Power system monitoring for electrical disturbances in wide network using machine learning

Due to infrastructure developments, wide disturbances have occurred in the power system. There is a need for intelligent monitoring systems across wide power networks for the stability and security of systems. A significant challenge in a comprehensive power monitoring system is identifying the noises in electrical measurements and oscillatory errors. In this research, the disturbances in the power system are monitored using principal component analysis with a Support vector machine and Extreme Learning Machine (ELM) for analyzing the monitored data. In this work, PCA has been used to reduce the curse of dimensionality of the original data. Then, SVM was used to select the relevant and essential features from the disturbance signals. These selected features are fed as input into the Extreme learning machine to classify the power quality events. This machine learning advantage is that it can analyze many wide-area variables in real time and reduce the masking effect of the oscillatory trends and noise on disturbances. Compared to the existing feature selection and classification of PQ disturbance data, the proposed model secured an improved accuracy of 99.16%, and the comparison results prove the model's effectiveness.