Actual Photovoltaic Modules Research Articles

A simple but accurate two-state model for nowcasting PV power

A two-state model for short-term forecasting of PV plants output power (further referred as PV2-state) is proposed. PV2-state connects in an inventive way an empirical estimator for clear-sky photovoltaic (PV) power output with a statistical predictor for the sunshine number, a binary indicator stating whether the Sun shines or not. PV2-state shows remarkable features: (1) accessibility (only data series resulted from the PV plant monitoring are processed), (2) universality (no physical models for PV plant components are required) and (3) high-performance (due to a continuous adaptation to the actual atmospheric and PV modules conditions). Based on different error metrics, the model performance is investigated from three perspectives: forecast accuracy, forecast precision and the response to the variability in the state-of-the-sky. The study was conducted with high-quality data collected from a fully monitored experimental micro-PV plant developed on the Solar Platform of the West University of Timisoara, Romania. By processing information about the actual performance of the PV plant, PV2-state proves a notable advance in the forecast precision, becoming a robust competitor in the race for high-accuracy in intra-hour forecast of PV power.

Short-Term Forecasting of Energy Production for a Photovoltaic System Using a NARX-CVM Hybrid Model

In this paper, a methodology for short-term forecasting of power generated by a photovoltaic module is reported. The method incorporates a nonlinear autoregressive with exogenous inputs (NARX) fed by the solar radiation and temperature times series, as well as an estimation of power time series obtained by implementing an ideal single diode model. This synthetic time series was validated against an actual photovoltaic module. The NARX model has been implemented in conjunction with the corrective vector multiplier (CVM) technique, which uses solar radiation under clear sky conditions to adjust the forecasting results. In addition, collinearity and the Granger causality tests were used to choose the input variables. The forecasting horizon was 24-h-ahead. The hybrid NARX-CVM model was compared to a nonlinear autoregressive neural network and persistence model using the typic forecasting error measures such as the mean bias error, mean squared error, root mean squared error and forecast skill. The results showed that the forecasting skills of the hybrid model are about 34% against the NAR model and about 42% against the Persistence model. The model was validated by blind forecasting. The results demonstrated evidence of the quality of the conformed forecasting model and the convenience of its implementation and building.

Design and Implementation of a Flexible Photovoltaic Emulator Using a GaN-Based Synchronous Buck Converter.

In order to efficiently facilitate various research works related to power converter design and testing for solar photovoltaic (PV) generation systems, it is a great merit to use advanced power-converter-based and digitally controlled PV emulators in place of actual PV modules to reduce the space, cost, and time to obtain the required scenarios of solar irradiances for various functional tests. This paper presents a flexible PV emulator based on gallium nitride (GaN), a wide-bandgap (WBG) semiconductor, and a based synchronous buck converter and controlled with a digital signal processor (DSP). With the help of GaN-based switching devices, the proposed emulator can accurately mimic the dynamic voltage-current characteristics of any PV module under normal irradiance and partial shading conditions. With the proposed PV emulator, it is possible to closely emulate any PV module characteristic both theoretically, based on manufacturer’s datasheets, and experimentally, based on measured data from practical PV modules. A curve fitting algorithm is used to handle the real-time generation of control signals for the digital controller. Both simulation with computer software and implementation on 1 kW GaN-based experimental hardware using Texas Instruments DSP as the controller have been carried out. Results show that the proposed emulator achieves efficiency as high as 99.05% and exhibits multifaceted application features in tracking various PV voltage and current parameters, demonstrating the feasibility and excellent performance of the proposed PV emulator.

Differential evolution with dynamic control factors for parameter estimation of photovoltaic models

Parameter estimation of photovoltaic (PV) models is an essential component in the design of a PV system with enhanced performance. Although many reliable solutions have been proposed by various authors in recent years, the estimation of PV model parameters is still an emerging area and remains an important focus. The estimation problem is formulated as a single objective function to be minimized based on the difference between the experimental and estimated current. Although different variants of differential evolution (DE) have been proposed in the past, in the current work, a DE with dynamic control factors (DEDCF) is proposed. DEDCF considers the experimental I–V datasets to estimate the parameters of single- and double-diode-based PV cell models and PV modules. The control factors include mutation and crossover factors, both of which need to undergo dynamic adjustment to arrive at better solutions. The results show the efficiency of DEDCF in comparison with other methods proposed in recent works for estimating the parameters of actual PV cells and modules.

