Predictive Maintenance of Power Grid Infrastructure using Long Short-Term Memory Networks

This study examines the application of Long Short-Term Memory (LSTM) networks for the prescient upkeep of a control lattice framework. Leveraging verifiable information comprising sensor readings and support records, the LSTM demonstrate precisely expects potential disappointments or debasement within the control network, empowering proactive support techniques. Through broad experimentation, the LSTM demonstrates accomplishing momentous execution with an exactness of 90%, precision of 88%, review of 92%, F1-score of 90%, and an area beneath the ROC curve (AUC) of 0.95. Comparative examination against standard methods and related works within the writing illustrates the prevalence of the LSTM-based approach in prescient support of control network infrastructure. Optimization of hyperparameters and interpretability examination advance upgrades the model's execution and encourages decision-making in control network administration. This study sheds light on the potential of machine learning strategies to revolutionize control framework upkeep, guaranteeing a more solid, strong, and economic vitality supply.