Recently, the distribution network has been integrated with an increasing number of renewable energy sources (RESs) to create hybrid power systems. Due to the interconnection of RESs, there is an increase in power quality disturbances (PQDs). The aim of this article was to present an innovative method for detecting and classifying PQDs that combines convolutional neural networks (CNNs) and long short-term memory (LSTM). The disturbance signals are fed into a combined CNN and LSTM model, which automatically recognizes and classifies the features associated with power quality disturbances. In comparison with other methods, the proposed method overcomes the limitations associated with conventional signal analysis and feature selection. Additionally, to validate the proposed method's robustness, data samples from a modified IEEE 13-node hybrid system are collected and tested using MATLAB/Simulink. The results are good and encouraging.

This paper presents a Wavelet packet transform with entropy features and support vector machine (SVM) based differential protection of power transformer by using internal fault and inrush current. The wavelet packet transform one of the powerful signal-processing tool and it is used to extract the information of differential current from third level using Db 9 mother wavelet. A two cycles of transformer fault current data is processed through wavelet packet transform to obtain wavelet coefficients and then features are extracted by using Shannon entropy principle. Subsequently, the extracted features are applied as inputs to SVM for distinguishing inrush current from internal fault. The application of this method is studied through detailed simulation of different faults on a power transformer using MATLAB/SIMULINK software. The results of the proposed new technique were found to be reliable, fast and accurate in identifying the fault condition.