A New Application of Chaos Game Optimization Algorithm for Parameters Extraction of Three Diode Photovoltaic Model

The precision of the PV model greatly influences the simulation results to enhance the effectiveness of photovoltaic (PV) energy systems. The PV mathematical model is based on a remarkably nonlinear relationship of its I-V characteristic. The data sheets of overall PV cells do not supply complete information of its parameters. This leads to a nonlinear mathematical model of PV with numerous unknown parameters. Consequently, in this paper, a new application of an appropriate optimization algorithm called Chaos Game Optimization algorithm (CGO) is proposed for estimating the unknown parameters of the three-diode (TD) PV model. The simulation results are carried out for PV real cells and PV module which with varying the temperature and irradiation. The proposed model of the PV module is evaluated by matching its results with the actual PV modules experimental results. To confirm the performance of the CGO algorithm in extracting the parameters of the PV model, its results are compared with the most present and robust techniques results in the literature. The results show that the CGO algorithm attains the least Root Mean Square Error (RMSE), the mean and standard deviation as the best solution. In addition, CGO provides the smallest implementation time compared with the other investigated algorithms.

Modeling and simulation of solar PV modules based inverter in MATLAB-SIMULINK for domestic cooking

In recent days, the power extraction from renewable energy resources has become a major research area. To generate electricity from the solar energy, the Photovoltaic (PV) modules are connected either in series or parallel or series–parallel combination. The characteristics of PV modules with different environment factors are considered for simulating model in MATLAB software. In this paper, the PV modules with Maximum Power Point Tracking (MPPT) algorithm for extracting maximum power is simulated using MATLAB Simulink software. The algorithm is used to extract maximum power from the solar energy with various dynamic conditions especially irradiance and temperature levels of the PV panels. For the changing values of temperature and irradiance the voltage, current and output power of various PV modules are determined. All the results from MATLAB Simulink are verified with the theoretical calculations. The proposed model helps in understanding and analysing the various PV modules and their characteristics with changing environment conditions. In this paper 500 W of Photo Voltaic modules with AC/DC loads for domestic cooking is designed. The simulation results are compared and analysed for different types of PV modules. The results obtained from the Simulink model blocks are very similar to the actual PV modules. The simulated results obtained from MATLAB/SIMULINK which are same as reference PV modules, compatible with different types of PV module and its analysis are user-friendly.

Experimental study of flame spread underneath photovoltaic (PV) modules

The horizontal flame spread underneath a photovoltaic (PV) module (or a plate) was studied for various gap heights to understand the fundamental fire dynamics between it and a flat roof construction. In a number of experiments, an opaque black polymethyl methacrylate (PMMA) and a black stainless-steel plate were used as surrogate fuel and module, respectively. Additional experiments were conducted with actual PV modules to analyse the reproducibility of the surrogate results and to study the potential effect of the small fuel contribution from the PV modules. PMMA samples (70 cm long) of varying widths (20 cm, 30 cm and 40 cm) were ignited at one end, and flame spread rate, mass loss, radiative heat flux and temperatures were recorded. For the 30 cm wide samples, a reduction in gap height from 20 cm to 17 cm resulted in a significant increase in the flame spread rate (from 0.37 mm/s to 2.41 mm/s). A similar critical gap height (transition into a much faster flame spread rate) was identified for all widths. Thus, introducing gap height requirements into PV installation standards could offer an inexpensive and elegant design that could reduce the fire risk associated with PV modules on flat roofs significantly.

Advanced PV Performance Modelling Based on Different Levels of Irradiance Data Accuracy

In photovoltaic (PV) systems, energy yield is one of the essential pieces of information to the stakeholders (grid operators, maintenance operators, financial units, etc.). The amount of energy produced by a photovoltaic system in a specific time period depends on the weather conditions, including snow and dust, the actual PV modules’ and inverters’ efficiency and balance-of-system losses. The energy yield can be estimated by using empirical models with accurate input data. However, most of the PV systems do not include on-site high-class measurement devices for irradiance and other weather conditions. For this reason, the use of reanalysis-based or satellite-based data is currently of significant interest in the PV community and combining the data with decomposition and transposition irradiance models, the actual Plane-of-Array operating conditions can be determined. In this paper, we are proposing an efficient and accurate approach for PV output energy modelling by combining a new data filtering procedure and fast machine learning algorithm Light Gradient Boosting Machine (LightGBM). The applicability of the procedure is presented on three levels of irradiance data accuracy (low, medium, and high) depending on the source or modelling used. A new filtering algorithm is proposed to exclude erroneous data due to system failures or unreal weather conditions (i.e., shading, partial snow coverage, reflections, soiling deposition, etc.). The cleaned data is then used to train three empirical models and three machine learning approaches, where we emphasize the advantages of the LightGBM. The experiments are carried out on a 17 kW roof-top PV system installed in Ljubljana, Slovenia, in a temperate climate zone.

