Boost Chopper Behaviors in Solar Photovoltaic System

The impact of solar irradiance changes on the winter polar stratosphere is investigated using a general circulation model in which the equatorial Quasi‐Biennial Oscillation (QBO) is internally generated and self‐sustaining. The model results compare favorably with observations, supporting previous findings that the equatorial zonal wind modulates the polar stratospheric response to solar irradiance changes. In the QBO easterly phase, Northern Hemisphere sudden stratospheric warmings are found to be more prevalent under solar minimum conditions than under solar maximum conditions. However, in the QBO westerly phase the reverse is true. The possible solar‐modulation of the QBO period is also investigated. Although small changes are evident in the same sense as those observed, i.e. a lengthening of the period during solar minimum conditions, longer simulations would be required to verify the statistical significance of this result.

Accurate prediction of solar irradiance is beneficial in reducing energy waste associated with photovoltaic power plants, preventing system damage caused by the severe fluctuation of solar irradiance, and stationarizing the power output integration between different power grids. Considering the randomness and multiple dimension of weather data, a hybrid deep learning model that combines a gated recurrent unit (GRU) neural network and an attention mechanism is proposed forecasting the solar irradiance changes in four different seasons. In the first step, the Inception neural network and ResNet are designed to extract features from the original dataset. Secondly, the extracted features are inputted into the recurrent neural network (RNN) network for model training. Experimental results show that the proposed hybrid deep learning model accurately predicts solar irradiance changes in a short-term manner. In addition, the forecasting performance of the model is better than traditional deep learning models (such as long short term memory and GRU).

K-Nearest Neighbors hybrid method for maximum power point tracking under partial shading for photovoltaic power systems

Photovoltaic (PV) systems provide a number of challenges, one of the most critical of which is determining how to extract the maximum amount of usable power from the PV system even when the system is running in conditions of fast change in irradiance. This study aims to demonstrate a novel hybrid approach to maximum power point tracking (MPPT), which is built on the grey wolf optimization algorithm (GWO) and modified perturb and observe (MP&O) techniques. The second goal of this method is to provide a robust MPPT method with minimum losses in the MPP point of the PV module. This technique is based on the connection of a single KC200GT PV module. The KC200GT PV module is connected to the DC bus via DC/DC boost converter to raise the voltage and implement the MPPT algorithm. After that, the PV system was integrated to supply a new multilevel inverter (MLI) with a reduced number of switches. Additionally, the MATLAB/Simulink environment was used to demonstrate and evaluate the efficacy of the proposed MPPT approach. Complex fast changes in solar irradiation are applied to the module to verify the feasibility and efficacy of the hybrid method by comparing its performance to that of the P&O, and MP&O algorithms. The obtained results demonstrated that the hybrid GWO-MP&O MPPT method achieved an excellent level of efficacy in terms of MPP accuracy, convergence speed (0.05 sec), and overall tracking efficiency (99.7%) compared to other approaches. Keywords: Grey Wolf Optimizer, maximum power point, boost converter, photovoltaic system, multilevel inverter, perturb and observe. https://doi.org/10.55463/issn.1674-2974.50.2.7

The tracking of power in solar PV systems depends on controlling the voltage or current to the optimum level irrespective of changes in solar irradiation. There are two aspects of maximum power point tracking (MPPT), first is to control the voltage or current and second is to find the peak power with an algorithm. This work deals with both the aspects, using current control which provides fast control response and a momentum-based MPPT algorithm for improved peak tracking. Since current programmed control gives a simple implementation and fast response of converters, it would seem obvious to link it with current referenced MPPT but this method is prone to fail when irradiation drops suddenly, leading to control loss. Voltage oriented MPPT control (which has logarithmic dependance on irradiation), with an inner current control loop is used, as it maintains control even with step changes in irradiation. The second part of this work discusses the perturb and observe (hill climbing) algorithm for finding the power peak, which is known for its simple and effective implementation. A simple modification of this algorithm by adding momentum to the hill climbing is shown to improve the speed of tracking while maintaining the oscillations around maximum power point to acceptable levels.

This paper presents an improved reinitialisation condition for time invariant maximum power point tracking (MPPT) methods used in photovoltaic (PV) systems experiencing partial shading conditions (PSC). Time invariant (MPPT) methods, such as Particle Swarm Optimisation (PSO), overcome the limitations of existing MPPT by tracking the global maximum power point (GMPP) of a PV system operating under PSC. However, due to the time invariant structure of these MPPT methods, they also require a reinitialisation condition to be defined for when a change in irradiance or temperature occurs. Testing was performed using simulations of a model built in Matlab/ Simulink, where the performance of existing and developed conditions was evaluated using test cases with changes in solar irradiance. Limitations of existing conditions were identified and a more robust reinitialisation condition developed. The developed reinitialisation condition used sentry particles to monitor the PV voltage range for changes in the measured power of any sentry. The developed condition had a 96 % rate of successful detection, as compared to as low as 68 % successful detection for existing methods, demonstrating improved performance and robustness.

