Transmission Line Fault Diagnosis System Integrating Fuzzy Theory and RCNN Algorithm

In response to the problem of insufficient accuracy and diagnostic efficiency of traditional transmission line fault diagnosis models, this study proposed an improvement of region-based convolutional neural networks based on fuzzy logic algorithm and constructed a transmission line fault diagnosis algorithm. And based on this, a transmission line fault diagnosis model was constructed. The effectiveness of the proposed fault diagnosis algorithm was evidenced, and the accuracy, precision, and F1 value of the algorithm were 98.7%, 87.2%, and 0.81, respectively, which was higher than other comparative algorithms. Besides, the study also validated the effectiveness of the transmission line fault diagnosis model integrating fuzzy theory, and found that the accuracy of the model was 0.94, the loss function value was 0.06, the precision was 96.2%, the mean absolute error was 1.64 ∗ 10⁻², and the root mean square error value was 1.78 ∗ 10⁻², which was better than other comparative models. In summary, the transmission line fault diagnosis model that integrates fuzzy theory and improved region-based convolutional neural network has better diagnostic accuracy and efficiency than traditional models. The diagnostic model proposed in the study can quickly and accurately diagnose faults in transmission lines, thereby prompting maintenance personnel to take timely measures to reduce the impact of faults on the power system, improve system stability and reliability.