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Advancements in Photovoltaic Technologies: Innovations for Enhanced Energy Conversion and Efficiency

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A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: closed (15 May 2026) | Viewed by 7229

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Editors

Dr. Mohammed Gmal-Osman

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Guest Editor

Doctoral School of Energy Engineering, National University of Science and Technology POLITEHNICA Bucharest, 060042 Bucharest, Romania
Interests: renewable energy and green technologies process intensification; green extraction; enabling technologies (ultrasound, microwaves, hydrodynamic cavitation); sustainable chemical processes
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Gheorghe Lazaroiu

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Guest Editor

University MARITIMA of Constanta, 900663 Constanta, Romania
Interests: solar-based hydrogen production; solar irradiance; energy transition; solar photovoltaic modules; solar–wind hybrid systems; concentrator photovoltaics (CPVs)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue entitled "Advancements in Photovoltaic Technologies: Innovations for Enhanced Energy Conversion and Efficiency" focuses on the latest breakthroughs and challenges in photovoltaic (PV) technology aimed at improving energy conversion efficiency and sustainability in solar energy systems.

With the increasing global demand for renewable energy solutions, photovoltaic technologies have gained prominence as a key player in achieving clean energy goals. This Special Issue seeks to explore recent innovations in PV materials, devices, and systems that have the potential to enhance the performance, scalability, and affordability of solar energy technologies.

We invite original research articles, reviews, and perspectives that delve into various aspects of photovoltaic technology advancements. Topics of interest include, but are not limited to, the following:

Development of high-efficiency solar cells, including tandem and multijunction solar cells.
Emerging materials for photovoltaics, such as perovskites, organic photovoltaics (OPVs), and quantum dot solar cells.
Advances in thin-film and flexible solar cell technologies.
Photovoltaic system integration with smart grids and energy storage systems.
Innovations in photovoltaic cell architecture and design for enhanced energy conversion.
Novel approaches for reducing energy losses and increasing cell stability and longevity.
Scaling up PV technologies for industrial and commercial applications.
Photovoltaic manufacturing advancements and cost reduction strategies.
Environmental and lifecycle assessments of next-generation photovoltaic systems.

This Special Issue aims to provide a comprehensive platform for researchers, engineers, and industry professionals to share cutting-edge developments, fostering further progress in photovoltaic technology and its role in a sustainable energy future.

Dr. Mohammed Gmal Osman Abdelfadeel
Prof. Dr. Gheorghe Lazaroiu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

photovoltaic energy conversion
high-efficiency solar cells
emerging PV materials
thin-film photovoltaics
smart grid integration with PV
PV system advancements
scalable solar technologies

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Published Papers (7 papers)

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Research

17 pages, 9423 KB

Open AccessArticle

Photovoltaic Power Prediction Based on Multi-Source Environmental Information Fusion Using a VMD-ZOA-LSTM Hybrid Mode

by Zixiu Qin, Hai Wei, Xiaoning Deng, Yi Zhang and Xuecheng Wang

Processes 2026, 14(7), 1166; https://doi.org/10.3390/pr14071166 - 4 Apr 2026

Viewed by 551

Abstract

New energy power generation has become the first choice for low-carbon reform in the energy industry due to its emission reduction characteristics and environmental friendliness. However, due to the fluctuating nature of renewable energy, sustaining consistent reliability and secure performance within the power network has become increasingly challenging. A novel ensemble prediction scheme for photovoltaic (PV) output is presented, leveraging multi-source environmental data fusion to enhance forecast precision. The relationship between environmental variables and PV generation is quantitatively assessed using Pearson’s correlation coefficient to isolate the most influential factors. Subsequently, the PV time-series data are decomposed via variational mode decomposition (VMD) to extract multi-scale dynamic patterns. The refined features are then utilized within a long short-term memory (LSTM) network, whose parameters are adaptively optimized by the zebra optimization algorithm (ZOA). Historical datasets comprising environmental observations and corresponding PV generation records from a representative power station serve as the empirical basis. Results reveal that the VMD-ZOA-LSTM framework achieves the lowest RMSE and MAE, reducing errors by over 50% relative to comparative models. Furthermore, its R² metric outperforms that of the baseline LSTM and VMD-LSTM configurations by 2.05% and 1.19%, respectively, thereby substantiating the efficiency and validity of the proposed modeling strategy. Full article

