Photovoltaic Power Generation System Research Articles

Design and Performance Analysis of Photovoltaic Power Generation Light Emitting Diode Device

This research focuses on an independent photovoltaic power generation system with supercapacitor energy storage as the study subject. The model is simplified, and the photovoltaic power generation light emitting diode (LED) device is designed based on the given parameters. This system selects a Boost-type step-up converter for the supercapacitor charging circuit, capable of achieving Maximum Power Point Tracking (MPPT). The supercapacitor discharge circuit employs an LM2596 series regulator to power the rated load LED (12 V, 1 W), providing good linear regulation capability. In designing other hardware components of the device, the characteristics of a photoresistor are utilized to implement an automatic load switch circuit. An efficient single-chip integrated circuit LM2575 series is used as a regulator to convert the voltage across the supercapacitor terminals to +15 V and +5 V. Considering the need to collect the output voltage of the photovoltaic cell array and the voltage across the supercapacitor terminals, a resistor divider method is used to sample these two voltages. The sampling circuit includes a resistor divider circuit and a linear optocoupler isolation circuit. An HNC-25LTS series Hall current sensor is used for current sampling to measure AC, DC, and pulse signals under electrical isolation conditions. The supercapacitor, solar photovoltaic panel, control unit, and the main circuit for supercapacitor charging and discharging have been assembled in the experiment. Connecting the charging and discharging circuit with the photovoltaic panel and LED, the system provides power to the LED during the night. Under varying light intensity and temperature conditions, the photovoltaic output voltage waveform, PWM waveform, and Boost circuit output voltage waveform remain stable when reaching the MPPT point. The output power with added MPPT control at different times is compared. The results indicate that the designed system has effectively achieved the functionality of MPPT for solar energy.

IGWO-VINC Algorithm Applied to MPPT Strategy for PV System

As a kind of inexhaustible renewable energy, solar energy is popular, and photovoltaic power generation has been paid attention to by all circles. However, determining the global maximum power point (GMPP) is difficult under external ambient temperature and light intensity change, and MPPT control technology becomes the key to research. Fast, accurate, and stable GMPP capture has become a hot research problem in PV power generation systems. The GWO algorithm incorporating the Levy flight function and the INC using the vertex as the dividing point with different step sizes on the left and right sides are combined and applied to the MPPT control strategy of PV systems. The global search is completed by IGWO first, and then the exact search is completed by the improved INC when it is close to the global optimum. The final tracking accuracy is above 99%, and compared with the GWO, INC, and ICS-IP&O algorithms, respectively, in the case of abrupt changes, the tracking time is accelerated by 0.021 s on average. The oscillation amplitude is smaller, and the voltage is more stable.

Research on opportunity maintenance strategy for PV power generation systems considering random impact

Research on opportunity maintenance strategy for PV power generation systems considering random impact

CNN-based defect detection and classification of PV cells by infrared thermography method

ABSTRACT As a crucial component of the photovoltaic (PV) power generation system, effective defect detection of PV cells can help to ensure the power generation efficiency and the system safety. The photo-thermal-radiation infrared thermography experimental system has been built, and the infrared images of normal and defective PV cells were collected by an IR camera. An effective convolutional neural network (CNN) based model with the residual architecture is designed here to detect and classify the defects. An offline date augmentation method is performed to overcome the insufficient image dataset in the step of dataset establishment, so as to improve the defects classification capability of the designed CNN model. The defects classification accuracy of the proposed model is obtained as 96.96% with 3-class. In order to verify the defect classification ability of the designed CNN model, other four models were adopted for comparative analysis. The results indicate that the proposed CNN model has advantages in defect classification of PV cells. The completed work can provide meaningful exploration in the industrial application of defect detection of PV cells.

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.

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 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.

Fault diagnosis study of ship solar photovoltaic power generation system

In this paper, a fault diagnosis method of ship photovoltaic (PV) power generation system based on a convolutional neural network (CNN) is proposed to ensure the smooth navigation of ships. Firstly, to understand the normal operation and fault state of the generation system, a simulation model of the photovoltaic power generation system is constructed using MATLAB / Simulink. Then, the model is simulated to extract the fault data under different fault conditions, and the eigenvalue extraction of the fault data is carried out by using the fast Fourier transform method. Following that, a fault diagnosis model is built using the convolutional neural network and trained accordingly. The final diagnostic results show that the established simulation model and convolutional neural network can provide support for the fault diagnosis of ship photovoltaic power generation system, and can autonomously detect the type of faults and locate the location of the faults after the faults occur in the generation system, to ensure the stable and safe operation of ship power generation system.

