Photovoltaic System Research Articles

DC Series Arc Fault Detection Method With Resonant Filter Design for PV Systems

DC Series Arc Fault Detection Method With Resonant Filter Design for PV Systems

Broad-Scale Electroluminescence Analysis of 5 Million+ Photovoltaic Cells for Defect Detection and Degradation Assessment

This paper presents a comprehensive study on the detection, classification, and impact of defects in photovoltaic (PV) modules, using Electroluminescence (EL) imaging as the primary diagnostic tool. The study inspects 85,000 PV modules across 167 installations over a nine-year period, with defects categorized into line cracks, complex cracks, edge-ribbon cracks, and Potential-Induced Degradation (PID). A key finding reveals an increasing prevalence of complex and edge-ribbon cracks in recent installations, highlighting potential shifts in manufacturing or installation processes. Despite these observed defects, the degradation rates of the PV systems remain below 1% per year, with long-term projections indicating that most systems will retain over 80% of their original capacity after 25 years. This study underscores the importance of early defect detection for maintaining system efficiency and longevity, and its findings are crucial for refining manufacturing and installation standards. Novel insights include the integration of EL imaging for defect classification and the suggestion of semantic segmentation techniques to further improve automated defect detection. These advancements hold the potential to optimize the predictive maintenance of PV systems, ensuring enhanced performance and reduced operational losses over time.

Investigating the Impact of Surface Textured Solar Photovoltaic Dummies for Soiling Mitigation: A Computational Fluid Dynamics Study

Soiling of solar collectors is a significant factor causing energy losses in solar photovoltaic power plants. The present study explores the concept of passive soiling mitigation by utilizing surface-textured solar photovoltaic dummies to minimize soiling on solar photovoltaic panels. Notably, this approach has not been extensively explored in the literature, despite its promising potential for enhancing photovoltaic system performance. Employing Computational Fluid Dynamics methodology, the dust-capturing capabilities of these surface textured photovoltaic dummies were explored. The investigation adopted the Discreet Phase Modelling approach coupled with the shear stress transport k-ω model for turbulence modelling. The study systematically analysed the optimal positioning of the dummy within distances between panel and dummy ranging from 2 m to 3 m and varied heights and tilts of installation. The wind analysis of the study area revealed an average wind speed of 3.31 m/s, with stronger winds primarily from the East of South-East to North-East. Experimental designs, incorporating dummy distance, height, and tilt angle, demonstrated that the dummy accumulated 58 % more dust compared to the photovoltaic panel due to micro-turbulences induced by its textured surface. The dummy tilt angle was identified as a critical factor influencing dust deposition, with higher angles increasing turbulent kinetic energy and enhancing dust capture. Additionally, the installation height and distance between the dummy and panel significantly impacted soiling, with closer distances offering better protection. Surface impressions on the dummy further increased dust deposition by 23 % through enhanced friction and reduced airflow. This study highlights the promising potential of surface-textured photovoltaic dummies in mitigating soiling, a concept not widely explored in existing literature. The study advocates for the widespread adoption of these photovoltaic dummies, particularly in environments prone to frequent soiling and where the installation configuration does not allow for easy application of other methods of soiling mitigation.

Observer-based fuzzy T–S control with an estimation error guarantee for MPPT of a photovoltaic battery charger in partial shade conditions

To improve the efficiency and performance of a photovoltaic system (PV) an observer-based fuzzy controller design methodology is provided in the study. The desired controller is achieved by employing a combination of linear matrix inequalities (LMIs). The system consists of a photovoltaic generator (PVG) connected to a boost converter. A battery is linked to the boost converter to stock additional energy for further use. A fuzzy controller based on a T–S fuzzy type observer that guarantees a predefined L2 performance is suggested to achieve maximum power point tracking (MPPT) even under changing weather conditions. An optimal trajectory should be tracked to ensure maximum power operation. For this aim, a specific reference fuzzy model is proposed to create the aimed trajectories. Using this method, the system dynamics are precisely reproduced over a large range of operations. The whole T–S fuzzy methodology, suggested in this paper, aims to ensure the most efficient energy recovery to recharge a battery under partially shaded conditions, resulting in high system efficiency. The proposed method is simulated with MATLAB/SIMULINK and the simulation results, with realistic reference trajectories, are driven while taking into account climate variations. The analysis of these simulations, along with a comparison with two other commonly used approaches, led to the conclusion that the suggested strategy succeeded in reducing the tracking time, as well as eliminating the oscillation that often occurs around maximum power point (MPP).

