Determination of RMS based Continuous S-Transform with Deep Learning for Detecting and Classifying Voltage Sag and Swell | Chapter 8 | Technological Innovation in Engineering Research Vol. 5

Voltage sag and swell can result in serious problems with power quality, including as instability, a short lifespan, and data errors. Voltage swells are often indicative of a system malfunction. This study demonstrates voltage sag and swell detection and categorization. The S-Transform serves as the foundation for the Root Mean Square (RMS) method, which finds the point at which disturbances start. By combining the characteristics into a MATLAB-based Extreme Learning Machine (ELM) neural network approach, this study also illustrates the various sags and swells. In order to determine which method delivers the best classification, the ELM approach is contrasted with the Support Vector Machine (SVM) and Decision Tree approaches. As a percentage, the classification accuracy was described. It was demonstrated that detection and classification using RMS and ELM are feasible since the results amply demonstrate the benefits of RMS in detecting and ELM in classifying power quality concerns.

Author (s) Details:

Kamarulazhar Daud,
Centre for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, 13500 Permatang Pauh, Pulau Pinang, Malaysia.

Syazreena Sarohe,
Centre for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, 13500 Permatang Pauh, Pulau Pinang, Malaysia.

Wan Salha Saidon,
Centre for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, 13500 Permatang Pauh, Pulau Pinang, Malaysia.

Saodah Omar,
Centre for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, 13500 Permatang Pauh, Pulau Pinang, Malaysia.

Nurlida Ismail,
Centre for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, 13500 Permatang Pauh, Pulau Pinang, Malaysia.

Nazirah Mohamat Kasim,
Centre for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, 13500 Permatang Pauh, Pulau Pinang, Malaysia.

Please see the link here: https://stm.bookpi.org/TIER-V5/article/view/7467