Photovoltaic Power Generation Research Articles

Investigating safety precautions for linking photovoltaic and wind power generation to the grid

The grid integration of renewable energy sources such as solar and wind power has received significant research attention, however, there remains a vast scope for exploration and improvement. Specifically, the discourse around relay protection and safety automatic devices in these power generation methods has been somewhat limited during the grid connection process. This article endeavors to investigate the technical obstacles encountered by photovoltaic and wind power grid protection mechanisms and crucial equipment, amidst the steady growth of renewable energys share in the power grid. This exploration is considered advantageous. The aim is to study and summarize existing research on protective measures and automation equipment for photovoltaic and wind power generation during grid connection, to promote technological innovation and development in this field. This paper critically assesses studies conducted so far on the topic of relay protection and automation hardware used in grid-connected solar and wind power generation, underscoring the significance of different protection techniques and their pivotal roles in ensuring system safety during grid connectivity. It then dives into the present obstacles and constraints that these technologies contend with. In conclusion, it suggests a few potential areas and anticipations for prospective research.

Experimental study on effect factors of wind-induced response of flexible photovoltaic support structure

In recent years, the proportion of flexible photovoltaic (PV) support structures (FPSS) in PV power generation has gradually increased, and the wind-induced response of FPSS has gradually been noticed. In this study, the wind-induced responses of a FPSS with a single row and a single span were investigated by aeroelastic model wind tunnel tests. The effects of structural parameters of FPSS were systematically analyzed on 17 test cases (including inclinations, module spacings, initial tensions, spans and module sizes), and the variation of flutter critical wind velocities with structural parameters was obtained. The experimental results show that the most unfavorable wind direction is 180°. The static wind-induced response is mainly affected by the module inclination angle, module spacing and the structure span. The vortex-induced vibration (VIV) of the test models occurs in some specific test cases at low wind velocity, and it can be easily suppressed by changing the structural parameters. The flutter occurs at both directions of 0° and 180° when the wind velocity increases to a critical value. With the increase of module inclination angle, the flutter critical wind velocity of FPSS decreases first and then increases, and the most unfavorable module inclination is 25°. It is beneficial for suppressing the structure flutter by increasing the cable initial tension and the module spacing, and decreasing the span of the FPSS. Changing the module size will affect the aerodynamic characteristics and structural frequency of the structure, thereby affecting the wind-induced vibration of the structure.

Analysis of partial load loss of the PCS and internal storage battery loss in residential PV power generation and storage battery systems

AbstractThis study aims to quantify the amount of loss due to partial load of power conditioning system (PCS) and internal loss of storage battery in residential photovoltaic (PV) power generation and storage battery system by using home energy management system (HEMS) data. After identifying the configuration of the PV power generation and storage battery system, HEMS data on PV power generation, household demand, storage battery charging, storage battery discharging, purchased power and sold power were extracted and analyzed according to the research purpose. As a result, the loss due to partial load of the PCS and the average internal loss of the storage battery were quantified. In addition, it was confirmed that a more realistic system simulation could be performed by considering the impact of the partial load of the PCS and the average internal loss of the storage battery.

Comparative Numerical Energy and Performance Analysis of Solar Photovoltaic and Solar Thermal Power Generation

This study analyses fixed and tracking solar photovoltaics and, solar thermal power. Performance in different locations was analysed for each configuration and technology using simulations based on a Typical Meteorological Year data. The study analysed a 25MWp solar photovoltaic and solar thermal power plants in each of the eleven selected locations in Zimbabwe. The performance of the solar photovoltaic and solar thermal power plants under different meteorological variables were assessed for all the selected locations. It was shown that different configurations together with different technologies have different conversion efficiencies. A high solar thermal conversion efficiency was found to be 18.718% in Gweru while it was 15.502% for solar photovoltaics in Mutare. The study also showed that highest insolation and clearness index values were found in Gweru. The average energy generated by the fixed photovoltaic collectors, tracking photovoltaic collectors and the Concentrating Solar Power plant were respectively 47.38GWh, 68.18GWh and 192.86GWh. There was a maximum percentage difference in the LCoE generated of 32.03% between the fixed PV collectors and the Concentrating Solar Power plant and a difference of 4.74% was realised between the tracking photovoltaics and Concentration Solar Power.

