Abstract
<title>Abstract</title> <p>Artificial intelligence (AI) is becoming an essential engineering tool for managing uncertainty, asset reliability, operational complexity, renewable variability, and stability challenges in modern power systems. Recent studies show that AI is especially useful because many grid problems are nonlinear, data-intensive, and time-sensitive. Traditional analytical and rule-based methods remain important, but they can become difficult to scale as power systems incorporate distributed energy resources, inverter-based generation, and more dynamic load behavior. At the same time, successful AI adoption requires careful attention to interpretability, cybersecurity, data quality, operator trust, and integration with existing utility workflows. This paper reviews recent peer-reviewed and industry literature across six major application areas: load forecasting, fault detection and protection, predictive maintenance, grid optimization, renewable integration, and stability assessment. It also presents an engineering-oriented methodology for evaluating AI in practical power-system environments using operational, weather, asset, and event data; task-specific model families; and performance metrics that consider accuracy, latency, robustness, and constraint compliance. The main conclusion is that AI provides the greatest value when it functions as a physics-aware, human-supervised decision-support layer that strengthens established engineering judgment rather than replacing it. Key barriers to broader deployment include data readiness, legacy-system interoperability, model trust, cyber risk, and regulatory acceptance, all of which must be addressed alongside algorithm development.</p>