Intelligent Detection Method of Power Grid Fault Big Data Based on Deep Learning
Xiaolei Tian, Yue Wang, Yunchang Liu · 2023
To ensure the stable operation of the distribution network, a new approach is proposed for the intelligent detection of power grid faults using deep learning and big data analysis. The method involves establishing a loss function and iteratively processing the results obtained from data classification. These results are then input into the system, which utilizes deep learning techniques to extract relevant information from the power grid fault big data. By defining the power grid fault state and analyzing the transmission path of the traveling wave, the actual fault location can be determined by converting the average value. This enables intelligent detection of power grid faults using big data. Experimental results demonstrate that the proposed method achieves excellent performance, with a maximum detection accuracy of 98.5%. This approach is both accurate and practical.