Improvement of X-ray Photoelectric Conversion Performance of MAPbI3 Perovskite Crystals by Ionic Liquid Treatment

Although perovskite has great potential in optoelectronic devices, the simultaneous satisfaction of material stability and high performance is still an issue that needs to be solved. Most perovskite optoelectronic devices use quantum dot spin coating or the gas-phase growth of perovskite thin films as the photoelectric conversion layer. Due to stability limitations, these materials often experience a significant decrease in photoelectric conversion efficiency when encountering liquid reagents. The self-assembled growth of hybrid perovskite crystals determines superior lattice ordering and stability. There are three types of ionic liquids—[Emim]BF4, EMIMNTF2, and HMITFSI—that can effectively enhance the X-ray photoelectric conversion performance of hybrid perovskite crystal CH3NH3PbI3 (MAPbI3), and the enhancement in the photocurrent leads to an improvement in the sensitivity of X-ray detectors. We soak the perovskite crystals in an ionic liquid and perform two treatment methods: electrification and dilution with ETOH solution. It is interesting to find that MAPbI3 perovskite single crystal materials choose the same optimized ionic liquid species in X-ray detection and photovoltaic power generation applications, and the effect is quite the opposite. Compared with untreated MAPbI3 crystals, the average photocurrent density of Electrify-HMITFSI MAPbI3 increased by 826.85% under X-ray excitation and the sensitivity of X-ray detectors made from these treated MAPbI3 crystals significantly increased by 72.6%, but the intensity of the PL spectrum decreased to 90% of the untreated intensity.

Machine Learning Models for Regional Photovoltaic Power Generation Forecasting with Limited Plant-Specific Data

Predicting electricity production from renewable energy sources, such as solar photovoltaic installations, is crucial for effective grid management and energy planning in the transition towards a sustainable future. This study proposes machine learning approaches for predicting electricity production from solar photovoltaic installations at a regional level in Italy, not using data on individual installations. Addressing the challenge of diverse data availability between pinpoint meteorological inputs and aggregated power data for entire regions, we propose leveraging meteorological data from the centroid of each Italian province within each region. Particular attention is given to the selection of the best input features, which leads to augmenting the input with 1-hour-lagged meteorological data and previous-hour power data. Several ML approaches were compared and examined, optimizing the hyperparameters through five-fold cross-validation. The hourly predictions encompass a time horizon ranging from 1 to 24 h. Among tested methods, Kernel Ridge Regression and Random Forest Regression emerge as the most effective models for our specific application. We also performed experiments to assess how frequently the models should be retrained and how frequently the hyperparameters should be optimized in order to comprise between accuracy and computational costs. Our results indicate that once trained, the model can provide accurate predictions for extended periods without frequent retraining, highlighting its long-term reliability.

A hybrid machine learning forecasting model for photovoltaic power

The increasing use of photovoltaic (PV) power generation presents a significant opportunity for global energy transformation. However, accurately forecasting PV power remains a challenge. This study proposes a hybrid approach that combines variational mode decomposition (VMD), whale optimization algorithm (WOA), and long short-term memory neural network (LSTM) to forecast photovoltaic (PV) power accurately. The model decomposes the time series of PV power using VMD to address the non-stationary nature of the time series. The VMD parameters are optimized using WOA. Subsequently, and the PV power time series data is then decomposed using the optimized VMD parameters to obtain multiple intrinsic mode functions (IMFs) components and a residual component. These components are then reconstructed with meteorological parameters to obtain the reconstructed IMF components and residuals. Finally, multiple LSTM sub-models are built, with each of them taking the IMF components and residual from the previous reconstruction as inputs. The sub-models are optimized using the WOA method to determine their hyperparameters and then constructed with these optimized hyperparameters. The predicted values of each IMF and residual are output and added for sequence reconstruction to derive the final predicted value of PV power. The model's effectiveness was verified for one-hour-ahead forecasting at the 1.8 MW solar system in Yulara, central Australia. The test results show that the proposed model outperforms other benchmark models, with mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and R-squared value (R2) of 15.247 kW, 19.753 kW, 4.405 % and 0.997, respectively. Compared to the LSTM, MAE, RMSE, and MAPE decreased by 84.942 %, 86.746 %, and 82.611 %, respectively, while R2 increased by 23.438 %. The proposed model has better predictive performance for both stable power changes and large fluctuations, essential for effectively integrating renewable energy sources into the power grid.

