Robust series resistance estimation for diagnostics of photovoltaic modules

Séra, Dezső; Teodorescu, Remus

doi:10.1109/iecon.2009.5415022

Cited by 22 publications

(13 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [76] another method to find R s is proposed. In this scheme the derivative of voltage with respect to current is calculated at point nearby I MPP .…”

Section: Current Methodologies For Determination Of Shunt and Series mentioning

confidence: 99%

Detection of Typical Defects in Silicon Photovoltaic Modules and Application for Plants with Distributed MPPT Configuration

et al. 2019

View full text Add to dashboard Cite

During their operational life, photovoltaic (PV) modules may exhibit various defects for poor sorting of electrical performance during manufacturing, mishandling during transportation and installation, and severe thermo-mechanical stresses. Electroluminescence testing and infrared thermographic imaging are the most common tests for checking these defects, but they are only economically viable for large PV plants. The defects are also manifested as abnormal electrical properties of the affected PV modules. For defect diagnosis, the appropriate parameters on their I-V curves are open circuit voltage, photo-generated current, series resistance, and the shunt resistance. The health of PV modules can be assessed by calculating these values and comparing them with the reference parameters. If these defects are diagnosed in time, the power loss is avoided and safety hazards are mitigated. This paper first presents a review of common defects in PV modules and then a review of the methods used to find the above-mentioned parameters during the normal PV operation. A simple approach to determine the resistances of the equivalent circuit is discussed. Finally, through a modification in an ordinary maximum power point tracking (MPPT) algorithm, information about the state of health of PV modules is obtained. This method is effective, especially if applied to submodule-integrated MPPT architectures.

show abstract

“…In [76] another method to find R s is proposed. In this scheme the derivative of voltage with respect to current is calculated at point nearby I MPP .…”

Section: Current Methodologies For Determination Of Shunt and Series mentioning

confidence: 99%

Detection of Typical Defects in Silicon Photovoltaic Modules and Application for Plants with Distributed MPPT Configuration

et al. 2019

View full text Add to dashboard Cite

show abstract

“…I-V curve without considering would be somewhat dissimilar than the curves outlined including its value. On the basis of annual simulation, the predicted power output from PV systems will be 5% to 8% lower when correct series resistance is not used [32,51]. It can be determined as oc,…”

Section: Reverse Saturation Current ( )mentioning

confidence: 99%

An Improved Mathematical Model for Computing Power Output of Solar Photovoltaic Modules

Jakhrani

Samo

Kamboh

et al. 2014

International Journal of Photoenergy

View full text Add to dashboard Cite

It is difficult to determine the input parameters values for equivalent circuit models of photovoltaic modules through analytical methods. Thus, the previous researchers preferred to use numerical methods. Since, the numerical methods are time consuming and need long term time series data which is not available in most developing countries, an improved mathematical model was formulated by combination of analytical and numerical methods to overcome the limitations of existing methods. The values of required model input parameters were computed analytically. The expression for output current of photovoltaic module was determined explicitly by Lambert W function and voltage was determined numerically by Newton-Raphson method. Moreover, the algebraic equations were derived for the shape factor which involves the ideality factor and the series resistance of a single diode photovoltaic module power output model. The formulated model results were validated with rated power output of a photovoltaic module provided by manufacturers using local meteorological data, which gave ±2% error. It was found that the proposed model is more practical in terms of precise estimations of photovoltaic module power output for any required location and number of variables used.

show abstract

“…The training of an RBFNNs network involves the determination of a number of RBFs and optimal values of the centers, weights and biases [14], [15], [16] and [17]. The criterion is to minimize the sum of squared errors.…”

Section: The Proposed Rbfnn Based Modelmentioning

confidence: 99%

A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module

et al. 2012

View full text Add to dashboard Cite

The design process of photovoltaic (PV) modules can be greatly enhanced by using advanced and accurate models in order to predict accurately their electrical output behavior. The main aim of this paper is to investigate the application of an advanced neural network based model of a module to improve the accuracy of the predicted output I-V and P-V curves and to keep in account the change of all the parameters at different operating conditions. Radial basis function neural networks (RBFNN) are here utilized to predict the output characteristic of a commercial PV module, by reading only the data of solar irradiation and temperature. A lot of available experimental data were used for the training of the RBFNN, and a backpropagation algorithm was employed. Simulation and experimental validation is reported.

show abstract

Robust series resistance estimation for diagnostics of photovoltaic modules

Cited by 22 publications

References 16 publications

Detection of Typical Defects in Silicon Photovoltaic Modules and Application for Plants with Distributed MPPT Configuration

Detection of Typical Defects in Silicon Photovoltaic Modules and Application for Plants with Distributed MPPT Configuration

An Improved Mathematical Model for Computing Power Output of Solar Photovoltaic Modules

A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module

Contact Info

Product

Resources

About