Performance prediction of a novel solar power tower system based on spectral beam splitting

Photovoltaic technology is currently the most widely used solar generation technology in engineering. The particular spectral response band of PV cells cannot convert all solar radiation into electricity. Meanwhile, some radiation will become heat, leading to an increase in cell temperature and a decrease in the conversion efficiency of photovoltaic cells. Spectral splitting technology is an effective technology to improve electrical generation efficiency and can achieve full spectrum utilization of solar energy. The current work involved (1) proposing a novel solar power tower comprehensive utilization system based on spectral splitting; (2) developing a spectral splitting thin film configuration by using the Needle method whose transmittance is higher than 90% against the band range of 510 nm-1, 020 nm; (3) setting up a spectral splitting PV panel whose cover glass is coated the developed film, and replacing the heliostat of SPT system by using such spectral splitting PV panels; (4) predicting the electrical power generation of the spectral splitting-based tower type concentrating solar power system. The results demonstrate that the power generation efficiency of the proposed system is 5.5% and 3.46% higher than PV power generation and SPT systems respectively.

Preface

Available on the latest research progress of clean energy and power generation technology, this paper volume gathers a bunch of papers collected from 2023 8th International Conference on Clean Energy and Power Generation Technology (CEPGT 2023), held via virtual form in Zhuhai, China during December 15th to 17th, 2023.The Conference was attended by about 100 delegates around the globe. In the opening ceremony speech, all academicians, experts and scholars were welcomed to attend the Conference and to share insightful speeches and presentations. Then in the keynote speech part, Prof. Marc A. Rosen (Ontario Tech University, Canada), Prof. Haiping Yang (Huazhong University of Science and Technology, China), Prof. Qinmin Yang (Zhejiang University, China), Prof. Qiang Lu (North China Electric Power University, China) and other professors made wonderful academic reports on international frontier hot spots. Apart from the keynote speeches, oral and poster presentation parts were also held and displayed by various scholars, leading to a warm atmosphere of academic discussion.We received a number of submissions from the Conference. After rigorous review and review rebuttal process by related top experts, various excellent papers were accepted and included in this paper volume. These papers cover many topics of the Conference theme, including Energy Security and Clean Use, Energy Conservation and Emission Reduction, Biomass Energy Engineering, Hydroelectric Power Generation, Solar Photovoltaic Power Generation System, etc. The works of this volume can promote the development of clean energy and power generation technology, and thereby enhance scientific information interchange among scholars from top universities, research centers and high-tech enterprises working all around the world.Featuring the most cutting-edge research directions and achievements related to clean energy and power generation technology, this Conference provided the most comprehensive research in related fields and a more comprehensive understanding of the latest results of cross researches in the fields. Meanwhile, it also helped researchers and engineers understand the research frontier, as well as discover the solutions to potential problems.We would like to acknowledge the authors for their contributions and the reviewers for their time to review the submissions. We are thankful to all the committee members and advisors of this volume. Finally, our appreciation also goes to the editors and members of Journal of Physics: Conference Series for their efforts in publishing this volume.We hope that it will serve as a reference for researchers and practitioners in academia and industry in the areas related to clean energy and power generation technology.The Committee of CEPGT 2023List of Committee Member is available in this pdf.

Design of maximum power point energy storage and inverter for photovoltaic power generation

With the increasing tension of fossil energy in the world and the damage to the environment caused by the use of fossil energy, new energy led by solar energy has been paid much more attention by countries around the world. If the efficiency of solar energy is improved, it will bring breakthrough changes to the world’s energy structure. Based on the related applications of solar photovoltaic power generation, this paper designs an independent photovoltaic power generation system with energy storage device. In the form of DC/DC conversion, the system uses the maximum power point tracking technology of photovoltaic cells to realize the efficient use of solar energy during the charging process. The system inverts the DC voltage after the storage, realizing the transformation of DC voltage to 220 V AC voltage, to realize the utilization of energy in various aspects.

Study on the effect of PV tilt angle on power generation

The power generation of a photovoltaic (PV) system is significantly influenced by the tilt angle of the module. The system achieves the highest power efficiency when operated at the optimal tilt angle, which is typically considered in the design of system installation. In this study, the optimal tilt angle of photovoltaic (PV) modules is determined by using PVsyst software and analyzed through shadow simulation under specific boundary conditions. The study examines the power generation of photovoltaic (PV) panels at various inclination angles and illustrates the impact of inclination angle on PV panel power generation by using project-specific data. The study results indicate that the secondary optimization of the tilt angle by PVsyst, in conjunction with the geographic factors of a specific region in Hainan, reveals that the optimal installation tilt angle for maximum power generation benefit is 9°. This finding aligns with the best power generation and benefits from the actual installation, which is at 10° (adjacent to 9°). The conclusions drawn from the exploration emphasize the essential nature of EPC optimization for future projects, as it can serve as a valuable reference for the design of photovoltaic power generation systems.

