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            <title xml:lang="en">Fault Detection and Diagnosis of PV Systems Using Kalman-Filter Algorithm Based on Multi-Zone Polynomial Regression.</title>
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                <title xml:lang="en">Fault Detection and Diagnosis of PV Systems Using Kalman-Filter Algorithm Based on Multi-Zone Polynomial Regression.</title>
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                <term xml:lang="en">Kalman Filter</term>
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              <p>Faults must be timely diagnosed, detected, and identified to enhance photovoltaic (PV) system’s dependability. In this context, this paper presents a novel Fault Detection and Diagnosis (FDD) methodology based on a hybrid combination of model-based, through Kalman Filter (KF), and a statistical data-driven regression approach for online monitoring of a PV system’s DC side. This statistical approach is formulated on Multi-Zone non-linear Polynomial regression (MZP) techniques of PV characteristics under Global Maximum Power Points (GMPP) at the array level. In particular, the proposed method effectively detects intermittent soft Short-Circuit (SC) even at very low irradiation. The performance of the proposed FDD methods is evaluated via MATLAB/Simulink® considering varying weather conditions.</p>
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