Employing Extended Kalman Filter for Faulty Sensor Detection in Water Distribution Systems

State estimation techniques offer an effective approach for integrating information from hydraulic models with sensor measurements, providing a more accurate representation of the system dynamics. The accuracy of state estimation depends heavily on the reliability of sensor data, making the identification of faulty sensors critical for decision-makers who rely on model estimates. This study proposes a new approach for detecting faulty sensors in water distribution systems to mitigate the adverse effects of incorrect measurements on operational decisions. We utilize the Extended Kalman Filter as the state estimation method and introduce a masking approach for identifying faulty pressure sensors. The effectiveness of the proposed approach is evaluated using a benchmark network model, demonstrating its proficiency in detecting faulty sensor data.