IET Renewable Power Generation最新文献

{"title":"Photovoltaic Power Forecasting: A Review on Models and Future Research Directions","authors":"Upma Singh,&nbsp;Nisha Singh,&nbsp;Hasmat Malik,&nbsp;Mohammad Asef Hossaini","doi":"10.1049/rpg2.70186","DOIUrl":"https://doi.org/10.1049/rpg2.70186","url":null,"abstract":"<p>The stability of power grid systems can be significantly affected by the unpredictability and volatility of power generation; however, accurate forecasting of solar energy power can help reduce this impact. This benefits the system through lower operating costs, balanced operation, and optimal dispatch. Over the past decade, extensive research has been published on this topic, exploring physical models, artificial intelligence (AI) techniques, and numerical and probabilistic approaches. Additionally, previous review studies centred their review discussions on a specific event horizon, others focused exclusively on the geographical horizon, and assessed only particular classes of photovoltaic (PV) output power forecasts. They paid little or no attention to other classes. Therefore, a thorough analysis of solar PV output power forecasting methods is required. In this paper, special focus is given to deep learning (DL), machine learning (ML), and hybrid methods, as these AI areas are gaining popularity. This study aims to provide a comprehensive and critical review of the latest AI applications. It also features a statistical analysis of forecasting errors based on over a hundred solar generation forecast studies. Additionally, the paper offers a brief introduction to the metrics used in ML, DL, and hybrid methods and their interpretation. A discussion of factors influencing forecasting errors is included. Future models will be more accurate because of the clarification that has been provided.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"20 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70186","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146680395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coordinated Planning of Flexibility Resources for HVDC-Connected Renewable Base and Load Center: A CVaR-Based Probabilistic Approach","authors":"Yu Xiao,&nbsp;Hongxing Ye,&nbsp;Yinyin Ge,&nbsp;Yi Huang,&nbsp;Haoxiang Wang","doi":"10.1049/rpg2.70193","DOIUrl":"https://doi.org/10.1049/rpg2.70193","url":null,"abstract":"<p>The geographic separation of renewable resources from major demand centers has promoted the use of high voltage direct current (HVDC) lines for long-distance power transmission. The inherent volatility and uncertainty of renewable, coupled with load fluctuations and HVDC transmission constraint, pose significant challenges to system reliability. This paper proposes a conditional value-at-risk (CVaR)-based coordinated planning framework for flexibility resources in HVDC-interconnected systems. The work establishes a probabilistic reserve allocation framework that improves supply confidence level by quantifying the tail risk via CVaR theory. Furthermore, an operation-constrained planning model is developed to jointly optimize storage deployment, thermal unit retrofits, and cross-regional reserve allocation at both sending and receiving ends. Simulation is carried out with a real-world case. The case study reveals the turning point of flexible resource investment. Numerical simulations on a real inter-regional HVDC system show that, relative to deterministic methods, the proposed approach lowers system risk by 74.2% and 27.12% at the sending and receiving ends, respectively, while maintaining a renewable accommodation confidence above 97% and load supply reliability over 98%.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"20 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70193","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146215895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating Long-Term Degradation and Defects of Solar Photovoltaic Modules in Tropical Climates: A Case Study of Malaysia","authors":"Abdulwahab A. Q. Hasan,&nbsp;A. W. Mahmood Zuhdi,&nbsp;Ali Q. Al-Shetwi,&nbsp;Mohammad Aminul Islam,&nbsp;K. Prajindra Sankar,&nbsp;M. I. Idris,&nbsp;Gamal Alkawsi","doi":"10.1049/rpg2.70198","DOIUrl":"https://doi.org/10.1049/rpg2.70198","url":null,"abstract":"<p>Photovoltaic (PV) modules are vital components of renewable energy systems, yet their performance tends to decline over time due to exposure to various environmental conditions. In Malaysia's tropical climate, where high humidity, intense sunlight, and frequent rainfall prevail, identifying the key factors contributing to this degradation is crucial. This paper examines the primary degradation modes in four monocrystalline silicon PV modules after nine years of outdoor exposure in Kuala Lumpur, Malaysia, offering insights into real-world degradation mechanisms that can help manufacturers enhance durability and performance of solar energy systems in similar climates. The outcomes are compared with other studies that analyse the degradation of PV modules under similar tropical conditions with nearly identical exposure periods of 8, 11, and 12 years. The analysis was carried out using visual inspection, electroluminescence, and electrical performance evaluation to assess the extent of degradation. The findings indicated a substantial average power degradation of 40.35%. The most significant factors contributing to this severe power loss were encapsulant discolouration and the presence of snail trails with cracks. Notably, the extreme discolouration of the encapsulant documented in this research aligns with results from a Spanish study, in which PV modules underwent 22 years of exposure. This comparison highlights how humidity and temperature accelerate deterioration, making the findings relevant for other tropical regions. These results underscore the rapid deterioration of PV modules in tropical regions and provide crucial insights for improving PV system performance in high-humidity climates.