Parameters extraction of single diode model for degraded photovoltaic modules

Piliougine, Michel; Guejia-Burbano, Rudy Alexis; Petrone, G.; Sánchez‐Pacheco, Francisco José; Mora‐López, Llanos; Sidrach‐de‐Cardona, M.

doi:10.1016/j.renene.2020.09.035

Cited by 58 publications

(18 citation statements)

References 33 publications

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“…Finally, a fifth‐degree polynomial is fitted using an LMS regression and the first derivative of that polynomial is fixed to be equal to zero 40 . Among all the roots, the real one falling in the voltage range of the selected points is considered, so that V Pmax , I Pmax , and P max are determined.…”

Section: Methodsologymentioning

confidence: 99%

Temperature coefficients of degraded crystalline silicon photovoltaic modules at outdoor conditions

Piliougine

Oukaja

Sidrach‐de‐Cardona

et al. 2021

Progress in Photovoltaics

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The temperature effect on photovoltaic modules is usually quantified by means of some coefficients relating the variations of the open‐circuit voltage, of the short‐circuit current, and of the maximum power to temperature changes. In this paper, comprehensive experimental guidelines to estimate the temperature coefficients when measurements are performed outdoors are given. In addition, a correction procedure is applied to the short‐circuit current to cancel out the influence of the fluctuations in the irradiance level. The experimental results shown in this paper refer to several single‐crystalline silicon modules that have been operating for 21 years. Consequently, the estimated values of the temperature coefficients also account for module ageing processes. The coefficients of determination achieved in the case of the temperature coefficients of the open‐circuit voltage and the maximum power are very close to one. Results concerning the temperature coefficients of the short‐circuit current are also suitable if they are previously corrected.

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Section: Methodsologymentioning

confidence: 99%

Temperature coefficients of degraded crystalline silicon photovoltaic modules at outdoor conditions

Piliougine

Oukaja

Sidrach‐de‐Cardona

et al. 2021

Progress in Photovoltaics

Self Cite

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“…Namely, a degradation fault can happen because of an increase in the series resistance between the modules due to limited adherence to contacts or corrosion induced by water vapor [62]. Degradation faults can negatively impact the performances of PV systems by causing power losses [63].…”

Section: Pv System With Module Degradationmentioning

confidence: 99%

Monitoring of Photovoltaic Systems Using Improved Kernel-Based Learning Schemes

Harrou

Saidi

Sun

et al. 2021

IEEE J. Photovoltaics

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Data-based procedures for monitoring the operating performance of a PV system are proposed in this paper. The only information required to apply the procedures is the availability of system measurements, which are routinely on-line collected via sensors. Here, kernel-based machine learning methods, including support vector regression (SVR) and Gaussian process regression (GPR), are used to model multivariate data from the PV system for fault detection due to their flexibility and capability to nonlinear approximation. Essentially, the SVR and GPR models are adopted to obtain residuals to detect and identify occurred faults. Then, residuals are passed through an exponential smoothing filter to reduce noise and improve data quality. In this work, a monitoring scheme based on kernel density estimation is used to sense faults by examining the generated residuals. Several different scenarios of faults were considered in this study, including PV string fault, partial shading, PV modules shortcircuited, module degradation, and line-line faults on the PV array. Using data from a 20 MWp grid-connected PV system, the considered faults were successfully traced using the developed procedures. Also, it has been demonstrated that GPR-based monitoring procedures achieve better detection performance over SVRs to monitor PV systems.

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“…From the names of these models, it is clear that they are distinguished by the number of diodes in the model. The SDM has one diode to represent the diffusion current in the P-N junction [8][9][10], a resistance connected in series with the diode to represent the total resistance of the semiconductor material at the neutral region, and a resistance connected in parallel with the diode to represent the total current leakage resistance across the P-N junction of the solar cell. The DDM has two diodes, one series resistance, and one parallel resistance; the second diode represents the recombination effect in the P-N junction [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Accurate Photovoltaic Models Based on an Adaptive Opposition Artificial Hummingbird Algorithm

et al. 2022

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The greater the demand for energy, the more important it is to improve and develop permanent energy sources, because of their advantages over non-renewable energy sources. With the development of artificial intelligence algorithms and the presence of so many data, the evolution of simulation models has increased. In this research, an improvement to one recent optimization algorithm called the artificial hummingbird algorithm (AHA) is proposed. An adaptive opposition approach is suggested to select whether or not to use an opposition-based learning (OBL) method. This improvement is developed based on adding an adaptive updating mechanism to enable the original algorithm to obtain more accurate results with more complex problems, and is called the adaptive opposition artificial hummingbird algorithm (AOAHA). The proposed AOAHA was tested on 23 benchmark functions and compared with the original algorithm and other recent optimization algorithms such as supply–demand-based optimization (SDO), wild horse optimizer (WHO), and tunicate swarm algorithm (TSA). The proposed algorithm was applied to obtain accurate models for solar cell systems, which are the basis of solar power plants, in order to increase their efficiency, thus increasing the efficiency of the whole system. The experiments were carried out on two important models—the static and dynamic models—so that the proposed model would be more representative of real systems. Two applications for static models have been proposed: In the first application, the AOAHA satisfies the best root-mean-square values (0.0009825181). In the second application, the performance of the AOAHA is satisfied in all variable irradiance for the system. The results were evaluated in more than one way, taking into account the comparison with other modern and powerful optimization techniques. Improvement showed its potential through its satisfactory results in the tests that were applied to it.

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Parameters extraction of single diode model for degraded photovoltaic modules

Cited by 58 publications

References 33 publications

Temperature coefficients of degraded crystalline silicon photovoltaic modules at outdoor conditions

Temperature coefficients of degraded crystalline silicon photovoltaic modules at outdoor conditions

Monitoring of Photovoltaic Systems Using Improved Kernel-Based Learning Schemes

Accurate Photovoltaic Models Based on an Adaptive Opposition Artificial Hummingbird Algorithm

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