(This article belongs to the Special Issue Advancements in Photovoltaic Technologies: Innovations for Enhanced Energy Conversion and Efficiency)

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19 pages, 1073 KB

Open AccessArticle

Domain-Adaptive Multimodal Large Language Models for Photovoltaic Fault Diagnosis via Dynamic LoRA Routing

by Junjian Wu, Yiwei Chen, Qihao Min, Ming Chen, Jie Zhao and Mang Ye

Processes 2026, 14(4), 653; https://doi.org/10.3390/pr14040653 - 13 Feb 2026

Viewed by 1290

Abstract

The reliability of photovoltaic (PV) equipment is vital for ensuring the safe and stable operation of power systems. While multimodal large language models (MLLMs) open up promising avenues for intelligent fault diagnosis, they often falter when confronted with the heterogeneity of PV data—where visual observations come from different sensor modalities (e.g., visible, infrared, and thermal) and display strong domain-dependent variations. Conventional Low-Rank Adaptation (LoRA) is not expressive enough to model such modality-aware differences, which can result in insufficient exploitation of informative patterns. To overcome this limitation, we propose PV-FaultExpert, a domain-adaptive MLLM designed specifically for PV equipment fault analysis. PV-FaultExpert is built upon DyLoRA (Dynamic Expert Routing with LoRA), a dynamic routing strategy that reformulates standard LoRA into a shared low-rank component coupled with multiple expert-specific adapters. A routing module then selects expert paths according to input characteristics, allowing the model to adapt to diverse modalities while maintaining parameter efficiency. Moreover, we construct a PVfault diagnosis dataset via ChatGPT-4o-assisted chain-of-thought reasoning and subsequent expert verification, which both supports model training and enables rigorous evaluation of our method. Extensive experiments demonstrate that PV-FaultExpert consistently surpasses strong baselines, including GPT-4 and Claude-3, across multiple evaluation criteria, producing fault analysis reports that are accurate, interpretable, and aligned with safety-critical requirements. Full article

(This article belongs to the Special Issue Advancements in Photovoltaic Technologies: Innovations for Enhanced Energy Conversion and Efficiency)

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24 pages, 4083 KB

Open AccessArticle

Voltage Adaptability of Hierarchical Optimization for Photovoltaic Inverter Control Parameters in AC/DC Hybrid Receiving-End Power Grids

by Ran Sun, Jianbo Wang, Feng Yao, Zhaohui Cui, Xiaomeng Li, Hao Zhang, Jiahao Wang and Lixia Sun

Processes 2026, 14(2), 350; https://doi.org/10.3390/pr14020350 - 19 Jan 2026

Viewed by 524

Abstract

The high rate of photovoltaic integration poses significant challenges in terms of violations of voltage limits in power grids. Additionally, the operational behavior of PV systems under fault conditions requires thorough investigation in receiving-end grids. This paper analyzes the dynamic coupling characteristics between reactive power and transient voltage in a receiving-end grid with high PV penetration and multiple HVDC infeeds, considering typical AC and DC fault scenarios. Voltage adaptability issues in PV generation systems are also examined. Through an enhanced sensitivity analysis method, the suppression capabilities of transient voltage peaks are quantified in the control parameters of low-voltage ride-through (LVRT) and high-voltage ride-through (HVRT) photovoltaic inverters. On this basis, a hierarchical optimization strategy for PV inverter control parameters is proposed to mitigate post-fault transient voltage peaks and improve the transient voltage response both during and after faults. The feasibility of the proposed method has been verified through simulation on a revised 10-generator 39-bus power system. Following optimization, the transient voltage peak is reduced from 1.263 to 1.098. This validation offers support for the reliable grid connection of the Henan Power Grid. In the events of the N-2 fault at 500 kV and Tian-zhong HVDC monopolar block fault, the post-fault voltage at each node remains below 1.1 p.u. This serves as evidence of a significant enhancement in transient voltage stability within the Henan Power Grid, demonstrating effective improvements in power supply reliability and operational performance. Full article