Distributed power conditioning unit of large-scale space solar power station

In this paper, a multi-bus distributed Power Conditioning Unit (PCU) is proposed for the Space Solar Power Station with large scale photovoltaic array and power levels reaching MW level. In this unit, there are multiple independent Photovoltaic (PV) arrays. In each PV array, there are multiple independent PV subarrays. In this paper, a V-P droop control method with adaptive droop coefficient is proposed, which modifies the droop intercept based on the bus voltage deviation and the power per unit value of the PV array. This method ensures the accuracy of bus voltage and achieves proportional distribution of power between PV arrays based on the proposed topology structure in this paper. When the load changes or the output power of the PV array fluctuates, this method can ensure that power is distributed proportionally. The principle and control method of the proposed droop control method is analyzed in this paper. The effectiveness of the method is verified through MATLAB/Simulink simulation and experiment. Simulation and experimental results show that the proposed method can achieve power distributed proportionally when load changes and PV output power fluctuates, reduce bus voltage error caused by line impedance and differences in rated power of different PV arrays, and improve the performance of PV power generation system applied to space.

Vanadium redox flow battery: Characteristics and application

Renewable energy such as solar energy and wind energy will enter a new period of development. However, the output power of photovoltaic power generation has great randomness. In order to ensure the normal operation of the whole photovoltaic power generation system, it is particularly important to introduce smooth power dynamics of energy storage system (ESS) to ensure electrical stability. As an energy storage device, flow batteries will develop in the direction of large-scale and modularization in the future. The flow battery system can easily realize computer automatic control and is an ideal smart battery. In addition, the combination of flow batteries with photovoltaic cells, wind power stations, tidal power stations, biogas power stations and other renewable energy systems is an important category and content in developing the application fields of flow batteries. As a new type of green battery, Vanadium Redox Flow Battery (VRFB) has the advantages of flexible scale, good charge and discharge performance and long life. It is suitable for large-scale electric energy storage and has attracted wide attention in recent years. In this paper, the characteristics and applications of liquid flow battery and VRFB are summarized. This paper starts from introducing ESS, analyzing several types of flow batteries, and finally focusing on VRFB to analyze its technical characteristics and application market.

LCL-T Module Reduction of Offshore Photovoltaic Power Generation System Based on Current Feedback-routh Approximation Method

The rapid development of offshore photovoltaic power generation has attracted more and more attention. Aiming at the problem of poor output power quality caused by the randomness and intermittentness of offshore photovoltaic power generation, LCL filters and isolation transformers are widely used on the AC side of photovoltaic power generation systems to improve the power quality of Grid-connected. Although this method has a good suppression effect on harmonics and DC components, its higher order will result in a larger amount of calculation, especially in the simulation of large-scale photovoltaic grid-connected systems, the operating speed will be too slow. In order to increase the simulation rate while ensuring the quality of the output power, the high order LCL-T (LCL filter and isolation transformer) module of the photovoltaic power generation system is reduced in order. When the transformer is not saturated, the LCL-T module reduction method based on the weighted current feedback method is adopted, and the purpose of simplification is achieved through the zero-pole cancellation of the transfer function; When the transformer is saturated, the LCL-T module reduction method based on the Routh approximation method is adopted, Use advanced mathematics formulas to reduce the order of high-order system equivalent processing. The PSCAD/EMTDC simulation software verifies the off-grid characteristics and simulation rate of the reduced-order model.

Surface Solar Radiation Resource Evaluation of Xizang Region Based on Station Observation and High-Resolution Satellite Dataset

Xizang boasts a vast and geographically complex landscape with an average elevation surpassing 4000 m. Understanding the spatiotemporal distribution of surface solar radiation is indispensable for simulating surface processes, studying climate change, and designing photovoltaic power generation and solar heating systems. A multi-dimensional, long-term, spatial, and temporal investigation of solar radiation in Xizang was conducted using three unique datasets, including the difference in surface solar radiation (SSR) between high-resolution satellite and ground station data, the annual and monthly distribution of SSR, and the interannual–monthly–daily variation and the coefficient of hourly variability. Combined with high-resolution elevation data, a strong linear correlation was shown between the radiation and the elevation below 4000 m. Furthermore, analysis reveals greater differences in data between east and west compared to the center, as well as between summer and winter seasons. SSR levels vary in steps, reaching the highest from Ngari to Shigatse and the lowest in a U-shaped area formed by southeastern Shannan and southern Nyingchi. In June, high monthly SSR coverage was the highest of the year. Since 1960, the annual mean SSR has generally exhibited a declining trend, displaying distinctive trends across various seasons and datasets. Owing to intricate meteorological factors, some regions exhibited double peaks in monthly SSR. Finally, we have introduced a solar resource assessment standard, along with a multidimensional evaluation of the resources, and categorized all townships. We offer a thorough analysis of Xizang’s solar radiation to provide a comprehensive understanding, which will help to prioritize recommendations for PV construction in Xizang.