Changes in solar irradiance cause variations in photovoltaic (PV) power generation and thus affect customer voltage. Load tap changers in substations are used to mitigate voltage variations, but they are relatively slow and cannot regulate the voltage adequately. Furthermore, abrupt voltage changes induced by PVs could greatly cause mechanical wear and reduce their lifetime. This letter develops a novel voltage smoothing control algorithm for distributed energy storage systems to reduce the impact of PV generation on voltage quality. Different from other works, the proposed control can improve the performance by having both active and reactive power control and monitoring voltage directly. The technique's performance is verified through simulation and modeling of the IEEE 4-bus test system. The proposed technique can effectively smoothen the voltage and reduce the number of tap operations by approximately 50%.

Photovoltaic (PV) power has been successfully used for over five decades. Whether in dc or ac form, photovoltaic cells provide power for systems in many applications on earth and space. Its principles of operation are therefore well understood, and circuit equivalents have been developed that accurately model the nonlinear relationship between the current and voltage of a photovoltaic cell. With the improved efficiencies of power electronics converters, it is now possible to operate photovoltaic system about its maximum power point (MPP) in order to improve the overall system efficiency. Hitherto, this problem has been tackled using tracking (MPPT) algorithms that iteratively find the point of maximum power and respond to changes in solar irradiance accordingly. A mathematical manipulation that uses the mean value theorem is presented here that provides the analytic solution of a point in a close neighborhood of the MPP. It is thoroughly proved that this point is enclosed in a ball of small radius that also contains the MPP and therefore can practically be considered as the MPP. Since the solution is analytic, no iterative schemes are necessary, and only a periodic measurement is required to adjust to changes in solar irradiance. A circuit is implemented that shows the validity of the theory and the accuracy of the solution.

Nowadays, Photovoltaic (PV) energy sources are responsible for more than 720TWh of the global energy consumed. Therefore, among all the arising research topics regarding PV sources, Maximum Power Point tracking (MPPT) algorithms are widely studied, since they have critical importance in order to properly track and acquire the Maximum Power Point (MPP) of energy that can be harvested from the PV source; where, metaheuristic optimization algorithms have been adapted as MPPT solutions, showing an improved settling time with steady-state oscillations reduction. Yet, many of the Metaheuristic-based MPPTs have issues against dynamically variable MPP through time, since they cannot work properly without a reset signal upon temperature and irradiance parametric changes, due the fact that those algorithms converge on a global solution after some iterations and need to know when to start looking for the MPP again in order to avoid getting stuck in old MPP points. Hence, this work shows the implementation of Artificial Neural Networks (ANN) for pattern recognition through the power data acquisition, which enables an efficient solution that allows detecting changes in solar irradiation, which leads to a reliable reinitialization signal through the detection of solar irradiation through the ANN; where, results show over 99% of accuracy from the change in solar irradiation detection. Moreover, the ANN was trained using the metaheuristic Earthquake Algorithm (EA), which was again validated as a training method for ANN. In addition, this work enables other users to suite the EA algorithm to other applications and the implementation of ANN into the Simulink environments; both achievements, through the presentation and exploration of a complete MATLAB code example for the implementation of the EA as ANN training method, and the complete Simulink testbed design with a full description of the ANN implementation for the Simulink environment.KeywordsArtificial neural networksMetaheuristicsPhotovoltaic systems

The paper aims at the incompatibility between the speed and stability of the traditional MPPT algorithm and the imprecise search of the fuzzy control algorithm. An improved photovoltaic adaptive fuzzy control MPPT algorithm is proposed in this thesis. The solar irradiance changes dramatically and hence four kinds of fuzzy control algorithms with different input are modeled and simulated. The results indicate that the proposed fuzzy control algorithm using slope and slope change rate of P-U curve as input is the best. On this basis, dP/dU and duty cycle D(n-1) at n-1 moment are used as input to improve the tracking speed and optimal range. At the same time using shrinkage factor 1/I*|dP/dU| real-time adjustment of D(n-1) further shortens the optimal time of the algorithm. The algorithm is simulated and applied in a block. Simulation results show that the proposed algorithm is superior to the fuzzy control algorithm in steady-state oscillation rate, tracking speed and efficiency, and the algorithm is simple and easy to implement.

