PV panel modeling and identification

In this chapter, the modelling techniques of PV panels from I-V characteristics are discussed. At the beginning, a necessary review on the various methods are presented, where difficulties in mathematics, drawbacks in accuracy, and challenges in implementation are highlighted. Next, a novel approach based on linear system identification is demonstrated in detail. Other than the prevailing methods of using approximation (analytical methods), iterative searching (classical optimization), or soft computing (artificial intelligence), the proposed method regards the PV diode model as the equivalent output of a dynamic system, so the diode model parameters can be linked to the transfer function coefficients of the same dynamic system. In this way, the problem of solving PV model parameters is equivalently converted to system identification in control theory, which can be perfectly solved by a simple integral-based linear least square method. Graphical meanings of the proposed method are illustrated to help readers understand the underlying principles. As compared to other methods, the proposed one has the following benefits: 1) unique solution; 2) no iterative or global searching; 3) easy to implement (linear least square); 4) accuracy; 5) extendable to multi-diode models. The effectiveness of the proposed method has been verified by indoor and outdoor PV module testing results. In addition, possible applications of the proposed method are discussed like online PV monitoring and diagnostics, noncontact measurement of POA irradiance and cell temperature, fast model identification for satellite PV panels, and etc

EQUIVALENT MODELS FOR PHOTOVOLTAIC CELL – A REVIEW

Over the years, the contribution of photovoltaic energy to an eco-friendly world is continually increasing. Photovoltaic (PV) cells are commonly modelled as circuits, so finding the appropriate circuit model parameters of PV cells is crucial for performance evaluation, control, efficiency computations and maximum power point tracking of solar PV systems. The problem of finding circuit model of solar PV cells is referred to as “PV cell equivalent model problem”. In this paper, the existing research works on PV cell model parameter estimation problem are classified according to error quali-quantitative analysis, number of parameters, translation equations and PV technology. The existent models were discussed pointing out its different levels of approximation. A qualitative comparative ranking was made and four models were found to be the best ones for simulating PV cells. Besides, based on the conducted review, some recommendations for future research are provided

Polychromatic determination of spectral response of PV devices

This thesis introduces a novel spectral response (SR) measurement technique using polychromatic filters (filters with very broad spectral transmittances) to determine SR of large area PV devices. Conventionally, SR of a photovoltaic (PV) device is determined by illuminating the device under test (DUT) with a series of monochromatic beams at different wavelengths as described in the international standard IEC 60904-8, or beams of limited spectral content using narrow band pass filters or monochromator. One significant problem associated with the application of the narrow band pass filters for a large-area SR measurement is that low light intensity produced on the measurement plane particularly in certain wavelength ranges: the ultraviolet and infrared. This can produce weak signal responses from a tested PV device. In addition, the imperfection of the filter s mounting position can shift the peak wavelength of the filter s transmittance at angle of incidence greater than 10. This can cause stray light on the measurement plane. The proposed SR measurement method is called as the polychromatic SR fitting method or, in short, it is known as the polychromatic method . The advantage of this method is that higher beam intensity can be produced on the measurement plane as a result of large spectral transmittance of the polychromatic filters. This can improve the signal strength of a tested PV device. This new SR measurement method works by comparing the variations in the currents which are measured at different spectra to the currents which are calculated at the same spectral conditions using the SR model. Validations of this method for a large- and small-area SR determinations show that it is potentially feasible as a new technique for determining SR of a PV device with deviations within ±2% across the wavelength bands

Fault diagnosis for PV arrays considering dust impact based on transformed graphical feature of characteristic curves and convolutional neural network with CBAM modules

Various faults can occur during the operation of PV arrays, and both the dust-affected operating conditions and various diode configurations make the faults more complicated. However, current methods for fault diagnosis based on I-V characteristic curves only utilize partial feature information and often rely on calibrating the field characteristic curves to standard test conditions (STC). It is difficult to apply it in practice and to accurately identify multiple complex faults with similarities in different blocking diodes configurations of PV arrays under the influence of dust. Therefore, a novel fault diagnosis method for PV arrays considering dust impact is proposed. In the preprocessing stage, the Isc-Voc normalized Gramian angular difference field (GADF) method is presented, which normalizes and transforms the resampled PV array characteristic curves from the field including I-V and P-V to obtain the transformed graphical feature matrices. Then, in the fault diagnosis stage, the model of convolutional neural network (CNN) with convolutional block attention modules (CBAM) is designed to extract fault differentiation information from the transformed graphical matrices containing full feature information and to classify faults. And different graphical feature transformation methods are compared through simulation cases, and different CNN-based classification methods are also analyzed. The results indicate that the developed method for PV arrays with different blocking diodes configurations under various operating conditions has high fault diagnosis accuracy and reliability