A distributed photovoltaic short-term power forecasting model based on lightweight AI for edge computing

Abstract In recent years, an immense amount of distributed photovoltaics are integrated into low-voltage distribution network, producing a substantial volume of operational data. The centralized cloud data center cannot process massive amounts of data precisely and promptly. Therefore, the operational status of distributed photovoltaic systems in low-voltage distribution network becomes difficult to predict. However, edge computing in the distribution network enables local data processing to improve the forecasting service’s real-time reliability. In this regard, this paper proposes a distributed photovoltaic short-term power forecasting model based on lightweight AI algorithms. Firstly, based on the Pearson correlation coefficient method, an analysis is conducted on the historical operational data in the network to extract important meteorological features correlated with the photovoltaic power output. Secondly, a distributed photovoltaic power forecasting model for the distribution network is constructed based on the Xception and attention mechanism. Finally, the model is trained using pruning, which removes redundant parts of the model, resulting in a compact and efficient forecasting model. By validating real-world datasets, the results demonstrate that the model presented in this article has a smaller size and higher forecasting accuracy than other state-of-the-art forecasting models.

Firmitas, Utilitas, and Venustas of photovoltaic architecture

The growing interest in applying photovoltaics for construction results in solutions based on the concept of integration with the architecture of the building and its surroundings. This means that the challenge lies not only in the technical integration itself but also in a strictly relationship with architecture. The study aims at determining a critical history of the evolution of photovoltaic architecture, narrowing down its role in the contemporary architecture design, in terms of firmitas (structure), utilitas (functionality), and venustas (aesthetics) of the building as well as its relationship with the environment. This study offers an architectural perspective on the design approaches through the carefully selection of several case study to illustrate main topics, design criteria, limitations, and challenges with a broad spectrum of interpretation. The results demonstrate that the development of integrated photovoltaic systems strengthens the relationship between PV technology and architecture in terms of structure, utility, and aesthetics. This relationship is synergistic and stimulates the parallel development of photovoltaic technology and architectural solutions.

Comparative analysis on the hydrodynamic characteristics of offshore floating photovoltaic systems based on membrane structures under different mooring configurations

In the context of rapidly increasing global energy demand, solar energy is considered one of the most promising alternatives due to its ubiquity and sustainability. Floating photovoltaics, which do not occupy land and offer higher power generation efficiency, are becoming increasingly popular. As a novel type of floating photovoltaic, membrane structures are drawing more attention due to their lightweight nature, easy installation, and cost-effectiveness. Based on the Ocean Sun's floating photovoltaic membrane prototype as a reference, this study designed and fabricated a 1:40 scale model for laboratory experiments. The research investigated the influence of different mooring configurations and lengths on the hydrodynamic characteristics of membrane structures. The conclusions indicate that structures with more anchor chains exhibit smaller amplitudes in various movements and mooring forces. As the mooring length decreases, the amplitudes of various structural movements decrease, but mooring forces increase. Compared to regular wave conditions, mooring has a more significant impact on structural motion responses under irregular waves. In practical engineering, a comprehensive consideration of structural safety and cost-effectiveness is necessary for design. The selection of appropriate mooring configurations and lengths plays a crucial role in ensuring the safe operation and sustainability of membrane structures.

Novel design of centralized square array of pin-fins in microchannels heat sink for thermal management of high concentrator photovoltaic system

Novel design of centralized square array of pin-fins in microchannels heat sink for thermal management of high concentrator photovoltaic system

Collaborative water-electricity operation optimization of a photovoltaic/pumped storage-based aquaculture energy system considering water evaporation effects

The increasing global population, economic development, and urbanization trends have led to a heightened demand for seafood, presenting a significant challenge to the growth trajectory of the food industry. As the most rapidly expanding segment within the food industry, aquaculture presents a sustainable solution to mitigate the overexploitation of marine resources and address the growing demand for food. Aquaculture's sufficient aquatic expanses offer considerable potential for PV deployment, thereby relieving the demand for limited land resources. However, existing research rarely addresses the collaborative operation of water and electricity in aquaculture systems. The effects of water evaporation from PV panel-covered water surfaces on the collaborative water-electricity operation are generally neglected. Hence, this work proposes a collaborative water-electricity operation of a photovoltaic (PV)-pumped storage-based aquaculture energy system considering the water evaporation effects. This optimization method accounts for the reduced water evaporation due to PV panel shading. Water surface PV and pumped storage are integrated into the system to fulfill electricity needs, with pumped storage serving as a dual-purpose device for water and electricity supply. Environmentally sustainable water-electricity generation and aquaculture energy system requirements are established. Water surface PV electricity generation is modeled based on climate and engineering conditions, while aquaculture pond electricity requirement includes oxygenation, feeding, and water replenishment. Finally, a collaborative water-electricity operation model is introduced to optimize operation strategies for various devices. A case study on a fish-light complementation project demonstrates that this optimization method can reduce reliance on the utility grid and overall system operational costs.

