Nonlinear observer-based fault diagnosis in photovoltaic systems integrated with voltage source converters using an extended Kalman filter
1
Applied Automation and Industrial Diagnostics Laboratory, Faculty of Science and Technology, University of Djelfa 17000 DZ, Algeria.
2
Department of Electrical Engineering, Faculty of Science and Technology, University Mohamed El Bachir El Ibrahimi of Bordj Bou Arreridj,34030 DZ, El Anasser, Bordj Bou Arreridj, Algeria
3
Applied Automation and Industrial Diagnostics Laboratory, Faculty of Science and Technology, University of Djelfa 17000 DZ, Algeria.
Submission date: 2025-12-07
Final revision date: 2026-06-03
Acceptance date: 2026-06-05
Online publication date: 2026-06-16
Publication date: 2026-06-16
Corresponding author
Hannene Hanene Chaourar
Applied Automation and Industrial Diagnostics Laboratory, Faculty of Science and Technology, University of Djelfa 17000 DZ, Algeria.
Applied Automation and Industrial Diagnostics Laboratory, Faculty of Science and Technology, University of Djelfa 17000 DZ, Algeria.
KEYWORDS
TOPICS
ABSTRACT
Amid the global shift to renewable energy, fault diagnosis systems are crucial for stabilizing and managing solar and wind systems. This work presents a nonlinear, observer-based fault diagnostic system for photovoltaic systems using a voltage source converter (VSC). By employing an extended Kalman filter (EKF), the system estimates states and detects voltage and current sensor faults in real time, enhancing reliability and reducing maintenance. A review of prior studies highlighted areas for improvement, guiding strategies to boost fault detection accuracy, system dependability, and maintenance efficiency. Results show EKF-based nonlinear controllers reduce estimation errors by 99.9%, detect faults within milliseconds, maintain MPPT efficiency above 95%, and improve output power stability compared to non-diagnosed systems. The VSC further enhances system response and stability under varying conditions.
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