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"20 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70198","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146154737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-Enabled Predictive Analytics for Wind Turbine Health and Solar Farm Performance Using Distributed Sensor Networks","authors":"Nasir Muhamad,&nbsp;Xisheng Li,&nbsp;Saqif Imtiaz,&nbsp;Mohammad Rashed M. Altimania,&nbsp;Waleed Mohammed Abdelfattah,&nbsp;Hafiz Mudassir Munir","doi":"10.1049/rpg2.70200","DOIUrl":"https://doi.org/10.1049/rpg2.70200","url":null,"abstract":"<p>Operational optimisation and predictive maintenance are essential for upgraded reliability and cost-efficiency in renewable energy systems. This study proposed an integrated model combining physics-informed neural networks (PINNs), adaptive decision-making and transfer learning to improve the performance of solar farms and wind turbines. The model leverages domain knowledge to capture precise fault identification, enlarge failure prediction lead times and enable rapid deployment across geographically diverse installations with nominal site-specific data. Field evaluations across multiple solar and wind installations illustrate that the PINN-based framework achieves up to 87.3% accuracy in component fault prediction with lead times of 14.2 days for blade issues and 21.3 days for gearbox failures, outperforming commercial condition monitoring systems and conventional machine learning. Innovative algorithms to optimise the cleaning of solar panels have shown improved energy production (8.3%), reduced water consumption (31.2%) and decreased labour requirements (34.1%). The architecture that has been used for the edge-computing systems supports analytics in real time and has had an impact on maintaining operational capabilities above 93.2% even with disruption in communication. This forecast also indicates that through the use of the model's ability to perform transfer learning; it provides the opportunity to capture more than 85% of the initial model's performance when installing on new installations with only 65.1% of the necessary initial training samples while overcoming the cold-start challenge. Although there are limitations due to the reliance on detailed component specifications, environmental variability, and the length of time required for system adaptation, the results have demonstrated significant economic and operational advantages. The practical and scalable implementation of this concept will allow for the continued implementation of Predictive and Resource-Efficient Renewable Energy Operations.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"20 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70200","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146216774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TCN–Transformer Hybrid Network With Physical Constraints for Short-Term Wind Speed Interval Prediction","authors":"Chen Huang,&nbsp;Lihua Mi,&nbsp;Jie Cai,&nbsp;Kai Li,&nbsp;Ye Liu,&nbsp;Yan Han","doi":"10.1049/rpg2.70197","DOIUrl":"https://doi.org/10.1049/rpg2.70197","url":null,"abstract":"<p>Accurate probabilistic wind speed forecasting is crucial for mitigating the adverse effects of wind variability on power systems and facilitating large-scale wind energy integration. Existing studies have primarily focused on deterministic predictions and ignore the guidance of physical laws for probabilistic prediction. This research proposes a novel physics-informed interval forecasting approach that combines temporal convolutional networks (TCN) with Transformer architectures and quantile regression (QR) methodology. Furthermore, the energy conservation and the ideal gas equation of state ensure that the model follows physical laws during the training process. Comprehensive experiments use multiple datasets across different seasons and different quantile levels (<i>α</i> = 0.05 and <i>α</i> = 0.1). The results demonstrate that the TCN–Transformer model consistently outperforms four benchmark methods in both single-step and multi-step predictions. For instance, the proposed model maintains a PICP (coverage probability of predictive interval [PI]) value of 0.969 and a PINAW (PI-normalised average width) value of 0.341 for single-step winter predictions at PINC = 0.95, while the PICP values of other benchmark models are less than 0.95. These results establish the TCN–Transformer framework as an advanced solution for probabilistic wind speed forecasting in power system applications.