Artificial Intelligence Applications in Power Systems: A Comprehensive Review of Techniques, Implementations, and Future Directions

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@article{1722424,
      author = {Ibrahim Musa Ibrahim, Mohammed Mustapha, Mohammed Wakilbe},
      title = {Artificial Intelligence Applications in Power Systems: A Comprehensive Review of Techniques, Implementations, and Future Directions},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {2},
      pages = {2652-2667},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1722424.pdf},
      abstract = {The electrical power industry is undergoing a structural transformation driven by the increasing penetration of variable renewable energy, the proliferation of distributed energy resources, the electrification of transport, and the digitalisation of grid infrastructure through advanced metering and wide-area sensing. These trends have substantially increased the dimensionality, uncertainty, and temporal volatility of power system operation, exceeding the practical limits of classical model-based analytical tools. Artificial intelligence (AI), encompassing machine learning, deep learning, fuzzy inference, evolutionary computation, and reinforcement learning, has emerged as a complementary and, in several domains, a superior paradigm for addressing these challenges. This paper presents a comprehensive and critical review of AI applications across the principal functional domains of power systems: short- and long-term load and renewable generation forecasting, fault detection and protection, state estimation and security assessment, optimal dispatch and unit commitment, microgrid and demand-side energy management, electric vehicle integration, and cybersecurity of cyber-physical grid infrastructure. Rather than offering a purely descriptive catalogue of prior work, the review critically evaluates the comparative strengths, computational costs, data requirements, interpretability limitations, and deployment barriers of competing AI paradigms, drawing on more than one hundred and twenty recent peer-reviewed sources. Comparative tables synthesising algorithmic performance, application maturity, and outstanding technical gaps are presented. The review further interrogates persistent challenges, including data scarcity for rare fault classes, the lack of explainability in deep models for safety-critical protection functions, adversarial vulnerability of learning-based controllers, and the absence of standardised benchmarks for cross-study comparison. The paper concludes that while AI techniques deliver demonstrable improvements in forecasting accuracy, fault diagnosis speed, and operational efficiency, their large-scale deployment in mission-critical protection and control loops remains constrained by reliability, interpretability, and regulatory certification requirements. Recommendations for hybrid physics-informed and explainable AI architectures are proposed as a pathway toward trustworthy adoption in next-generation power systems.},
      keywords = {artificial intelligence; power systems; machine learning; deep learning; smart grid; fault diagnosis; renewable energy forecasting.},
      month = {August},
      doi = {https://doi.org/10.64388/IREV10I2-1722424}
  }