Real-time emulation of photovoltaic energy using adaptive state feedback control

The current paper presents an experimental validation of photovoltaic (PV) source emulation using a real-time dSpace controller. The PV source emulator is a nonlinear power electronic device used to provide in real-time the electrical behavior of PV panels by tracking the PV current–voltage (I–V) characteristic. It aims to deliver nearly the same current and voltage as an actual PV module without depending on atmospheric conditions. This innovative tool is able to aid researchers in PV systems to perform their tests and measurements at the desired weather parameters without time, space and cost constraints. The proposed PV emulator is based on a simple and efficient adaptive state feedback control that ensures good accuracy and high dynamic response compared to the existing commercial PV emulators based on the programmable power supply, which improves significantly the static and dynamic performances of existing PV emulators.

Maximum Power Point Tracking of Photovoltaic System Based on Reinforcement Learning.

The maximum power point tracking (MPPT) technique is often used in photovoltaic (PV) systems to extract the maximum power in various environmental conditions. The perturbation and observation (P&O) method is one of the most well-known MPPT methods; however, it may face problems of large oscillations around maximum power point (MPP) or low-tracking efficiency. In this paper, two reinforcement learning-based maximum power point tracking (RL MPPT) methods are proposed by the use of the Q-learning algorithm. One constructs the Q-table and the other adopts the Q-network. These two proposed methods do not require the information of an actual PV module in advance and can track the MPP through offline training in two phases, the learning phase and the tracking phase. From the experimental results, both the reinforcement learning-based Q-table maximum power point tracking (RL-QT MPPT) and the reinforcement learning-based Q-network maximum power point tracking (RL-QN MPPT) methods have smaller ripples and faster tracking speeds when compared with the P&O method. In addition, for these two proposed methods, the RL-QT MPPT method performs with smaller oscillation and the RL-QN MPPT method achieves higher average power.

An Improved Beta Method With Autoscaling Factor for Photovoltaic System

Maximum power point tracking (MPPT) is essential to improve the energy yield of solar energy systems. However, conventional MPPT algorithms show obvious problems such as the conflict of the steady-state oscillations and dynamic speed, and the clash of high computational burden and accuracy. Originated from the beta method, which shows the advantages of fast tracking speed in the transient stage, small oscillations in the steady-state, and medium complexity of implementation, this paper proposed an improved beta method to further improve the overall performance, especially for practical applications. Instead of manually tuning key parameters such as the range of $\beta$ parameter and scaling factor $N$ for different operating conditions, an autoscaling factor is used, which make the method easier in practical implementation and suitable for wider conditions. The meteorological data of two distinct locations are used to verify that the $\beta$ parameters derived from photovoltaic (PV) modules are valid for one whole year under different environmental conditions. A PV system with the proposed MPPT method was built in MATLAB/Simulink, and different indices such as the rise time, the setting time, and the tracking energy loss are used to evaluate the performance of various MPPT algorithms. Finally, two experimental tests were carried out, including the indoor test with solar array emulator and the outdoor test with an actual PV module, respectively, to show the effectiveness of the proposed MPPT algorithm.

Estimation of the degradation rate of multi-crystalline silicon photovoltaic module under thermal cycling stress

The overall power of an outdoor-exposed photovoltaic (PV) module decreases as a result of thermal cycling (TC) stress, due to the formation of cracks between the solder and metal. In this study, the thermal fatigue life of solder (62Sn36Pb2Ag) interconnection between copper and silver metallization in PV module was studied. This paper describes in detail the degradation rate (RD) prediction model of solder interconnection for crystalline PV module. The RD prediction model is developed which based on published constitutive equations for solder and TC test results on actual PV module. The finite element method was employed to study the creep strain energy density of solder interconnections in TC conditions. Three types of accelerated tests were conducted to determine the prediction model parameters. RD in benchmark condition is predicted and compared with those of TC conditions.

Photovoltaic Module Modeling using Simulink/Matlab

This paper describes a method of modeling and simulation photovoltaic (PV) module that implemented in Simulink/Matlab. It is necessary to define a circuit-based simulation model for a PV cell in order to allow the interaction with a power converter. Characteristics of PV cells that are affected by irradiation and temperature are modeled by a circuit model. A simplified PV equivalent circuit with a diode equivalent is employed as model. The simulation results are compared with difference types of PV module datasheets. Its results indicated that the created simulation blocks in Simulink/matlab are similar to actual PV modules, compatible to different types of PV module and user-friendly.