Fault Detection and Diagnosis of PV Systems Using Kalman-Filter Algorithm Based on Multi-Zone Polynomial Regression. - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2022

Fault Detection and Diagnosis of PV Systems Using Kalman-Filter Algorithm Based on Multi-Zone Polynomial Regression.

Résumé

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.
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Dates et versions

hal-03916297 , version 1 (30-12-2022)

Identifiants

  • HAL Id : hal-03916297 , version 1

Citer

Al-Rifai Yehya, Adriana Aguilera Gonzalez, Ionel Vechiu. Fault Detection and Diagnosis of PV Systems Using Kalman-Filter Algorithm Based on Multi-Zone Polynomial Regression.. 16th European Workshop on Advanced Control and Diagnosis - ACD2022, Nov 2022, Nancy, France. ⟨hal-03916297⟩
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