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

In this paper, a novel procedure for fault detection and diagnosis in the direct current (DC) side of PV system, based on probabilistic neural network (PNN) classifier, is proposed. The suggested procedure consists of four main stages: (i) PV module parameters extraction, (ii) PV array simulation and experimental validation (iii) elaboration of a relevant database of both healthy and faulty operations, and (iv) network construction, training and testing. In the first stage, the unknown electrical parameters of the one diode model (ODM) are accurately identified using the best-so-far ABC algorithm. Then, based on these parameters the PV array is simulated and experimentally validated by using a PSIM™/Matlab™ co-simulation. Finally, efficient fault detection and diagnosis procedure based on PNN classifier is implemented. Four operating cases were tested in a grid connected PV system of 9.54 kWp: Healthy system, three modules short-circuited in one string, ten modules short-circuited in one string, and a string disconnected from the array. Moreover, the PNN method was compared, under real operating conditions, with the feed forward back-propagation Artificial Neural Network (ANN) classifiers method, for noiseless and noisy data to evaluate the suggested method’s accuracy and test its aptitude to support noisy data. The obtained results have demonstrated the high efficiency of the proposed method to detect and diagnose DC side anomalies for both noiseless and noisy data cases.Peer ReviewedPostprint (author's final draft

In this paper, a heuristic optimization approach based on Artificial Bee Colony (ABC) algorithm is applied to the extraction of the five electrical parameters of a photovoltaic (PV) module. The proposed approach has several interesting features such as no prior knowledge of the physical system and its convergence is not dependent on the initial conditions. The extracted parameters have been tested against several static IV characteristics of different PV modules from different manufacturers. In order to assess the effectiveness of the extracted parameters, a dynamic model based maximum power point has been used and compared to real measurements data of a grid connected system located in the Centre de Developpement des Energies Renouvelables (CDER) in Algiers. In addition, comparison of the proposed ABC algorithm with some wellknown heuristic algorithms such as, Particle Swarm Optimization (PSO) and Differential Evolution (DE), has given better results in terms of local minimum avoidance and accuracy.

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This paper proposes a simple method to detect and diagnose short circuits and open circuits faults in photovoltaic (PV) systems based on the evaluation of three coefficients. The proposed method consists fundamentally on two steps: an offline step based on a simulated model and an online step in which a comparison of the real measured coefficients against those obtained in the offline step is performed. The simulated model of the PV array has been validated using a real experimental data of a daily profile from a 3 kWp grid connected system installed at Algiers. The effectiveness of the proposed method has been evaluated based on PSIM™/Matlab™ Co-simulation approach of four operating cases: healthy operating case, one short circuit module in a string operating case, five short circuits modules in a string operating case, and finally a completely disconnected string operating case. Simulation results have demonstrated the ability of the proposed method to detect and diagnose short circuits and open circuits faulty operation under any meteorological conditions.Peer ReviewedPostprint (published version

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