Wide Area Power System Network Protection using Wavelet-Machine Learning Approach

Power system networks are evolving quickly, which emphasizes how important it is to have reliable and effective protection measures. Using machine learning approaches, this work presents a unique wavelet-based protection system designed for large-scale power grids. The suggested solution seeks to improve the efficacy and dependability of wide-area power systems by fusing machine learning algorithms with wavelet transform techniques. By applying wavelet analysis, a statistical technique that separates signals into discrete frequency and time components, the system examines the power network's behavior at different wavelengths. This makes it possible to identify and analyze a variety of disturbances-such as defects or unusual conditions-with greater accuracy. The machine learning algorithm has been trained on historical power system data and can forecast possible network outages. Its main goal, independent of fault impedance, fault resistance, or fault inception angle, is the prompt detection and precise localization of these disturbances.