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"20 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70197","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146155288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent Energy Management for EV Charging in Renewable Energy Based Microgrids Using Advanced Hybrid Fuzzy-PI Controller","authors":"Rathika Natarajan,&nbsp;Jaisiva Selvaraj,&nbsp;Prabaakaran Kandasamy,&nbsp;Tefera Mekonnen Azerefegn","doi":"10.1049/rpg2.70196","DOIUrl":"https://doi.org/10.1049/rpg2.70196","url":null,"abstract":"<p><b>Background</b>: Increasing Electric Vehicle (EV) possession has resulted in an abundance of Charging Stations (CSs), which nurtures load demands and causes grid interruptions in peak hours. By using an effective Energy Management Strategy (EMS), microgrids provide a workable solution to these problems with the electrical distribution infrastructure. DC microgrids powered by renewable energy present a promising alternative, but their efficacy is limited by the fluctuating availability of renewable energy sources (RES) and the erratic demand for EV charging. Therefore, to ensure cost-effective, reliable, and environmentally sustainable EV charging, an efficient and adaptive EMS is required.</p><p><b>Methods</b>: This research proposes an advanced hybrid energy management approach for DC microgrids powered by RES that incorporate EV charging stations. The method optimises power flow among solar systems, fuel cells, battery storage, and EV loads by combining a Dwarf Mongoose–Zebra Optimisation tuned Proportional–Integral controller with Fuzzy Logic Control (DMZO-PI+Fuzzy). The hybrid Dwarf Mongoose–Zebra Optimisation algorithm is utilised to optimise PI controller gains. The control signal from the fuzzy control and the DMZO Optimised PI controller are combined to enhance the controller performance in the proposed model of EVCS. MATLAB/Simulink simulations are used to validate the proposed DMZO-PI+Fuzzy method under various operating conditions.</p><p><b>Results</b>: The proposed DMZO-PI+Fuzzy strategy performs significantly better than traditional approaches, according to simulation results. With a minimum tariff of 0.034 USD/kWh during off-peak hours, charging costs can be lowered by up to 75.56%. On weekdays and weekends, average charging rates drop to 0.086 and 0.088 USD/kWh, respectively, representing cost savings of 45.26% and 56.11%. Also, under dynamic operating conditions, enhanced convergence speed and DC bus voltage stability are observed, and optimal renewable utilisation results in a maximum GHG emission reduction of 55.75%.</p><p><b>Conclusion</b>: The proposed DMZO-PI+Fuzzy energy management framework offers an effective, reliable, and economical feasible EV charging solution for DC microgrids powered by renewable energy. The approach improves both economic and environmental performance by simultaneously optimising charging costs, the use of renewable resources, and efficient power management (PM) in DC MGs.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"20 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70196","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146155289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-Carbon Scheduling Strategy of Integrated Energy System Based on Improved Carbon Emission Flow and Green Certificate-Carbon Joint Trading","authors":"Yi Ding,&nbsp;Chunling Wang,&nbsp;Nian Liu,&nbsp;Chunming Liu","doi":"10.1049/rpg2.70194","DOIUrl":"https://doi.org/10.1049/rpg2.70194","url":null,"abstract":"<p>To achieve the low-carbon transformation of energy system, a low-carbon optimal scheduling strategy for integrated energy systems (IESs) based on improved carbon emission flow and green certificate-carbon joint trading is proposed. Firstly, based on the multi-energy coupling characteristics of IES, the impacts of renewable energy generations (REGs), multi-energy coupling components, and energy storage devices on the carbon flow distribution are quantified, and an improved carbon flow calculation method for IES is proposed to solve the tracing problem of the temporal and spatial transfer of carbon emissions caused by the power loss of coupling components and the operation of energy storage. Then dynamic carbon emission factor reflecting the temporal and spatial distribution characteristics is proposed in combination with the concept of carbon potential. Moreover, to deeply explore the potential of source-load synergy for carbon reduction, a green certificate-carbon joint trading mechanism is constructed on the power generation side by combining the green certificate carbon reduction mechanism and carbon trading, and on the load side, the dynamic carbon emission factor is used as the carbon price correction coefficient for energy consumption prices, driving users to respond synergistically to carbon-energy. Finally, by combining the scheduling of the source-side units with the demand response on the load side, a bi-level optimal scheduling model considering the deep reduction of carbon by source-load synergy is constructed. The simulation analysis of the case study shows that the proposed method achieves carbon measurement, carbon tracing, and reasonable allocation of carbon emission responsibilities in the IES, effectively improving the system's carbon reduction capacity and economic benefits.