It is possible to preserve power quality by classifying and identifying abnormalities. Prior studies focused on enhancing the PQD classification performance in one-dimensional (1D) CNNs. Recently, various image conversion methods have been established to facilitate CNN for PQD classification. PQD is a 1D signal that needs to be converted to a 2D image through data pre-processing since 2D images may include more PQD information than 1D signals. However, the PQD data used for the power quality classifier is synthetic PQD produced using mathematical models with parameter modifications in accordance with IEEE Std. 1159, which places limitations on prior research. This study uses data from the Amrita Honeywell Hackathon 2021 to examine how the response-based 2D deep CNN power quality classifier responds to actual field power quality disruptions. The results of the study show that a 2D deep CNN with regulated 2D grayscale pictures based on a process-regulated 2D image matrix can classify real data power quality disturbances with accuracy, precision, recall, and F1-score of 98.80%, 98.99%, and 98.60%, respectively. Additionally, 2D images can potentially contain more PQD data than 1D signals, enhancing identification performance on actual data.

The primary source of the various power-quality-disruption (PQD) concerns in smart grids is the large number of sensors, intelligent electronic devices (IEDs), remote terminal units, smart meters, measurement units, and computers that are linked by a large network. Because real-time data exchange via a network of various sensors demands a small file size without an adverse effect on the information quality, one measure of the power-quality monitoring in a smart grid is restricted by the vast volume of the data collection. In order to provide dependable and bandwidth-friendly data transfer, the data-processing techniques’ effectiveness was evaluated for precise power-quality monitoring in wireless sensor networks (WSNs) using grayscale PQD image data and employing pretrained PQD data with deep-learning techniques, such as ResNet50, MobileNet, and EfficientNetB0. The suggested layers, added between the pretrained base model and the classifier, modify the pretrained approaches. The result shows that advanced MobileNet is a fairly good-fitting model. This model outperforms the other pretraining methods, with 99.32% accuracy, the smallest file size, and the fastest computation time. The preprocessed data’s output is anticipated to allow for reliable and bandwidth-friendly data-packet transmission in WSNs.