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.

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

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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.

Photovoltaic (PV) systems based on multistring configuration are the best effective solution, given its advantages in terms of system availability, reliability, and energy efficiency. In this particular configuration each substring has its own dc-dc converter and a dedicated maximum power search algorithm which increase the cost and complexity. In this article, an efficient centralized global maximum power tracking (GMPPT) algorithm for multistring PV array subject to partial shading conditions is proposed. The algorithm is based on artificial bee colony (ABC) as an optimization approach to provide the optimal duty cycles allowing the extraction of the optimal global maximum power from each substring. In particular, the proposed approach allows significant reduction of the required sensors to only one pair of current and voltage sensors, at the common point of connection of the overall PV strings. The simulation study has been carried out under Cadence/Pspice and MATLAB/Simulink platforms on the I-V curves to confirm the effectiveness of the proposed algorithm when several shading patterns occur. In addition, complex shading pattern of a daily profile has been also carried out to demonstrate the GMPPT finding in dynamically variable conditions. Performance comparison against particle swarm optimization based maximum power point tracking algorithm and the traditional perturb and observe method has also been carried out. The obtained simulation and experimental results have shown the effectiveness and a good tracking capability of the proposed ABC algorithm in a multistring PV array configuration under uniform and nonuniform irradiance. Index Terms-Artificial bee colony (ABC) algorithm, digital signal processor (DSP), global maximum power tracking