Research on Surface Defect Detection Method of Photovoltaic Power Generation Panels——Comparative Analysis of Detecting Model Accuracy

INTRODUCTION: Research on intelligent defect detection technology using machine vision was conducted to address the challenging problem of detecting and localizing PV defects in photovoltaic power generation system operation and maintenance.
 OBJECTIVES: The aim is to improve the accuracy of PV defect detection and enhance the operation and maintenance efficiency of PV power plants.
 METHODS: In this paper, three detection methods such as image processing based detection, traditional machine learning based detection, and deep learning algorithm based detection are discussed and compared, and analyzed respectively. It is finally concluded that the deep learning based detection is more efficient in comparison. Then further analysis and simulation experiments are done through several detection algorithms based on deep learning.
 RESULTS: The experiment yields a high accuracy of the detection model based on the Faster-RCNN algorithm. Its mAP value reaches 92.6%. The detection model based on the YOLOv5 algorithm reaches a mAP value of 91.4%. But its speed is as much as 7 times faster than the model based on the Faster-RCNN algorithm.
 CONCLUSION: Comprehensive speed and accuracy index. Combining the needs of PV defect detection in the operation and maintenance of PV power generation systems with the results of simulation experiments. It is concluded that the detection model based on the YOLOv5 algorithm can provide better detection capability. Modeling with this algorithm is more suitable for PV defect detection.

Application of optimized photovoltaic grid-connected control system based on modular multilevel converters

Photovoltaic power generation is a promising method for generating electricity with a wide range of applications and development potential. It primarily utilizes solar energy and offers sustainable development, green environmental benefits, and abundant solar energy resources. However, there are many external factors that can affect the output characteristics of Photovoltaic cells and the effectiveness of the grid-connected control system. This study describes the introduction of Modular Multilevel Converter (MMC) technology into photovoltaic power generation systems to improve power generation efficiency. It proposes optimizing and improving the technology by adjusting the temperature and magnitude of lighting and combining traditional algorithms to propose a composite control algorithm. The photovoltaic power generation system employs the modular multi-level converter technology to enhance power generation efficiency alongside optimization and improvement. The temperature and size of light are regulated alongside the traditional algorithm to introduce the composite control algorithm. The improved composite algorithm surpasses the traditional one after experimental comparison of the results. The testing of a model photovoltaic power grid-connected system shows that the combination of modular multi-level converter technology and a photovoltaic grid-connected system, incorporating composite proportional integral control and quasi-proportional resonant control algorithms, yields improved results and feasibility. With rationality and effective control. The simulation results show that at 0.5 s, the light intensity suddenly increases from 750 to 1000 W/m2, and the direct-current voltage suddenly increases for a short time, but then decreases rapidly and finally returns to a stable level close to the rated voltage. From this, it can be seen that when the light intensity continues to change, the voltage value on the direct-current bus side of this MMC grid-tied photovoltaic system can still be maintained close to the rated value, ensuring the operational stability of the entire system. Sensibly and effectively controlled. The implementation of MMC technology in photovoltaic power generation systems enhances power generation efficiency, whilst simultaneously supporting the advancement of photovoltaic power generation and contributing towards environmental protection in the long term.

Self-adaptive enhanced learning differential evolution with surprisingly efficient decomposition approach for parameter identification of photovoltaic models

In order for the photovoltaic power generation system to convert energy with the highest efficiency after installation, the unknown parameters of the photovoltaic model need to be accurately set in advance. Therefore, in order to identify unknown parameters of photovoltaic models more reliably, effectively, and accurately, this paper first uses the decomposition approach that is extremely effective for different types of photovoltaic models, called matrix rotation decomposition. The unknown parameters of different photovoltaic models are decomposed into linear parameters and nonlinear parameters through matrix rotation decomposition approach. Then a simple and effective algorithm—Self-adaptive enhanced learning differential evolution (SaELDE)—is proposed to identify the decomposed nonlinear parameters. In SaELDE, there are three improvements: (1) the classification mutation learning mechanism is designed to make classified individuals evolve towards the promising direction; (2) the introduced crossover rate sorting approach effectively makes up for the neglected relationship between individuals and crossover rates; (3) combined with the population reduction strategy, it avoids the impact of the initial population setting on algorithm performance and balances the transition between the exploration and exploitation phases. Finally, the linear parameters are calculated by calculating the derived matrix equations according to the nonlinear parameters. Experimental results from a variety of common photovoltaic models, and even photovoltaic module models that need to face different temperatures and irradiance, show that the proposed SaELDE performs more accurately and has better robustness. In addition, the correlation coefficient of SaELDE is 1 under all conditions, which further proves the accuracy of the parameters extracted by SaELDE.