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Faults in photovoltaic (PV) systems, which can result in energy loss, system shutdown or even serious safety breaches, are often difficult to avoid. Fault detection in such systems is imperative to improve their reliability, productivity, safety and efficiency. Here, an innovative model-based fault-detection approach for early detection of shading of PV modules and faults on the direct current (DC) side of PV systems is proposed. This approach combines the flexibility, and simplicity of a one-diode model with the extended capacity of an exponentially weighted moving average (EWMA) control chart to detect incipient changes in a PV system. The one-diode model, which is easily calibrated due to its limited calibration parameters, is used to predict the healthy PV array’s maximum power coordinates of current, voltage and power using measured temperatures and irradiances. Residuals, which capture the difference between the measurements and the predictions of the one-diode model, are generated and used as fault indicators. Then, the EWMA monitoring chart is applied on the uncorrelated residuals obtained from the one-diode model to detect and identify the type of fault. Actual data from the grid-connected PV system installed at the Renewable Energy Development Center, Algeria, are used to assess the performance of the proposed approach. Results show that the proposed approach successfully monitors the DC side of PV systems and detects temporary shading.Peer ReviewedPostprint (author's final draft

Abstract:Optimal energy harvesting is a key point in any photovoltaic system where economic and efficiency aspects are strongly interrelated. In this paper a novel artificial bee colony optimization-based MPPT is proposed. The proposed Bee's algorithm allows the tracking of the maximal available power from a PV array under uniform and nonuniform illuminating conditions. A co-simulation methodology, combining Matlab/Simulink TM and Cadence/Pspice TM , has been used to verify the effectiveness of Bee's algorithm to track the MPP of serially connected PV modules subject to various shading patterns. In addition, a performance comparison with Particle Swarm Optimization (PSO) based MPPT algorithm is also presented. The experimental resuls have shown the validity of the developed heuristic algorithm and its good tracking capabilities under shading conditions.

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