Control of a BLDC Motor of an Electric Vehicle Powered by a Photovoltaic System based on Fuzzy Logic MPPT (FLC-MPPT)

Photovoltaic Systems (PV) represent a viable solution for converting solar energy into electricity without emitting harmful gases. This technology remains highly sought-after in the market, primarily due to its renewable characteristics and the absence of movable components, which renders it a low-maintenance option. This produced energy can be stored in batteries tobe used for example in operating electric vehicles. If the generated voltage is inadequate or requires regulation, a boost converter may be employed or a buck boost To manage energy during storage and discharge to provide the energy needed to run the induction motor. In this paper, we proposed the field-oriented control as an approach for controlling the speed of BLDC Motor drive electrical vehicles to improve the overall effectiveness of the proposed system, The BLDC Motor is powered by battery storage from a photovoltaic system. Furthermore, this work also addresses the specifics of modeling the Fuzzy logic control MPPT (FLC-MPPT) used for the energy management system (PV and batteries), as well as the proposed control approach for the electric vehicle motor, which are subsequently simulated in the MatLab environment.

Cost-Effective Dust Detection on Solar PV Panels through Deep Learning: A Step towards Automated Maintenance Systems

Abstract: Accumulation of dust on solar panels impacts the overall efficiency and the amount of energy it produces. Detecting and mitigating dust accumulation is therefore crucial for optimizing solar energy production. While various techniques exist for detecting dust to schedule cleaning, many of these methods uses licensed software like MATLAB, which can be financially burdensome. This study proposes the use of modified pre-trained ResNet-50 model architecture with an adjusted fully connected layer for binary classification. An experimental setup was installed utilizing a single 75 Wp panel with an inclination maintained at 30-degree angle. The fine dirt particles were artificially introduced and datasets of images of clean and dusty panels were collected from five different sides were taken, to compensate for the surface reflectance from the PV panel due to camera angles. Those datasets were used to train and test the model and the accuracy achieved was 90%. The model's ability to detect dust with minimal false positives ensures more efficient maintenance scheduling. This research demonstrates the potential of AI-driven dust detection systems to enhance the operational efficiency of solar PV installations.

A frequency control strategy with DC microgrid systems for EV stations

ABSTRACT Fluctuations in power supply and demand lead to frequency deviations, which have detrimental effects on the operation of electrical devices and equipment. Therefore, the implementation of effective frequency control mechanisms is essential for enhancing the strength and dependability of microgrid systems. Research aims to develop a frequency control mechanism for an electrical grid system that integrates both wind and photovoltaic (PV) energy sources. The proposed system utilises a Doubly Fed Induction Generator (DFIG) system with frequency controller for regulating frequency and balancing source and demand of power in the grid. To optimise the solar power output, a Luo converter is implemented, which offers high efficiency with improved conversion of power. Additionally, an adaptive neuro fuzzy inference system (ANFIS) maximum power point tracking (MPPT) controller is engaged to ensure optimal regulation of photovoltaic system. DC link obtained from the PV system is then utilised for powering DC loads and charging electric vehicles (EVs) effectively. In order to accomplish this, a bidirectional converter is used, which permits power flow in both ways and facilitates effective EV charging from the microgrid system. The validation of research is conducted on MATLAB software, and the results proves greater converter efficiency of 94.93%, with reduced total harmonic distortion (THD) of 1.24%.

Review of Recent Offshore Floating Photovoltaic Systems

Photovoltaic (PV) power generation is a form of clean, renewable, and distributed energy that has become a hot topic in the global energy field. Compared to terrestrial solar PV systems, floating photovoltaic (FPV) systems have gained great interest due to their advantages in conserving land resources, optimizing light utilization, and slowing water evaporation. This paper provides a comprehensive overview of recent advancements in the research and application of FPV systems. First, the main components of FPV systems and their advantages as well as disadvantages are analyzed in detail. Furthermore, the research and practical applications of offshore FPV systems, including rigid floating structures and flexible floating structures, are discussed. Finally, the challenges of offshore FPV systems are analyzed in terms of their stability and economic performance. By summarizing current research on FPV systems, this overview aims to serve as a valuable resource for the development of offshore FPV systems.

