Research on Power System Fault Prediction Algorithm Based on Deep Learning

Power systems are crucial infrastructure that requires constant monitoring and quick fault finding to guarantee reliability and prevent major problems with power. This study explores innovative approaches to develop a fault prediction model based on deep learning techniques, aiming to improve power’s predictive cap and response times. The study employs the Adaptive Cheetah Optimized Deep Neural Decision Forests (ACO-DNDF) algorithm for analyzing large-scale fault prediction in power systems. The datasets include fault activity in the electrical power system, studied over a specific signal period. The acquired data are preprocessed using Z-score normalization to standardize the data, and the dataset’s useful functions are extracted using the Fast Fourier Transform (FFT) approach. The method’s purpose is to detect potential faults before they occur. The F1-score (98.4%), precision (93%), and recall (98.2%) are utilized to evaluate the effectiveness of the suggested approach. This study advances the technology of energy systems by providing a more reliable and effective method of boosting system reliability.