Study on Fault Monitoring Technology of Photovoltaic Panel Based on Thermal Infrared and Optical Remote Sensing

Abstract. Rapid access to the operating status of Photovoltaic (PV) panels and troubleshooting can save management and maintenance costs for the development of PV power plants, which is important for PV power plant management and power generation capacity assurance. The use of remote sensing technology to identify the faults of photovoltaic panels has developed rapidly, however, current research usually relies only on a single optical data source to identify and count the area of PV panels in a PV electric field, although there are literature on PV panel fault detection, only the surface fault identification of PV panels is tested, while the internal faults (such as panel bad points or bad lines) cannot be identified because of the limitations of optical remote sensing. In this paper, a photovoltaic panel fault monitoring technology based on multi-source remote sensing is proposed. The optical and thermal infrared hybrid data combined with deep learning technology are used to achieve rapid and accurate fault identification and localization of PV panel arrays. It can not only automatically identify PV panels that are obscured by dust and foreign objects, but also locate PV panels that have bad dots or bad lines, which greatly improves the ability and effectiveness of remote sensing PV panel fault monitoring. The high-resolution unmanned air vehicle (UAV) optical image and thermal infrared image are applied in this experiment. The Mask RCNN algorithm is used to accurately locate and number the photovoltaic panel of the optical image. Then, the fault scene classification model is established for the multi-type fault characteristics of the optical image and thermal infrared image within the panel range, so as to identify five types of faults, such as dust cover, branch cover, bird droppings cover, internal bad points and bad lines of PV panel, which effectively solves the problem that the single optical remote sensing image cannot identify the internal component faults of the photovoltaic panel under normal conditions.

High-Accuracy Photovoltaic Power Prediction under Varying Meteorological Conditions: Enhanced and Improved Beluga Whale Optimization Extreme Learning Machine

Accurate photovoltaic (PV) power prediction plays a crucial role in promoting energy structure transformation and reducing greenhouse gas emissions. This study aims to improve the accuracy of PV power generation prediction. Extreme learning machine (ELM) was used as the core model, and enhanced and improved beluga whale optimization (EIBWO) was proposed to optimize the internal parameters of ELM, thereby improving its prediction accuracy for PV power generation. Firstly, this study introduced the chaotic mapping strategy, sine dynamic adaptive factor, and disturbance strategy to beluga whale optimization, and EIBWO was proposed with high convergence accuracy and strong optimization ability. It was verified through standard testing functions that EIBWO performed better than comparative algorithms. Secondly, EIBWO was used to optimize the internal parameters of ELM and establish a PV power prediction model based on enhanced and improved beluga whale optimization algorithm–optimization extreme learning machine (EIBWO-ELM). Finally, the measured data of the PV output were used for verification, and the results show that the PV power prediction results of EIBWO-ELM were more accurate regardless of whether it was cloudy or sunny. The R2 of EIBWO-ELM exceeded 0.99, highlighting its efficient ability to adapt to PV power generation. The prediction accuracy of EIBWO-ELM is better than that of comparative models. Compared with existing models, EIBWO-ELM significantly improves the predictive reliability and economic benefits of PV power generation. This study not only provides a technological foundation for the optimization of intelligent energy systems but also contributes to the sustainable development of clean energy.

Unsupervised fault detection approach based on depth auto-encoder for photovoltaic power generation system

Fault detection for photovoltaic power generation system is a challenging problem in condition monitoring and troubleshooting, which aims to maintain the safe operation of equipment and improve the benefit of photovoltaic industry. Aiming at the problems of frequent failures of photovoltaic power generation system, large amount of operating data and difficult to obtain fault samples, we propose an unsupervised fault detection approach for photovoltaic power generation system via bidirectional long/short memory deep auto-encoder which combines the auto-encoder in deep learning with the Bi-directional Long Short-Term Memory (BiLSTM). Specifically, We first take the statistical feature enhanced as the input of an auto-encoder based on BiLSTM. Then, we build a simulation model of Grid-connected PV system. Finally, we use the operation results under normal conditions to train the fault detection model to obtain the reconstruction error and determine the fault detection threshold, so as to judge the anomalies of the photovoltaic power generation system. We simulate the shadow occlusion fault and verify the effectiveness of the proposed method, and the fault detection accuracy of 0.95 is achieved. Compare with other models, the results show that it could set up better dependence on multi-dimensional data in time sequences, effectively testing solar panel failures and solving insufficient data labels problems.

Energy yield database management system based on solar photovoltaic cell using internet of things technology

This study investigates the performance of solar cell electric power generation, focusing on data collected from Prince of Songkla University, Surat Thani Campus, analyzing the Mean Squared Error (MSE) for each phase consisting of Phase A: 0.00133, Phase B: 0.00137, and Phase C: 0.00216. Through linear regression analysis, we establish the correlation between current and electrical energy in each phase, facilitating the prediction of photovoltaic power generation system performance and timely maintenance alerts. This predictive analysis aids in mitigating greenhouse gas emissions, preventing potential damages from short circuits, and ensuring system safety during installation. Our findings, which are accessible through a user-friendly dashboard interface, address a gap in previous studies by providing empirical data specifically from Prince of Songkla University, Surat Thani Campus, and by integrating IoT technology for real-time monitoring and analysis, underscoring the novelty of our approach.