(This article belongs to the Special Issue Advancements in Photovoltaic Technologies: Innovations for Enhanced Energy Conversion and Efficiency)

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15 pages, 1446 KB

Open AccessArticle

IWMA-VINC-Based Maximum Power Point Tracking Strategy for Photovoltaic Systems

by Yichen Xiong, Peichen Han, Wenchao Qin and Junhao Li

Processes 2025, 13(12), 3976; https://doi.org/10.3390/pr13123976 - 9 Dec 2025

Cited by 1 | Viewed by 580

Abstract

This paper proposes a hybrid photovoltaic (PV) Maximum Power Point Tracking (MPPT) strategy to tackle local optima, slow dynamic response, and steady-state oscillations under partial shading conditions (PSC). The method combines an Improved Whale Migration Algorithm (IWMA) with a variable-step Incremental Conductance (VINC) technique. IWMA employs a Tent–Logistic–Cosine chaotic initialization, dynamic weight coefficients, random feedback, and a distance-sensitive term to enhance population diversity, strengthen global exploration, and reduce the risk of convergence to local maxima. The VINC stage adaptively adjusts the step size based on incremental conductance, providing fine local refinement around the global maximum power point (GMPP) and suppressing steady-state power ripple. Extensive MATLAB/Simulink simulations with multiple random trials show that the proposed IWMA-VINC strategy consistently outperforms the Whale Migration Algorithm (WMA), A Simplified Particle Swarm Optimization Algorithm Combining Natural Selection and Conductivity Incremental Approach (NSNPSO-INC), and the Grey Wolf Optimizer and Whale Optimization Algorithm (GWO-WOA) under both static and dynamic PSC, achieving the highest tracking accuracies (99.74% static, 99.44% dynamic), higher average output power, shorter convergence times, and the smallest variance across trials. These results demonstrate that IWMA-VINC offers a robust and high-performance MPPT solution for PV systems operating in complex illumination environments. Full article

(This article belongs to the Special Issue Advancements in Photovoltaic Technologies: Innovations for Enhanced Energy Conversion and Efficiency)

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14 pages, 942 KB

Open AccessArticle

Power Effectiveness Factor: A Method for Evaluating Photovoltaic Enhancement Techniques

by Sakhr M. Sultan and C. P. Tso

Processes 2025, 13(8), 2532; https://doi.org/10.3390/pr13082532 - 11 Aug 2025

Viewed by 709

Abstract

Photovoltaic (PV) module enhancers, such as coolers and reflectors, are advanced technologies aimed at improving PV performance. The conventional approach for selecting the optimal PV enhancer relies on the observation of the highest power. While effective in comparing different enhancer designs, this method does not determine the maximum performance that the PV enhancer can achieve. To address this limitation, a new methodology is introduced that overcomes this drawback. It relies on three essential parameters: the net power gain with an enhancer, the power output of a PV module without an enhancer, and the maximum power of a PV module under standard test conditions. The impact of each parameter on the proposed method is analyzed, and enhancers are classified based on the method’s output. Maximum or minimum performance is observed when the method’s value is either in unity with or matches the ratio of a PV module’s power output (without an enhancer) to its maximum power under standard conditions. To validate this approach in practical applications, experimental data from previous studies are examined. The results confirm that this technique can be applied for real-world cases and can effectively categorize PV enhancers, offering valuable insights for researchers, designers, and manufacturers. Full article

