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Journal ArticleDOI

Photovoltaic fault detection algorithm based on theoretical curves modelling and fuzzy classification system

01 Dec 2017-Energy (Elsevier)-Vol. 140, pp 276-290
TL;DR: A fault detection algorithm based on the analysis of the theoretical curves which describe the behavior of an existing PV system can accurately detect different faults occurring in the PV system, where the maximum detection accuracy of before considering the fuzzy logic system is equal to 95.27%.
About: This article is published in Energy.The article was published on 2017-12-01 and is currently open access. It has received 101 citations till now. The article focuses on the topics: Fault detection and isolation & Fuzzy classification.

Summary (1 min read)

INTRODUCTION

  • And therefore usually require low maintenance, they are still subject to various failures and faults associated with the PV arrays, batteries, power conditioning units, utility interconnections and wiring [1 and 2] .
  • There are existing fault detection techniques for use in GCPV plants.
  • The approach uses ±3σ statistical analysis technique for identifying the faulty conditions in the DC/AC inverter units.

3. GCPV Fault Detection Algorithm Validation

  • The performance of the proposed fault detection algorithm is verified.
  • For this purpose, the acquired data for various days have been considered using 1.1 kWp GCPV plant.
  • The time zone for all measurements is GMT.

Efficicnecy =

  • Measured Output Power Theoretical Power (10) From Fig. 10(B ), the efficiency of the GCPV system decreased while increasing the PS applied to the PV system.
  • The detection accuracy rate can be increased using a fuzzy logic classification system.
  • Fig. 11 (A) illustrates one examined case scenario which shows the percentage of the partial shading on each examined PV module.
  • In order to detect all LMPPs and the GMPP obtained by the MPPT unit, it is required to further investigate MPPT techniques which is not one of the targets of this manuscript.

3.5 Discussion

  •  The fault detection algorithm can be used with wide range of PV installation, since it depends on the analysis of the power and the voltage ratios.
  • Multiple faults can be detected accurately, the minimum and maximum detection accuracy obtained by the algorithm are equal to 98.8% and 99.31% respectively. .
  • The efficiency of the voltage and current sensor has been taken into account in the mathematical modelling for the proposed fault detection algorithm.
  • Hot spot detection can also be evaluated using the proposed theoretical curves modelling.

Disadvantages:

  •  The algorithm depends on the voltage and the power ratios of the GCPV systems.
  • The algorithm is not capable of detecting faults occurring in the bypass diodes, which are commonly used nowadays with PV systems.
  • The fault detection algorithm cannot detect any fault arising in the DC/AC inverter units which are commonly used with GCPV systems.

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Citations
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Journal ArticleDOI
TL;DR: The types and causes of PV systems (PVS) failures are presented, then different methods proposed in literature for FDD of PVS are reviewed and discussed; particularly faults occurring in PV arrays (PVA).
Abstract: Faults in any components (modules, connection lines, converters, inverters, etc.) of photovoltaic (PV) systems (stand-alone, grid-connected or hybrid PV systems) can seriously affect the efficiency, energy yield as well as the security and reliability of the entire PV plant, if not detected and corrected quickly. In addition, if some faults persist (e.g. arc fault, ground fault and line-to-line fault) they can lead to risk of fire. Fault detection and diagnosis (FDD) methods are indispensable for the system reliability, operation at high efficiency, and safety of the PV plant. In this paper, the types and causes of PV systems (PVS) failures are presented, then different methods proposed in literature for FDD of PVS are reviewed and discussed; particularly faults occurring in PV arrays (PVA). Special attention is paid to methods that can accurately detect, localise and classify possible faults occurring in a PVA. The advantages and limits of FDD methods in terms of feasibility, complexity, cost-effectiveness and generalisation capability for large-scale integration are highlighted. Based on the reviewed papers, challenges and recommendations for future research direction are also provided.

308 citations

Journal ArticleDOI
TL;DR: The comparison results indicate that the generalization performance of the proposed RF based model is better than the one of the decision tree based model, therefore, the proposed optimal RF based method is an effective and efficient alternative to detect and classify the faults of PV arrays.

177 citations

Journal ArticleDOI
TL;DR: A new fault detection algorithm for photovoltaic (PV) systems based on artificial neural networks (ANN) and fuzzy logic system interface and both Mamdani, Sugeno fuzzy logic systems interface is proposed.

167 citations

Journal ArticleDOI
Zhicong Chen1, Chen Yixiang1, Lijun Wu1, Shuying Cheng1, Peijie Lin1 
TL;DR: A novel intelligent fault detection and diagnosis method for photovoltaic arrays based on a newly designed deep residual network model trained by the adaptive moment estimation deep learning algorithm, which can automatically extract features from raw current-voltage curves and ambient irradiance and temperature, and effectively improve the performance with a deeper network.

