Applications of Artificial Intelligence in Monitoring And Analysis of SCADA Data

Supervisory Control and Data Acquisition (SCADA) systems generate vast amounts of operational data essential for real-time monitoring and control of power grids and industrial processes. However, the complexity, volume, and heterogeneity of SCADA data present significant challenges in extracting actionable insights through traditional analytical methods. Artificial Intelligence (AI), particularly machine learning and deep learning techniques, offers promising solutions for enhancing SCADA data analysis by enabling advanced anomaly detection, predictive maintenance, fault diagnosis, and operational optimization. This paper provides a comprehensive overview of AI applications in SCADA data monitoring and analysis. Key AI methodologies such as neural networks, support vector machines, decision trees, and unsupervised learning are reviewed in the context of improving system reliability and efficiency. Additionally, practical challenges and future directions for integrating AI in SCADA environments are discussed, emphasizing the critical role of data quality, computational resources, and cybersecurity considerations.

Keywords:

SCADA , Artificial Intelligence , Machine Learning , Deep Learning , Anomaly Detection , Predictive Maintenance , Power Systems , Data Analysis , Neural Networks