Fault Detection In Photovoltaic Systems Research Articles

Extended analysis on Line-Line and Line-Ground faults in PV arrays and a compatibility study on latest NEC protection standards

Even with the expeditious progress in global Photovoltaic (PV) power generation, faults occurring in PV systems pose excessive challenges to the productivity and reliability of PV installations. Though specific installation standards have been developed for the protection of PV systems, the compatibility of these standards to cope with the unique operating characteristics of PV generating systems is questionable and hence, needs critical evaluation. Therefore, this paper briefly analyzes the standards available for the protection of PV systems, investigates the protection challenges and inspects the compatibility of latest National Electric Code (NEC) standards to protect PV arrays against Line-Line (LL) and Line-Ground (LG) fault occurrences. In particular, this article conducts a detailed behavioral study on LL and LG faults and evaluates the compatibility of NEC standards in the context of: (1) Varying mismatch levels, (2) Impact of Maximum Power Point Trackers (MPPTs) and (3) Changing irradiation levels. Detailed simulations as well as experimental analysis have been carried out to clearly portray the challenges in LL/LG fault detection despite by following new NEC recommendations. Further, based on the implications attained, some suggestions for reliable fault detection have also been presented that are expected to enhance the reliability of LL/LG fault detection in PV systems.

Bond Graph Model for Fault Detection of Partial Shaded PV Array Considering Different Module Connection Schemes and Effects of Bypass Diodes

Fault detection in solar photovoltaic (PV) arrays is a fundamental task to protect PV modules from damage and to eliminate risks of safety hazards. In this work, we show a new methodology for automatic supervision and fault detection of PV Systems, based mainly on optimal placement of sensors. This supposes the possibility to build a dynamic model of the system by using the bond graph tool, and the existence of a degradation model in order to predict its future health state. The choice of bond graph is motivated by the fact that it is well suited for modeling physical systems where several types of energies are involved. Fault behavior of PV arrays is highly related to the fault location, fault impedance, irradiance level, and use of blocking diodes. In this work, PV array is connected using series parallel (SP) and Total Cross Tied (TCT) configurations including sensors to measure voltage and currents. The simulation results show the importance of the approach applied for the detection and diagnosis of fault in PV system. These results have been contrasted with real measured data from a measurement campaign plant carried on electrical engineering laboratory of Grenoble using various interconnection schemes are presented.

Online reduced kernel GLRT technique for improved fault detection in photovoltaic systems

This paper proposes an effective kernel generalized likelihood ratio test (KGLRT) technique for fault detection in Photovoltaic (PV) systems. The proposed technique is considered as an improvement of the conventional KGLRT with extended online capabilities and lower computational complexity. The proposed online reduced KGLRT (OR-KGLRT) is based on transforming the process data into a higher dimensional space (where the data becomes linear), which makes the kernel-based scheme attractive for modeling nonlinear systems. The performance of the proposed method is evaluated and compared to the conventional KGLRT statistic using a simulated PV data. Both techniques are applied to detect single and multiple failures (including Bypass, Mismatch, Mix and Shading failures). The selected performance criteria are the good detection rate (GDR), false alarm rate (FAR), and computation time (CT). Simulation results show superior detection efficiency of the proposed approach compared to the conventional KGLRT statistic in terms of GDR, FAR and CT.

Design of a DC Series Arc Fault Detector for Photovoltaic System Protection

With the skyrocketing growth in global photovoltaic (PV) power capacity, fault detection of PV systems has gained prodigious importance in recent years. It has been known that in PV system the dc series arc fault is more difficult to be detected than the parallel arc fault. With the proposed technologies of characteristic extraction, filtering, and fault identification, a novel efficient detection algorithm protecting a PV system against the dc series arc fault is proposed. The detection algorithm is realized on an arc fault detector (AFD) based on a cost-effective TMS320F28335 digital signal processor. To verify the feasibility and effectiveness of the proposed AFD, a relevant experiment PV system is conducted to compare with two commercial AFDs by various testing states. A great deal of empirical evidence shows that the detection performance of the proposed AFD outperforms that of the commercially available AFDs. The research may be of some reference value for similar works in the future.