165 citations

Journal ArticleDOI
TL;DR: The proposed Convolutional Neural Network based photovoltaic array fault diagnosis method only takes the array of voltage and current of the photov Boltaic array as the input features and the reference panels used for normalization.

100 citations

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Proceedings ArticleDOI
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TL;DR: A PV panel model is built and tested, which is able to predict the panel behavior in different temperature and irradiance conditions, based on the single-diode five-parameters model.
Abstract: This work presents the construction of a model for a PV panel using the single-diode five-parameters model, based exclusively on data-sheet parameters. The model takes into account the series and parallel (shunt) resistance of the panel. The equivalent circuit and the basic equations of the PV cell/panel in Standard Test Conditions (STC)1 are shown, as well as the parameters extraction from the data-sheet values. The temperature dependence of the cell dark saturation current is expressed with an alternative formula, which gives better correlation with the datasheet values of the power temperature dependence. Based on these equations, a PV panel model, which is able to predict the panel behavior in different temperature and irradiance conditions, is built and tested.

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"Photovoltaic fault detection algori..." refers methods in this paper

  • ...The five parameter model is determined by solving the transcendental equation (1) using Newton102 Raphson algorithm [26] based only on the datasheet of the available parameters for the examined PV 103 module that was used in this work as shown in Table 1....

    [...]

Journal ArticleDOI
TL;DR: In this article, a procedure of simulation and modelling solar cells and PV modules, working partially shadowed in Pspice environment, is presented, where simulation results have been contrasted with real measured data from a commercial PV module of 209 Wp from Siliken.

448 citations

Journal ArticleDOI
TL;DR: An attempt has been made to review the applications of fuzzy logic based models in renewable energy systems namely solar, wind, bio-energy, micro-grid and hybrid applications and indicates that fuzzy based models provide realistic estimates.
Abstract: In recent years, with the advent of globalization, the world is witnessing a steep rise in its energy consumption. The world is transforming itself into an industrial and knowledge society from an agricultural one which in turn makes the growth, energy intensive resulting in emissions. Energy modeling and energy planning is vital for the future economic prosperity and environmental security. Soft computing techniques such as fuzzy logic, neural networks, genetic algorithms are being adopted in energy modeling to precisely map the energy systems. In this paper, an attempt has been made to review the applications of fuzzy logic based models in renewable energy systems namely solar, wind, bio-energy, micro-grid and hybrid applications. It is found that fuzzy based models are extensively used in recent years for site assessment, for installing of photovoltaic/wind farms, power point tracking in solar photovoltaic/wind, optimization among conflicting criteria. The review indicates that fuzzy based models provide realistic estimates.

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Journal ArticleDOI
TL;DR: In this article, a fault diagnostic technique for photovoltaic systems based on Artificial Neural Networks (ANN) is proposed for a given set of working conditions -i.e., solar irradiance and PV module's temperature -a number of attributes such as current, voltage, and number of peaks in the current voltage characteristics of the PV strings are calculated using a simulation model.

392 citations

Journal ArticleDOI
TL;DR: In this paper, an automatic supervision and fault detection procedure for PV systems, based on the power losses analysis, has been presented, which includes parameter extraction techniques to calculate main PV system parameters from monitoring data, taking into account the environmental irradiance and module temperature evolution.

351 citations


"Photovoltaic fault detection algori..." refers methods in this paper

  • ...Silvestre et al [7], presented a new automatic supervision and fault 38 detection technique which use a standard deviation method (±2σ) for detecting various faults in PV 39 systems such as faulty modules in a PV string and faulty maximum power point tracking (MPPT) units....

    [...]

  • ...A. Chouder & S. Silvestre et al [7], presented a new automatic supervision and fault 38 detection technique which use a standard deviation method (±2σ) for detecting various faults in PV 39 systems such as faulty modules in a PV string and faulty maximum power point tracking (MPPT) units....

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Frequently Asked Questions (1)
Q1. What are the contributions in this paper?

9 This work proposes a fault detection algorithm based on the analysis of the theoretical curves which 10 describe the behaviour of an existing grid-connected photovoltaic ( GCPV ) plant. 13 Furthermore, a third order polynomial function is used to generate two detection limits ( high and low 14 limit ) for the VR and PR ratios obtained using LabVIEW simulation tool. Furthermore, 17 samples that lies out of the detection limits are processed by a fuzzy logic classification system which 18 consists of two inputs ( VR and PR ) and one output membership function.