A method for detecting photovoltaic panel faults using a drone equipped with a multispectral camera

Abstract. Photovoltaic power stations utilizing solar energy, have grown in scale, resulting in an increase in operational maintenance requirements. Efficient inspection of components within these stations is crucial. However, the large area of photovoltaic power generation, coupled with a substantial number of photovoltaic panels and complex geographical environments, renders manual inspection methods highly inefficient and inadequate for modern photovoltaic power stations. To address this issue, this paper proposes a method and system for hot spot detection on photovoltaic panels using unmanned aerial vehicles (UAVs) equipped with multispectral cameras. The UAVs capture visible and infrared images of the photovoltaic power plant, which are then processed for photogrammetry to determine imaging position and attitude. The infrared images are stitched together using this information, forming a geographically referenced overall image. Hot spot detection is performed on the infrared images, enabling the identification of faulty photovoltaic panels and facilitating efficient inspection and maintenance. Experimental trials were conducted at a photovoltaic power station in Qingyuan, Guangdong Province China. The results demonstrate the effectiveness of the proposed method in accurately detecting panels with hot spot faults.

Optimization and analysis of methanol production from CO2 and solar-driven hydrogen production: A Danish case study

This paper presents a comprehensive techno-economic analysis of the world's first large-scale commercial Power-to-X (PtX) plant in Kassø, Denmark. This innovative facility combines solar photovoltaic (PV), an electrical grid, green hydrogen production, methanol synthesis, and district heating in an integrated system. We conduct an in-depth analysis to explore optimal operating conditions, the potential for sector coupling within the power and heat sectors, and the economic feasibility of the integrated system. In particular, we study different scenarios considering changes in solar PV power generation, electricity market price, CO2 price, and methanol price to explore their impacts on the system performance. Our results show that the electrolyzer's operation, a pivotal component of the system, heavily depends on the electricity spot price as well as the methanol price. Based on the green production of methanol, a premium price of 4800 DKK/t (0.64 €/t) is considered in this study (selling price of 8400 DKK/t). The results show that in case of high electricity prices (2022 spot market) or no premium price for methanol, the optimum annual production of methanol decreases by 58% and 67%, respectively. Furthermore, we observe that the variability of solar power output significantly impacts the plant's performance and economic viability, as it supplies about 30% of the plant's power consumption. Although the results are unique to the Kassø plant, the study's techniques and insights can be applied to other comparable systems around the world. The study also highlights how crucial it is to take market, climate, and technical factors into account when developing and running integrated energy systems.

A review on application and innovation of wind-solar power generation technologies based on dual carbon targets

With the increasing depletion of traditional energy sources, the greenhouse effect is becoming increasingly serious, mankind is in urgent need of clean and renewable new energy demand is increasing, in order to support the carbon peak&carbon neutral goal, in the future a longer period of time, the improvement of wind power age innovation is the principal procedure for the advancement of numerous nations today, many countries and international organizations to vigorously promote the development of wind power and photovoltaic power generation system will also continue to accelerate the development of new energy generation systems in China. Chinas renewable power age framework, which is basically founded on wind power and will keep on progressing, is being fiercely promoted by many nations and international organizations. This thesis mainly exemplifies the many applications of wind power generation technology in China based on the dual-carbon target through the approaches of literature reviews, and incorporates the inventive innovation model of wind power age innovation and different methodologies. Combined with domestic and international cases, this review summarizes how to solve the dilemmas faced by renewable energy technologies and how to optimize the operation and improve the efficiency of power generation by using innovative technologies, which can provide technical support for the realization of the dual-carbon goal.

Enhancing Power Quality in ON-Grid Systems with PV-STATCOM for Reactive Power Compensation

This research investigates the integration of an Artificial Neural Network (ANN) controlled boost inverter into a solar photovoltaic (PV) system connected to the utility grid. The boost inverter, equipped with a Static Synchronous Compensator (STATCOM), ensures stable grid voltage and enhances power quality. The ANN controller fine-tunes the DC-AC conversion process of the boost inverter in real-time, ensuring efficient operation under varying PV output and grid conditions. The system's performance was evaluated through simulations, demonstrating its ability to efficiently manage PV power generation and correct reactive power in local loads, thereby enhancing system reliability and grid stability.

