Joel Segun Ojerinde1*, Omobolaji HameenSonde2,  Joshua Oluwasegun Ogunlade3,

Ibrahim Ishola Mustapha4, Kaosarat Olamide Hamzat5 and Micheal Abimbola Oladosu 6-7. Moses Adondua Abah7

 1-5Department of Electrical and Electronics Engineering, Faculty of Engineering and Technology, Ilorin, Kwara State, Nigeria

6Department of Computer Science, Faculty of Science, University of Lagos, Akoka, Lagos, Nigeria.

7Department of Research and Linkages, Research Hub Nexus, Ayobo, Lagos, Nigeria.

() Corresponding Author

  Received: Apr 20, 2026/ Revised: June 2 2026/Accepted: June 4, 2026

Highlights

  • Systematically reviews hybrid model-based and data-driven state estimation techniques for microgrids.
  • Evaluates EKF–ML, physics-informed neural networks, and federated learning frameworks.
  • Highlights improved estimation accuracy and real-time performance in renewable-integrated microgrids.
  • Compares the strengths of physical modeling and machine learning-based approaches.
  • Identifies future challenges in cybersecurity, multi-energy integration, and benchmarking standards.

Abstract

The rapid proliferation of renewable energy sources (RES) within microgrids introduces substantial volatility and uncertainty that challenge conventional state estimation (SE) paradigms. Model-based approaches, while theoretically sound, often fail to capture the nonlinear dynamics introduced by photovoltaic arrays and wind turbines. Data-driven methods offer flexibility but lack the physical interpretability necessary for reliable real-time control. This systematic review critically appraises hybrid SE techniques that integrate model-based and data-driven approaches to achieve robust, real-time SE in renewable energy-integrated microgrids. Following PRISMA guidelines, 35 peer-reviewed studies published between 2020 and 2025 were identified and analysed from Scopus, Web of Science, and IEEE Xplore databases. Hybrid techniques, particularly physics-informed neural networks (PINNs), Extended Kalman Filter (EKF)-machine learning (ML) fusions, and federated learning frameworks, demonstrated superior accuracy, with root mean square errors (RMSE) below 0.015 per unit and real-time processing latencies under 50 ms. Hybrid SE frameworks represent the most promising trajectory for scalable, resilient microgrid state estimation, with critical gaps remaining in cybersecurity robustness, multi-energy system integration, and standardised benchmarking.

Keywords: State estimation; microgrids; hybrid methods; machine learning; Kalman filter; physics-informed neural networks; renewable energy; real-time systems

How to cite this article

Ojerinde, J. S., HameenSonde, O., Ogunlade, J. O., Mustapha, I. I., Hamzat, K. O., Oladosu, M. A., & Adondua Abah, M. (2026). A systematic review of hybrid model-based and data-driven techniques for real-time state estimation in renewable energy-integrated microgrids. Science Archives, 7(2), 133–142. https://doi.org/10.47587/SA.2026.7212

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