Cell modelling and model parameters estimation techniques for photovoltaic simulator application: A review

The contribution of solar photovoltaics (PV׳s) in generation of electric power is continually increasing. PV cells are commonly modelled as circuits. Finding 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 parameters of solar PV cells is referred to as “PV cell model parameter estimation problem,” and is highly attracted by researchers. In this paper, the existing research works on PV cell model parameter estimation problem are classified into three categories and the research works of those categories are reviewed. Based on the conducted review, some recommendations for future research are provided.

Parameter estimation of solar photovoltaic (PV) cells: A review

Cooling of PV panels is a critical issue in the design and operation of concentrated photovoltaic (CPV) technology. Due to high cell temperature and non-uniform temperature distribution, current mismatching problem and hot spot occurs on the cell resulting in either reduction of efficiency or permanent structural damage due to thermal stresses. Temperature non-uniformity on the surface of PV panel has a major impact on the performance of CPV systems and directly increases cell temperature and series resistance. This review paper highlights the importance of uniform PV cooling by exploring the possible causes and effects of non-uniformity. Cooling techniques with low average cell temperature and uniform temperature distribution are analyzed. Economic and environmental impact on the importance of cooling of PV systems are discussed and an experimental case study is presented for comparison between uniform and non-uniform cooling methods. Immersion cooling is a promising solution for uniform cooling and has been reported to reduce the cell temperature to 20–45 °C for CPV systems. Heat pipes reduced the temperature down to 32 °C with the best case temperature non-uniformity of 3 °C. Passive cooling by heat sinks was found to reduce the cell temperature as low as 37 °C for high concentrations but with an expense of large heat sink area. Active cooling by microchannels, impingement cooling and hybrid microchannel-impingement cooling were found to be most effective in dissipating high heat flux from PV surface. Cell temperature was reported to decrease to 30 °C for 200× CPV using impingement cooling. For hybrid cooling, deviation of 0.46 °C surface temperature was obtained. Using PCM materials temperature of panel was controlled within 28–65 °C whereas optimization of heat exchanger designs also showed low and uniform temperature across surface. The impact of non-uniformity was found to be significant for all PV systems however the effect is more pronounced in CPV systems.

Uniform cooling of photovoltaic panels: A review

This paper proposes a novel model-free solution algorithm, the natural cubic-spline-guided Jaya algorithm (S-Jaya), for efficiently solving the maximum power point tracking (MPPT) problem of PV systems under partial shading conditions. A photovoltaic (PV) system which controls the power generation with its operating voltage is considered. As the same as the generic Jaya algorithm, the S-Jaya is free of algorithm-specific parameters. A natural cubic-spline-based prediction model is incorporated into the iterative search process to guide the update of candidate solutions (operating voltage settings) in the S-Jaya and such extension is capable of improving the tracking performance. Simulation studies and experiments are conducted to validate the effectiveness of the proposed S-Jaya algorithm for better addressing PV MPPT problems considering a variety of partial-shading conditions. The performance of the proposed algorithm is benchmarked against the generic Jaya and the particle swarm optimization, which has been widely considered in the model-free MPPT, to demonstrate its advantages. Results of simulation studies and experiments demonstrate that the S-Jaya algorithm converges faster and provides a higher overall tracking efficiency.

A Prediction Model-Guided Jaya Algorithm for the PV System Maximum Power Point Tracking

One of the most important issues in the operation of a photovoltaic (PV) system is extracting maximum power from the PV array, especially in partial shading condition (PSC). Under PSC, P–V characteristic of PV arrays will have multiple peak points, only one of which is global maximum. Conventional maximum power point tracking (MPPT) methods are not able to extract maximum power in this condition. In this paper, a novel two-stage MPPT method is presented to overcome this drawback. In the first stage, a method is proposed to determine the occurrence of PSC, and in the second stage, using a new algorithm that is based on ramp change of the duty cycle and continuous sampling from the P–V characteristic of the array, global maximum power point (MPP) of array is reached. Perturb and observe algorithm is then re-activated to trace small changes of the new MPP. Open-loop operation of the proposed method makes its implementation cheap and simple. The method is robust in the face of changing environmental conditions and array characteristics, and has minimum negative impact on the connected power system. Simulations in Matlab/Simulink and experimental results validate the performance of the proposed methods.

Partial Shading Detection and Smooth Maximum Power Point Tracking of PV Arrays Under PSC

Intelligent fault diagnosis of photovoltaic array based on variable predictive models and I–V curves

The performance of a photovoltaic (PV) array is affected by array configuration, insolation, and temperature. It is important to understand the relation between these effects and the output power of PV array. This paper presents a Matlab Simulink based PV module model that includes a controlled current source and an S-Function Builder. The modeling scheme in S-Function Builder is deduced by some predigested function. The model is practically validated using different array configuration (PV module in series and in parallel) with nonuniform insolation. The simulation results show the I-V and P-V characteristics under nonuniform insolation conditions. It indicates that output power of PV array get more complex with multiple peaks. The presented model will help to validate new MPPT strategies, and will help to design the most optimum configuration PV array with the maximum power.

Matlab-Simulink Based Modeling to Study the Influence of Nonuniform Insolation Photovoltaic Array

Fault diagnosis plays a crucial role in the operation and maintenance (O&M) of photovoltaic (PV) arrays, and reasonable feature extraction is a prerequisite for effective fault diagnosis. In this paper, a feature extraction and fault diagnosis method based on current–voltage (I–V) conversion is proposed. First, the PV array modeling method based on the double diode model (DDM) and the reverse bias model (RBM) is proposed. This modeling method can simulate the I–V curves under different states and provide data foundation for feature extraction and fault diagnosis. Next, three procedures for correcting I–V curves and three feature enhancement methods are compared to select the optimal program for I–V conversion. The converted feature matrix is dimensionalized using T-distributed stochastic neighbor embedding (T-SNE) to achieve feature extraction. Finally, ten classification models for fault diagnosis are adopted to verify the effectiveness of the proposed feature extraction method. Experimental results demonstrate that the proposed methods perform well on simulation data and provide satisfactory fault diagnosis results for the measured I–V curves. Among the classification models tested, the variable prediction model (VPM) shows the optimal comprehensive performance, with the computational time of 0.17 s and the accuracy of 99.4%. 

Feature extraction and fault diagnosis of photovoltaic array based on current–voltage conversion

Health status evaluation of photovoltaic array based on deep belief network and Hausdorff distance

Research on real-time identification method of model parameters for the photovoltaic array

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