Analysis on solar PV emulators: A review

Ram, J. Prasanth; Manghani, Himanshu; Pillai, Dhanup S.; Babu, T. Narendiranath; Miyatake, Masafumi; Rajasekar, N.

doi:10.1016/j.rser.2017.07.039

Cited by 136 publications

(59 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A PV emulator via curve fitting employing a current regulator was presented in Reference [23]. In the literature, some PV emulators are classified based on their power range [6][7][8]. Others are categorized by PV model representation [9,10], given that PV cell and its modeling are the most important key parts of any PV emulation structures.…”

Section: Introductionmentioning

confidence: 99%

Design of a Low-Cost PV Emulator Applied for PVECS

2019

View full text Add to dashboard Cite

Applied tests on a real photovoltaic panel for a consolidated analysis require complex experiment setup and permanent availability of climatic conditions. This method is ineffective and can damage the PV system. As a result, PV emulators are highly requested in solar energy conversion and generation research, which rests essentially on a maximum power point tracking control algorithm (MPPT) and an adapting power stage as the DC-DC converter and PV inverter. The PV emulator guarantees a controllable light source environment to act as a real PV system in the laboratory. This paper deals with the study and development of an experimental PV emulator based on logarithmic approximation of the ideal single diode model (ISDM), which is implemented using analog electronic components. Mainly, the PV model, the controller, and the power stages, forming the PV emulator, are described. This simple, low-cost, and efficient device is considered as a nonlinear power supply template replacing the real PV system for any operating point irrespective of the environmental condition changes. The emulated current-voltage and power-voltage curves are validated via resistive load and batteries. Then, the performance of the proposed PV emulator is evaluated by its ability to recharge properly two 12V 7 Ah batteries.

show abstract

Section: Introductionmentioning

confidence: 99%

Design of a Low-Cost PV Emulator Applied for PVECS

2019

View full text Add to dashboard Cite

show abstract

“…Several PV emulators have been operated by researchers [17][18][19][20][21][22] and could be classified according to the controller type such as static, semi-dynamic, or dynamic one. [23][24][25] To improve the dynamic response of the PV emulator, several control strategies, which are simple and provide more output stability, are adopted, namely, the direct referencing method, 26 the hybrid-mode controlled method, 27 and the resistance comparison method.…”

Section: Introductionmentioning

confidence: 99%

Photovoltaic emulator based on PV simulator RT implementation using XSG tools for an FPGA control: Theory and experimentation

Moussa

Khedher

2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary This paper presents an efficient field‐programmable gate array (FPGA) implementation method for a photovoltaic (PV) simulator based on two‐diode model applied to control a PV emulator. The hardware set up allows to repeat the real PV system behavior in the laboratory regardless of external climatic conditions change. The proposed approach has been designed through Xilinx System Generator (XSG) tools and MATLAB/Simulink environment and verified across cosimulation method. A real‐time test bench is built based on the implemented PV simulator with a closed loop control using proportional integral (PI) regulator, a direct current/direct current (DC/DC) buck converter and a resistive load. Experimental results are in agreement compared with simulation ones and prove the high accuracy and efficiency of the proposed implementation method for allowing a good emulation of PV characteristics. From some experiments, the development of PV emulator constitutes an important challenge task in the PV energy field.

show abstract

“…According to the report of the International Energy Agency (IEA), the installed number of photovoltaic (PV) has been growing at a rapid speed in recent years [1]. As basic energy collection component of power generation system, the accurate and reliable modeling of PV module is quite significant in design, optimized simulation, operation and evaluation of photovoltaic power generation system [2][3][4]. The accurate results of model output could be used to get system information like maximum power point tracking (MPPT), power prediction [5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Prediction of I–V Characteristic Curve for Photovoltaic Modules Based on Convolutional Neural Network

Jia

et al. 2020

Sensors

View full text Add to dashboard Cite

Photovoltaic (PV) modules are exposed to the outside, which is affected by radiation, the temperature of the PV module back-surface, relative humidity, atmospheric pressure and other factors, which makes it difficult to test and analyze the performance of photovoltaic modules. Traditionally, the equivalent circuit method is used to analyze the performance of PV modules, but there are large errors. In this paper-based on machine learning methods and large amounts of photovoltaic test data-convolutional neural network (CNN) and multilayer perceptron (MLP) neural network models are established to predict the I-V curve of photovoltaic modules. Furthermore, the accuracy and the fitting degree of these methods for current-voltage (I-V) curve prediction are compared in detail. The results show that the prediction accuracy of the CNN and MLP neural network model is significantly better than that of the traditional equivalent circuit models. Compared with MLP models, the CNN model has better accuracy and fitting degree. In addition, the error distribution concentration of CNN has better robustness and the pre-test curve is smoother and has better nonlinear segment fitting effects. Thus, the CNN is superior to MLP model and the traditional equivalent circuit model in complex climate conditions. CNN is a high-confidence method to predict the performance of PV modules. from the measured current-voltage (I-V) characteristic curves is important for evaluation, modeling and diagnosis of the actual operating state of in situ PV arrays [14]. Nowadays, manufacturers of photovoltaic modules provide standard reporting condition (SRC) or standard test condition (STC) ratings of PV modules. The main test conditions of PV modules are mainly under laboratory environment, which include irradiation intensity of 1000 W/m 2 , spectral distribution in accordance with the AM1.5 spectrum and temperature of 25 ± 1 • C for the PV module. However, in the practical engineering environment (outdoor weather conditions), these conditions rarely appear at the same time. The adequacy and applicability of PV modules under STC is still a controversial issue [15]. Actually, the PV modules based on SRC are still unreasonable for the real-world weather conditions. Therefore, performance test of PV module based on STC condition needs to be improved in the outdoor weather conditions [16]. Although the outdoor characteristics of PV modules can be predicted by algebraic or numerical methods, these methods used in photovoltaic system engineering ignore some second-order effects, like wind speed, shunt resistance, parasitic capacitance, spectral effect and non-linearity under low illumination, thus the prediction error of these methods is large [17]. Based on this, many photovoltaic modeling methods have been proposed in recent years. Generally, these methods can be divided into two types: the white box model based on equivalent circuit and the black box model based on regression [18][19][20]. The equivalent circuit method is used for the prediction of photovolta...

show abstract

Analysis on solar PV emulators: A review

Cited by 136 publications

References 56 publications

Design of a Low-Cost PV Emulator Applied for PVECS

Design of a Low-Cost PV Emulator Applied for PVECS

Photovoltaic emulator based on PV simulator RT implementation using XSG tools for an FPGA control: Theory and experimentation

Prediction of I–V Characteristic Curve for Photovoltaic Modules Based on Convolutional Neural Network

Contact Info

Product

Resources

About