Cyber attacks detection and mitigation in hybrid power system using type-2 fuzzy controller

Abstract This paper presents a novel control technique using Fuzzy Type-2 controller for regulating the frequency deviations in an independent Hybrid Power System (HPS). The HPS comprises of a wind system and a solar photovoltaic (PV) system as primary energy sources, along with Fuel Cells (FCs) as additional generation systems and Double Layer Capacitor (DLC) Banks as storage devices to meet the load demand. The frequency variations occur in the HPS due to the intermittency in power outputs from the primary sources, the load changes as well as any attacks in the system. The frequency regulation is achieved by effective coordination control of FC and DLC with the aid of a Fuzzy Type-2 controller and a high pass filter (HPF). The DLC technology adequately satisfies the residual power of hybrid systems resulting from the slow dynamics of FCs. The Fuzzy Type-2 controller is trained using an input-output data set of a PID controller whose gains are tuned to their optimal values using Monte-Carlo state estimators. The proposed system is simulated using real weather data to validate the capability of the Hybrid Power System (HPS) in supplying isolated loads while maintaining power frequency balance conditions. Also the HPS is subjected to attacks in daily load variation and the results are compared with the system without attacks. The simulation results show that the peak over shoots are reduced considerably but the settling time is 0.2 seconds in the system with no attacks where as the settling time is prolonged to 1.2 seconds with the attacks. This paper demonstrates the effectiveness of the Fuzzy Type-2 controller for the coordination control of DLC and FC to achieve generation-load balance condition in comparison with that of the PID controller for HPS system with and without attacks.

Techno-Economic Analysis of Hybrid Pv-Battery-Diesel Power System for Rural Electrification: A Case Study of Ibudo Ora, Ogbomoso

Fossil-fueled generators and electrical grid extensions are the most popular energy sources for supplying electricity to rural areas. However, the high cost of running and maintenance, noise pollution, and a need for decarbonization necessitate the hybridization of different energy sources as a viable solution. Using the conventional technique for the optimal design of a Hybrid Power System (HPS), such as the Hybrid of Multiple Energy Resources (HOMER), is inefficient regarding electricity cost and carbon emission reduction. Hence, this research conducted a technical and economic analysis of an optimal photovoltaic (PV), battery, and diesel generator-based power system to electrify Ibudo Ora, a rural community in Ogbomoso. A feasibility study on electricity demand was conducted on the Ibudo Ora community by conducting an onsite survey, and the community load profile was estimated. Mathematical modeling of the HPS component was formulated. A multi-objective function was developed to minimize the Net Present Cost (NPC), Levelized Cost of Electricity (LCOE), and Total Carbon Emission (TCE) and maximize the System Reliability (SR) of the proposed HPS and optimized using the Energy Valley Optimizer (EVO). MATLAB R2021a was used to simulate the developed model. The performance of the developed EVO-based HPS was evaluated using NPC, LCOE, TCE, and SR as metrics and compared with HOMER, which was used for the same purpose. The NPC of the developed EVO-based HPS and HOMER-based HPS were $998702.87 and $1011984.27, respectively, while the LCOE were $0.4889 and $0.4954. The loss of power supply probabilities of the developed EVO-based and HOMER-based HPS was zero, while in terms of TCE, EVO-based HPS and HOMER-based HPS had 775958.15 kg and 832912.49 kg, respectively. The results showed an appreciable reduction in the NPC, LCOE, and TCE using EVO compared with HOMER for an optimized HPS. This research will assist the government, investors, and policymakers in making decisions on rural electrification using HPS.

Design analysis of a sustainable techno-economic hybrid renewable energy system: Application of solar and wind in Sigulu Island, Uganda

