Analysis through mathematical model of power quality disturbances to improvement

Singh, Rajendra; Tiwari, Neeraj

doi:10.47974/JIM-1813

Open Access Research Article

Analysis through mathematical model of power quality disturbances to improvement

^*Rajendra SinghCorresponding authorrajendra.singh@poornima.orgDepartment of Electrical EngineeringSchool of Engineering and TechnologyPoornima University JaipurJaipur, Rajasthan, 303905, IndiaView full profile → , Neeraj Tiwarineerajtiwari.1407@gmail.comDepartment of Electrical EngineeringSchool of Engineering and TechnologyPoornima University JaipurJaipur, Rajasthan, 303905, IndiaView full profile →

* Corresponding author · click or hover a name for details

pp. 211–221Vol. 27Issue 2March 2024DOI: 10.47974/JIM-1813XML

Published Online:: 19 Mar 2024
Article type:: Research Article
Language:: EN
Article no.:: JIM-1813
Pages:: 211–221

Abstract

Power quality studies have developed to analyses power quality data as the use of delicate electronic equipment has increased. The wavelet transformation method was developed to be more useful for analyzing different kinds of power quality events. In this research, we analyses the utilization of several wavelet types at various sizes and decomposition level of the system analyzing actual value of the power quality disturbances events in the transmission system. Signal created using MATLAB backdrop. Testing has been done on voltage sag, voltage swell, and transient events. This technique makes use of Wavelet transform and Artificial Neural Network (ANN) to identify and categories power quality disturbances in the power system. Comparing the suggested method to the standard approach, fewer characteristics are needed for identification. Artificial neural networks are taught to classify events using the feature that the wavelet extracts. The weight gained after neural network training is used to categories PQ concerns between the quantity demand and the price.

Keywords

Disturbances power quality Wavelet transforms techniques and Artificial neural network

Subject Classifications

00A05

References

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