<p>With the quick incorporation of distributed photovoltaic (PV) systems into the contemporary energy grid, guaranteeing consistent power output and system reliability has become critical. These systems are susceptible to a variety of operational anomalies, including dust accumulation, and shading which can reduce energy efficiency without being detected. Traditional anomaly detection methods frequently fail to capture the internal dynamics and latent factors that affect PV system performance over time. There is an urgent need for a robust, real-time, and dynamic monitoring strategy that can accurately detect abnormal behavior in distributed PV systems. The goal of this research is to create and execute a state space model (SSM)-based anomaly detection framework that dynamically estimates system conditions and alerts to deviations from expected power output in real time. A dataset of 5000 records and 10 attributes was used, which contained time-series data from distributed PV systems, such as actual and expected power output, irradiance, temperature, and other operational parameters. Model matrices (A, B, and C) were estimated using linear regression and enhanced by Kalman filtering. The system estimated power output at each time step and calculated the deviation from actual values. A threshold-based approach was employed to classify anomalies. The model was trained on data labeled as normal and validated against both normal and abnormal entries. The proposed SSM-based anomaly detection model had an accuracy of 94.70%, a precision of 93.85%, a recall of 94.30%, an F1-score of 94.07%, and a Matthews Correlation Coefficient (MCC) of 89.32%, indicating strong predictive capability in detecting abnormal performance in PV systems.</p>