and current. It can identify abnormal conditions precisely, but cannot give the type of the PQD. Although the existing methods are effective, they have some limitations: (1) They make use of synthetic PQD signals as per international standards, which do not represent the actual signals faithfully; (2) Different types and quantities of the features have a great influence on the classification results. For PQD classification, it is difficult to know what features should be extracted. In current research, the process of feature extraction relies heavily on various handcrafted features with different number and type, for example, relative mode energy ratio (RMER), mode's center frequency, number of zero-crossings, and instantaneous amplitude (IA) are used in [21]; mean, variance, and kurtosis are used in [22]. Taking inappropriate features decreases the accuracy of the classifier; (3) These methods all belong to the shallow model in nature. It has been validated that deep learning models are much more powerful than the shallow models due to their deep layer topology and big data support [30; 31]. In this paper, it is essential to develop new deep learning-based techniques for PQDs classification in order to extract features automatically, and simultaneously analyze all the nonlinear, nonstationary synthetic and operational signals. In deep learning technologies, convolutional neural networks (CNNs) have become an emerging and popular technique.