(This article belongs to the Special Issue Advancements in Photovoltaic Technologies: Innovations for Enhanced Energy Conversion and Efficiency)

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18 pages, 1634 KB

Open AccessArticle

Research on Photovoltaic Long-Term Power Prediction Model Based on Superposition Generalization Method

by Yun Chen, Jilei Liu, Bei Liu, Shipeng Liu and Dongdong Zhang

Processes 2025, 13(5), 1263; https://doi.org/10.3390/pr13051263 - 22 Apr 2025

Cited by 4 | Viewed by 1140

Abstract

The integration of renewable energy sources, specifically photovoltaic generation, into the grid at a large scale has significantly heightened the volatility and unpredictability of the power system. Consequently, this presents formidable challenges to ensuring the reliable operation of the grid. This study introduces a novel stacked model for photovoltaic power prediction, integrating multiple conventional data processing methods as base learners, including Group Method of Data Handling (GMDH), Least Squares Support Vector Machine (LSSVM), Radial Basis Function Neural Network (RBFNN), and Emotional Neural Network (ENN). A Backpropagation Neural Network (BPNN) serves as the meta-learner, utilizing the outputs of the base learners as input features to enhance overall prediction accuracy by mitigating individual model errors. To assess the model’s effectiveness, five evaluation metrics are employed: Bayesian Information Criterion (BIC), Percent Mean Average Relative Error (PMARE), Legates and McCabe Index (LM), Mean Absolute Deviation (MAD), and Root Mean Square Error (RMSE), ensuring long-term stability in photovoltaic power output forecasting. Additionally, the model’s effectiveness and accuracy are validated using operational data from photovoltaic power plants in a particular province of China. The results indicate that the stacked model, after training, testing, and validation on multiple performance metrics, surpasses baseline single models in performance. Full article

(This article belongs to the Special Issue Advancements in Photovoltaic Technologies: Innovations for Enhanced Energy Conversion and Efficiency)

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31 pages, 11718 KB

Open AccessArticle

Performance Evaluation of LMPO-Based MPPT Technique for Two-Stage GIPV System with LCL Under Various Meteorological Conditions

by Jaswant Singh, Surya Prakash Singh and Kripa S. Verma

Processes 2025, 13(3), 849; https://doi.org/10.3390/pr13030849 - 14 Mar 2025

Cited by 2 | Viewed by 1353

Abstract

This paper presents a variable step-size efficient learning modified P&O (LMPO) MPPT algorithm and adaptive proportional–integral (API)-based control techniques for a two-stage three-phase grid-integrated photovoltaic (TS-GIPV) system using an LCL filter. The proposed novel controlled technique introduces two-stage systems under different meteorological conditions and load deviations. The two-stage system with the presented control technique includes maximum power point tracking (MPPT) techniques, intermediate DC-link voltage, and grid current synchronization with a voltage source converter (VSC), respectively. This technique is implemented to improve the extract MPP of the solar PV generator system. An innovative grid-side VSC control technique addresses DC link regulation. Furthermore, this method regulates DC link voltage with an outer voltage loop and an inner current loop controller. Distinctively, the proposed technique regulates the inner loop while avoiding the outer loop. A control mechanism uses an API controller to regulate DC link voltage, distribute power, and synchronize grid current in the face of different scenarios. The fluctuating voltage of the DC link will be kept stable through power balancing. Hence, this technique improves the system stability, dynamic response, and component longevity by effectively reducing oscillations in the fluctuating DC link voltage at twice the grid frequency. The total harmonic distortion (THD%) of the grid currents of the PV power generated in the grid is maintained within the recommended limits. The proposed technique is simulated and verified through MATLAB/Simulink 2019b under different scenarios. Full article

(This article belongs to the Special Issue Advancements in Photovoltaic Technologies: Innovations for Enhanced Energy Conversion and Efficiency)

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