Short-term photovoltaic energy generation for solar powered high efficiency irrigation systems using LSTM with Spatio-temporal attention mechanism

Solar irrigation systems should become more practical and efficient as technology advances. Automation and AI-based technologies can optimize solar energy use for irrigation while reducing environmental impacts and costs. These innovations have the potential to make agriculture more environmentally friendly and sustainable. Solar irrigation system implementation can be hampered by a lack of technical expertise in installation, operation, and maintenance. It must be technically and economically feasible to be practical and continuous. Due to weather and solar irradiation, photovoltaic power generation is difficult for high-efficiency irrigation systems. As a result, more precise photovoltaic output calculations could improve solar power systems. Customers should benefit from increased power plant versatility and high-quality electricity. As a result, an artificial intelligence-powered automated irrigation power-generation system may improve the existing efficiency. To predict high-efficiency irrigation system power outputs, this study proposed a spatial and temporal attention block-based long-short-term memory (LSTM) model. Using MSE, RMSE, and MAE, the results have been compared to pre-existing ML and a simple LSTM network. Moreover, it has been found that our model outperformed cutting-edge methods. MAPE was improved by 6–7% by increasing Look Back (LB) and Look Forward (LF). Future goals include adapting the technology for wind power production and improving the proposed model to harness customer behavior to improve forecasting accuracy.

Optimal sizing of standalone hybrid renewable energy system based on reliability indicator: A case study

The economical electrification of rural settlements extensively utilizes renewable energy sources. In recent years, the deployment of solar photovoltaic, wind turbine, and biomass gasifier-based power generation technologies for electricity accessibility in remote regions has received greater prominence. This paper presents an optimal component design of a hybrid solar PV-wind and battery system along with a biomass gasifier to provide continuous electricity for rural communities of Uttarakhand, India. In this study, a recently developed population-based giza pyramids construction (GPC) methodology is applied to determine the optimal component sizing. The findings of the GPC method are contrasted with the particle swarm optimization (PSO) methodology to assess the effectiveness of the proposed method. The annualized system cost and levelized cost of energy of the hybrid renewable energy system have been minimized subject to technical reliability indicators such as loss of power supply probability and renewable fraction.Moreover, a systematic exploration of the system has been conducted to assess its optimal feasibility across different values of power loss probability ranging from (0% to 4%). The simulation indicates that the proposed method adeptly identifies globally optimal values, leading to a $177 decrease in annual cost and 8% reduction in average simulation time compared to PSO. The research reveals that the balanced point (relative attainment), where system reliability and cost-effectiveness intersect optimally, is achieved at a 3% power loss level, resulting in the most significant cost reduction of $199 compared to other probability levels.

The influence of ripple on DC energy measurement and the improvement of wavelet threshold function filtering method

DC energy has been applied in the construction of DC power grids, photovoltaic power generation, energy storage, and rapid charging of electric vehicles. The application of DC energy is of great significance for energy conservation, emission reduction, carbon reduction, and efficiency increase. In this paper, the reasons for the decrease in DC energy measurement accuracy caused by ripples in voltage and current are analyzed. The minimum requirements of ripples for DC energy meters are analyzed, and the traditional filtering method based on wavelet threshold is studied. An optimized and fast iterative wavelet threshold function was designed, the current signal of electric vehicle charging was selected, and the effectiveness of the algorithm proposed in this paper was verified through simulation.

Key technologies and applications of cloud-edge collaborative integrated low and medium voltage distributed photovoltaic coordination control

The large-scale distributed photovoltaic access leads to overvoltage, equipment overload, and difficulty in on-site consumption, which causes power to be sent back to the high voltage level, posing hidden dangers to the safe operation of the distribution network. This article proposes optimizing the medium-voltage distribution network at the cloud side with the goal of maximum consumption and operating cost, forming a rolling strategy adjustment based on load forecasting within the day. At the edge side, the distributed photovoltaic control in the low-voltage distribution network within the jurisdiction is adjusted based on the daily adjustment strategy issued by the cloud side, completing the optimization and regulation of distributed photovoltaics on a distribution substation unit basis to achieve the control and integration of distributed power sources and consumption, coordination between operation and side, and coordinated control in the integrated medium and low-voltage zoning, achieving autonomous distribution substations and feeder lines. Field applications have proven that this solution solves problems such as line and transformer overload, and voltage quality caused by distributed photovoltaic power generation.

Skip-based combined prediction method for distributed photovoltaic power generation

Skip-based combined prediction method for distributed photovoltaic power generation