# Deep Learning Algorithm for Automatic Classification of Power Quality Disturbances
## 2 Related Work  
PQDs refer to variations or deviations in the electrical characteristics of power systems that can impact the performance and reliability of electrical devices [6]. Common PQDs include the following:  
1. Voltage sag refers to a short-term dip in voltage below the normal operating level. It typically lasts for a few cycles and can be caused by faults, starting large motors, or sudden changes in load. Voltage sags can lead to equipment malfunctions, flickering lights, or temporary disruptions in device operation. 2. Voltage swells are short-term increases in voltage magnitude above the normal level. Like voltage sags, voltage swells can occur due to various factors such as faults, load switching, or capacitor bank energization. Voltage swells can cause damage to sensitive electronic equipment, premature aging of devices, or excessive current flow. Harmonics are additional sinusoidal waveforms that have frequencies that are integer multiples of the fundamental frequency (typically 50 Hz or 60 Hz). Harmonics are caused by non-linear loads such as power electronics, variable speed drives, or rectifiers. They can distort the voltage and current waveforms, leading to overheating of equipment, increased losses, and interference with communication systems. * Transients, also known as voltage spikes or surges, are brief and abrupt changes in voltage magnitude that can occur due to lightning strikes, switching operations, or equipment faults. Transients can have high amplitudes and short durations, potentially causing damage to sensitive devices, data loss, or disruption of operations. * Interruptions refer to complete power loss or a sudden stoppage of the supply voltage. They can be caused by faults in the power system, equipment failures, or intentional switching. Interruptions can lead to unexpected shutdowns, loss of data, or disruption of critical processes. * Flicker is the fluctuation of the perceived brightness of lights due to rapid changes in voltage. It can occur when large loads are switched on or off or during the operation of arc furnaces or welding equipment. Flicker can be visually disturbing and might affect the performance of sensitive equipment or human comfort. Other types of PQDs can include frequency variations, voltage unbalance, and voltage harmonics beyond the 50 th or 100 th order. Understanding and mitigating these PQDs is crucial for maintaining the reliability and efficiency of electrical systems and ensuring the proper functioning of connected devices. Figure 1 provides different PQD signals. Open-source dataset generators were developed as valuable tools for creating PQD datasets, as introduced by [PERSON] et al. [7]. These generators offer flexibility in controlling disturbance characteristics, including magnitude, duration, and frequency content. Moreover, the researchers recognized the effectiveness of deep learning classifiers, particularly CNNs, for PQD classification. [PERSON] and [PERSON] [11] proposed a novel closed-loop approach using a deep CNN model for PQD classification. Their model incorporates a unique unit construction that includes  
Figure 1: Different PQD Signals. (**a**) Normal signal. (**b**) Sag. (**c**) Swell. (**d**) Transient. (**e**) Flicker. (**f**) Harmonics. 1-D convolutional layers, pooling, and batch normalization, facilitating multi-scale feature extraction while reducing overfitting. ResNet50, a deep CNN architecture, has gained significant attention in image and signal analysis due to its ability to effectively learn deep features while mitigating the vanishing gradient problem through skip connections. Recent studies have leveraged ResNet50 for classifying power quality disturbances effectively. For instance, [PERSON] et al. [12] utilized ResNet50 to analyze power quality signals, demonstrating accuracy and robustness. Their findings emphasized the ability of ResNet50 to extract relevant features from complex time-series data.