A Comparative Evaluation of Advanced Fault Detection Approaches for PV Systems

Even with the consistent growth in global photovoltaic (PV) capacity, the necessity for fault detection in PV systems has not been widely addressed regardless of its importance. With International Electrotechnical Commission, Institute of Electrical and Electronics Engineers, and National Electric Code protection standard recommendations for PV systems being vulnerable to fault occurrences, advanced detection techniques are inevitable in PV systems to avail guaranteed protection from electric shocks and fire hazards. Even though numerous fault detection techniques have been conceptualized in last few years, these techniques are yet to be classified and quantified in a common platform. Hence, this paper takes up an initiative to study, classify and analyze the advanced fault detection approaches available in literature. Each fault detection technique is segregated based on the detection approach and are reviewed with respect to: types of faults detected; detection time; sensor requirement; procedural complexity; detection variables; and level of protection achieved. Furthermore, a compatibility study is conducted to evaluate the effectiveness of advanced fault detection techniques for protection against line-line, line-ground, and arc faults that are most common in PV systems. Overall, this investigation serves as a valuable reference for researchers to improve fault detection possibilities in PV systems.

Robust and flexible strategy for fault detection in grid-connected photovoltaic systems

Reliable and efficient detection of faults in photovoltaic systems provides pertinent information for improving their safety and productivity. However, data gathered from photovoltaic systems are generally tainted with a large amount of noise, which can swamp the most relevant features necessary to detect faults, and ultimately degrades fault detection capability of the monitoring system. Therefore, it is crucial to design a robust fault detection approach to deal with the problem of measurement noise in the data. The purpose of this study is to design a robust fault detection scheme to monitor the direct current side of a photovoltaic system and able to deal with the problem of measurement noise in the data by using multiscale representation. Towards this end, a framework merging the benefits of multiscale representation of data and those of the exponentially-weighted moving average scheme to suitably detect faults is proposed and used in the context of fault detection in photovoltaic systems. Here, multiscale representation of data using wavelets, an efficient feature/noise separation technique, is used to enhance fault detection performance by reducing noise effect and false alarms. First, a simulation model for the monitored photovoltaic array is built. Then residuals from the simulation model are used as the input for the designed scheme for fault detection. A real data from a 9.54 kWp photovoltaic plant in Algiers, Algeria is used to evaluate the effectiveness proposed method. Also, the performance of the proposed chart to that of the conventional exponentially-weighted moving average chart has been compared and found improved sensitivity to faults and robustness to noises.

Wavelet packet and support vector machine analysis of series DC ARC fault detection in photovoltaic system

AbstractIn a photovoltaic (PV) system, the serial arc is mainly due to the discontinuity in the current‐carrying conductor. Different from the AC arc, the DC arc does not have a periodic zero‐crossing and more easily blossoms into sustained arc, which is more likely to cause accidents. When a serial arc occurs in the DC system, it would lead to a steep drop in current or some unpredictable, irregular change of the current wave. The occurrence of the serial arc can be detected by analyzing the change of amplitude at different frequencies. An arc‐fault detection method based on wavelet packet (WP) and support vector machine (SVM) analysis is proposed in this paper. The threshold to distinguish arcing from normal operation is determined by analyzing the characteristics of the WP coefficients of the DC arc current collected from a real system. Compared to that of the fast Fourier transform (FFT), the effectiveness of the proposed algorithms has been validated with experiments in a 5‐kW grid‐connected PV inverter. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Monitoring and enhanced dynamic modeling of battery by genetic algorithm using LabVIEW applied in photovoltaic system

The dynamic modeling of batteries used in photovoltaic systems (PVS), exceptionally in stand-alone or microgrid systems, is considered as an important issue and a major problem for the monitoring and simulation and even for fault detection applications. In this work, we propose as first step an enhancement of CIEMAT model by the improvement of 21 charge and discharge parameters; also we propose an enhancement of 4 parameters for the estimation approximately of the gassing and the saturation levels by a new static method using genetic algorithm. Basically, this method relies on static measurements by charging of the battery with different constant currents to give a more accurate estimation of this area. Furthermore, a real-time interface system using LabVIEW using the improved model was proposed in this work to provide online estimation and measurements of all battery data and characteristics in PVS. The proposed robust and low-cost method of simulation and monitoring can be applied for the study of battery fault detection in PVS.

Statistical fault detection in photovoltaic systems

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.