The world is looking for utilization of renewable energy sources to reduce global warming as well as the consumption of fossil fuels. In this scenario, solar and wind energy are widely used at many places worldwide. However, both solar irradiance and wind speed are depending on nature. Hence, an energy storage device must be required to operate at their best utilization level by converting them to electricity. One of the best energy storage devices for medium power range is the battery. However, batteries require high maintenance and suffering from self-discharge as well as storage capacity will be decreased day by day. Hence, storing hydrogen can be an economically feasible solution instead of using batteries for high power range. Usually an aqua electrolyzer can easily convert water to hydrogen and oxygen through electricity. However, due to slow dynamics of heat transfer from electricity, the generation of hydrogen cannot meet the fast response like electrical devices. Therefore, a novel controlling technique is required to increase the production quality of hydrogen during random changes in both solar irradiance and wind speed. In order to achieve the best utilization, both photovoltaic panels and wind turbines are operated at their maximum power point levels. In this research boost converters are used to operate as maximum power point tracking devices. The whale optimization technique is integrated to respective controllers of all the converters to achieve stable production of hydrogen during rapid changes in irradiance and wind speed. The Whale Optimization Algorithm (WOA) technique is compared with Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) to show the benefits of tracking response of the system on improving production of hydrogen from hybrid renewable energy sources based Microgrid. Hardware—in the—Loop (HIL) is developed to analyze the results with the help of OPAL-RT modules. Along with HIL results, a Real Time Digital Simulator (RTDS) based results are also presented to evaluate the performance of the proposed method.

The attribution of part of global warming to changes in the total solar irradiance (TSI) is an important topic which is not, yet, fully understood. Here, we examine the TSI induced temperature (T) changes on a variety of time scales, from one day to centuries and beyond, using a variety of assumptions. Also considered is the latitude variation of the T-TSI correlations, where it appears that over most of the globe there is a small increase in the sensitivity of temperature to TSI in time. It is found that the mean global sensitivity (alpha)measured in K(Wm-2)-1 varies from about 0.003 for 1 day, via 0.05 for 11-years to about 0.2 for decades to centuries. We conclude that mean global temperature changes related to TSI are not significant from 1975 onwards. Before 1975, when anthropogenic gases were less important, many of the temperature changes can be attributed to TSI variations. Over much longer periods of time, from Kyear to Myear, the TSI changes are more efficient still, the sensitivity alpha increasing ro about 0.5. Since 1975 the changes in mean global temperature are not due to TSI changes, but rather to the increasing atmospheric CO2 content.

A two-dimensional global climate model is used to assess the climatic changes associated with the new IPCC SRES emissions scenarios and to determine which kind of changes in total solar irradiance and volcanic perturbations could mask the projected anthropogenic global warming associated to the SRES scenarios. Our results suggest that only extremely unlikely changes in total solar irradiance and/or volcanic eruptions would be able to overcome the simulated anthropogenic global warming over the century. Nevertheless, in the critical interval of the next two decades the externally-driven natural climate variability might possibly confuse the debate about temperature trends and impede detection of the anthropogenic climate change signal.

The long-term trends of total surface solar radiation (SSR), surface diffuse radiation, and surface air temperature were analyzed in this study based on updated 48-yr data from 55 observational stations in China, and then the correlation between SSR and the diurnal temperature range (DTR) was studied. The effect of total solar radiation on surface air temperature in China was investigated on the basis of the above analyses. A strong correlation between SSR and DTR was found for the period 1961–2008 in China. The highest correlation and steepest regression line slope occurred in winter, indicating that the solar radiation effect on DTR was the largest in this season. Clouds and water vapor have strong influences on both SSR and DTR, and hence on their relationship. The largest correlations between SSR and DTR occurred in wintertime in northern China, regardless of all-day (including clear days and cloudy days) or clear-day cases. Our results also showed that radiation arriving at the surface in China decreased significantly during 1961–1989 (dimming period), but began to increase during 1990–2008 (brightening period), in agreement with previous global studies. The reduction of total SSR offset partially the greenhouse warming during 1961–1989. However, with the increase of SSR after 1990, this offsetting effect vanished; on the contrary, it even made a contribution to the accelerated warming. Nonetheless, the greenhouse warming still played a controlling role because of the increasing of minimum and mean surface temperatures in the whole study period of 1961–2008. We estimated that the greenhouse gases alone may have caused surface temperatures to rise by 0.31–0.46°C (10 yr)−1 during 1961–2008, which is higher than previously estimated. Analysis of the corresponding changes in total solar radiation, diffuse radiation, and total cloud cover indicated that the dimming and brightening phenomena in China were likely attributable to increases in absorptive and scattering aerosols in the atmosphere, respectively.

This study investigates the possibility of changes in daily scale solar radiation and precipitation variability. Coefficients of variation (CVs) were computed for the daily downward surface solar radiation product from the International Satellite Cloud Climatology Project and the daily precipitation product from the Global Precipitation Climatology Project. Regression analysis was used to identify trends in CVs. Statistically significant changes in solar radiation variability were found for 35% of the globe, and particularly large increases were found for tropical Africa and the Maritime Continent. These increases in solar radiation variability were correlated with increases in precipitation variability and increases in deep convective cloud amount. The changes in high-frequency climate variability identified here have consequences for any process depending nonlinearly on climate, including solar energy production and terrestrial ecosystem photosynthesis. To assess these consequences, additional work is needed to understand how high-frequency climate variability will change in the coming decades.