Advancing Smart Grid Stability Through Machine Learning and Principal Component Analysis

Innovative solutions are required to maintain the stability, efficiency, and dependability of modern smart grid systems due to their increasing complexity and demand. This paper explores the potential synergy between Principal Component Analysis (PCA) and machine learning algorithms to improve the stability of smart grids. Through the application of cutting-edge machine learning techniques like Random Forests, XGBoost, Gradient Boosting Machine (GBM), Adaboost, and Generative Adversarial Networks (GANs), we hope to precisely identify and reduce instability factors in the grid. Large and complicated data dimensions are reduced to enhance the overall PCA computational cost and performance of machine learning models. The outcomes of research are analyzed and measured using the performance metrics like Accuracy, Mean Absolute Error (MAE), Precision, Recall, and F1 Score. Using real-world data analysis, and extensive simulations, results show massive improvements in grid reliability and stability. The XGBoost models demonstrate effectiveness as showcased by their high Accuracy, low MAE, and favorable Precision, Recall, and F1 Scores. Our research demonstrates the effectiveness by combining PCA and machine learning. Coding language used for the research is python and the models are implemented in Google Colab for measuring the output parameters.