Sustainable energy is central to achieving the global Sustainable Development Goals (SDGs), particularly in electrifying underserved communities. This study examines Sigulu Island, which lacks grid electricity and relies on costly, polluting diesel generators. On-site assessments revealed a daily load demand of 1,455.705 kWh. This study designed and analyzed a Sustainable Techno-economic Hybrid Renewable Energy System (STHRES) combining solar photovoltaics and wind turbines, with battery backup, to meet the island's energy needs. The research adopted both qualitative and quantitative methods, gathering atmospheric weather conditions specific to Sigulu Island. Solar panels and wind turbines were identified as the most viable options, with the system incorporating 677 units of 1 kW solar panels and 27 units of 1 kW wind turbines, generating 839.97 kW and 640.08 kW daily, respectively. Additionally, 527 Li-ion batteries were used to store approximately 1480.05 kW of surplus energy demand to manage fluctuations capable of sustaining the island for 8 hours during a total blackout. The initial installation costs are estimated at $90,393.04 for solar PV, $27,729.82 for wind turbines, $159,169.81 for batteries, and $92,407.00 for the inverter. The STHRES is projected to save $56,917.93 annually, covering 15.4 % of the installation costs compared to diesel operations. Moreover, this system will reduce Uganda's carbon footprint by 436,035.6 kgCO₂ annually, equivalent to a 0.01 % reduction in national emissions. The proposed system decreases the Net Present Cost (NPC) from $426,617.60 to $369,699.67 and the Cost of Energy (COE) from $32.12/kWh to $ 27.79/kWh. With a 9 % Internal Rate of Return (IRR) and a 3 % Return on Investment (ROI), STHRES has a payback period of 8.2 years, demonstrating its financial and environmental benefits for Sigulu Island.

Polyfluorene-based conjugated nanocomposites with in-situ gold nanoparticles: Synthesis via rational chemical passivation and characterization of supramolecular fibrillar structures

We present a rational and innovative chemical approach for synthesizing and characterizing conjugated polymer nanocomposites as direct passivation of gold nanoparticles (AuNPs), highlighting intrinsic morphologies and distinct photophysical properties in the solid state. The pioneering synthesis of polyfluorene with thiol, LaPPS79.10, revolutionized its application as a direct passivator of AuNPs, resulting in stable nanocomposites with the capacity for storage in the dry state and subsequent redispersion in organic solvent. The presence of AuNPs with average diameters of 3.5 ± 1.3 nm induced the formation of an interconnected fibrillar supramolecular network (ribbon-like) and several microdomains in the nanocomposite (LaPPS79.10+AuNPs), correlated by XRD, TEM and SEM measurements. Characterization of the polyfluorene precursor derivative, LaPPS10, revealed bundle-like morphologies with aggregation-induced emission (AIE). Introducing thiol groups into the intermediate structure induced polymer chain organization (LaPPS79.10), leading to unique straw-like and lenticular bundle morphologies, disrupting the non-planar π-π packing and quenching the emission, a case of aggregation-caused quenching (ACQ). In LaPPS79.10+AuNPs, the proximity of the π system to the Au localized surface plasmon resonance (LSPR) resulted in high absorption, negligible self-absorption, and efficient emission quenching, as well as improved thermal resistance compared to the original polymer. The unprecedented discovery of the properties resulting from the electronic interaction between the π system of the AuNPs passivating polymer and the effects of plasmonic resonance represents a significant advance in nanoscience, opening new perspectives for exciting applications in organic photovoltaic devices and metamaterials.

Eco-friendly synthesis of Nd³⁺-doped Eu₂O₃ nanoparticles for enhanced dye-sensitized solar cells utilizing oxalis Corniculata leaf extract

An environmentally friendly synthesis of Nd³⁺-doped Eu₂O₃ nanoparticles (NPs) was developed using Oxalis Corniculata leaf (OCL) extract to enhance the performance of dye-sensitized solar cells (DSSCs). The as-synthesized NPs were annealed at 600 °C to improve their crystallinity. X-ray diffraction and field-emission scanning electron microscopy revealed high crystallinity and a sub-100 nm spherical morphology. Annealing reduced the NP size from ∼85 nm to ∼45 nm and decreased the bandgap from 4.97 eV to 4.62 eV, enhancing low-energy photon absorption. Fourier-transform infrared spectroscopy showed changes in the chemical bonding environment, with a higher presence of dangling bonds in the as-prepared NPs compared to the 600 °C-annealed NPs, likely due to the OCL extract. Photoluminescence spectra confirmed strong red emission peaks at 580 nm and 612 nm, with a slight reduction in the full width at half maximum of the electric dipole transition after annealing. Integrating these NPs into TiO₂ matrices in DSSCs improved power conversion efficiency to 8.58%, outperforming both as-prepared NPs (6.88%) and bare TiO₂ cells (5.05%). This green synthesis approach offers a sustainable pathway for slightly enhancing performance of photovoltaic devices, with additional potential for UV-shielding applications when incorporated into PVA films.

Power quality improvement in three-phase grid tied pv systems with 3-D SVPWM control active power filters

Power quality improvement in three-phase grid tied pv systems with 3-D SVPWM control active power filters

Barriers to the Implementation of On-Grid Photovoltaic Systems in Ecuador

Barriers to the Implementation of On-Grid Photovoltaic Systems in Ecuador