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"20 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70194","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146176602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multi-Period Source-Storage Coordinated Planning Considering Locational Wind-Solar Complementarity and Dynamic Cost With Self-Declared Capacity","authors":"Yuankang He,&nbsp;Zijun Mao,&nbsp;Hongxing Ye","doi":"10.1049/rpg2.70189","DOIUrl":"https://doi.org/10.1049/rpg2.70189","url":null,"abstract":"<p>With fast growing renewable generations, source-grid-load-storage (SGLS) integrated systems have emerged in recent years. The economical feasibility of SGLS system is still a challenge in many power systems. This paper proposes a multi-period source-storage coordinated planning model for SGLS system project considering spatio-temporal complementarity and dynamic source cost. In order to capture demand for flexible resource and wind-solar complementarity, the model develops hourly operation constraints for wind power, photovoltaic output, and load. It incorporates annually changing investment costs for photovoltaic generators, wind turbine, and energy storage, determining the optimal investment timing. A concept of self-declared capacity is proposed to coordinately minimize the capacity fee by leveraging local resources. Case study with real-data demonstrates that the proposed model can reduce total life-cycle costs by 7.54% to 9.67% and capacity costs by approximately 7.6%, compared to the original project, while assisting the main grid in peak shaving and valley filling. The results reveal that wind farms tends to be built in the early stages, while PV generator and energy storage tend to defer investments. A high proportion of PV generator has seen an increase in the share of energy storage, while energy storage is most sensitive to cost reductions.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"20 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70189","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146154885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hierarchical Control Framework for Stable Operation in Inverter-Dominated Multi-Microgrid Systems via Adaptive Consensus Tuning","authors":"Masood Sorouri,&nbsp;Mahmoud Reza Shakarami,&nbsp;Meysam Doostizadeh,&nbsp;Houman Bastami","doi":"10.1049/rpg2.70191","DOIUrl":"https://doi.org/10.1049/rpg2.70191","url":null,"abstract":"<p>This paper presents a hierarchical control strategy for inverter-dominated multi-microgrid systems, structured into two distinct control layers: (1) the microgrid layer and (2) the multi-microgrid (MMG) layer. In the microgrid layer, decentralised primary control ensures accurate power distribution among inverter-based distributed generators (IBDGs), while distributed secondary control restores voltage and frequency to their reference values. Furthermore, within the MMG layer, the optimal active power reference for each IBDG is calculated, and consensus coefficients (CCs) are adaptively tuned to ensure system stability. Eigenvalue analysis within this layer identifies modes with low damping coefficients, which are classified as critical modes (CMs) and pose a threat to the system's stability. This research demonstrates that the CCs of specific secondary controllers play a significant role in the formation of CMs. This paper proposes a novel adaptive iterative method for increasing the damping ratio to enhance system stability. In each iteration, the CCs with the most significant contribution to CM formation are identified using the participation factor method. Their values are then adjusted to increase the damping of these modes, transitioning them from critical to non-critical modes. MATLAB simulations confirm the effectiveness of the proposed strategy, highlighting its ability to enhance system stability.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"20 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70191","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146155020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid Machine Learning Approach for Enhanced Fault Detection and Power Estimation in Photovoltaic Systems","authors":"Feyyaz Alpsalaz,&nbsp;Yıldırım Özüpak,&nbsp;Emrah Aslan,&nbsp;Hasan Uzel","doi":"10.1049/rpg2.70153","DOIUrl":"https://doi.org/10.1049/rpg2.70153","url":null,"abstract":"<p>Accurate power prediction and fault detection in photovoltaic (PV) systems are essential for improving energy efficiency and enabling predictive maintenance. This study proposes a novel hybrid regression model based on a stacking ensemble architecture, which integrates multiple machine learning algorithms: histogram-based gradient boosting (HGB), k-nearest neighbors (k-NN), decision tree (DT), random forest (RF), and LightGBM as base learners and employs Ridge regression as the meta-learner. The model was designed to detect complex fault conditions such as partial shading and module-level failures using SCADA-type input features. The performance of the proposed model was evaluated using standard regression metrics (<i>R</i>², RMSE, MAE), achieving superior results with an <i>R</i>² of 0.9939, RMSE of 12.0184, and MAE of 8.0544. Paired t-tests confirmed the statistical significance of performance improvements over baseline models (<i>p</i> &lt; 0.05). To ensure transparency, explainability analyses were conducted using SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME), which revealed that fault-related features had the greatest influence on model predictions. Comparative evaluation with recent state-of-the-art approaches demonstrated that the proposed hybrid model is scalable, computationally efficient, and robust under varying environmental and operational conditions. The findings suggest that the model can serve as a reliable and interpretable solution for real-time power forecasting and fault detection in PV systems.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"20 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70153","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146155180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}