Search Results (353)

The move to sustainable energy systems has increased the requirement for comprehensive decision support frameworks that are uncertainty-aware to guide the selection of solar power plant sites. The rapid growth of investments in solar energy has increased the demand for systematic and accurate decision-support tools to choose the best sites for photovoltaic (PV) power facilities. The selection of solar power plant sites is a complicated multi-criteria decision-making (MCDM) problem that involves technical, economic, environmental, social, and technological aspects. The process is typically associated with ambiguity and incomplete knowledge of experts. To overcome these problems, this paper offers an interval-valued neutrosophic fuzzy TOPSIS (IVN-TOPSIS) method, which extends the standard TOPSIS methodology by including truth, indeterminacy, and falsity membership degrees as interval values. The methodology is utilized in a real case study in the Mediterranean region of Türkiye, comprising three provinces with great potential: Antalya, Mersin, and Adana. An assessment of a complete set of environmental, economic, social, and technological criteria is performed using expert judgments stated in interval-valued neutrosophic language assessments. They were incorporated into a Geographic Information System (GIS) to produce a suitability map indicating the most suitable sites for the facility. The suggested approach is different from the traditional crisp or fuzzy MCDM techniques since it clearly models the degrees of truth, indeterminacy, and falsehood, thus providing a more detailed representation of the expert evaluations. According to the data, Mersin is the most ideal site for the construction of a solar power plant, followed by Antalya, and the least suitable site is Adana. The results suggest that sustainable solar energy planning must go beyond technical resource potential and include integrated and uncertainty-aware assessments. The suggested IVN-TOPSIS framework can serve as a powerful decision-support tool to policymakers, planners, and investors that wish to encourage regionally balanced and sustainable renewable energy development. Full article

The integration of photovoltaic (PV) and wind turbine (WT) systems into modern power grids demands not only accurate component-level models but also a holistic understanding of their coordinated operation. This review bridges the gap between low-level device physics and high-level system coordination, offering a dual perspective often overlooked in existing surveys that treat generation and management separately. We systematically analyze PV models, from single-diode equivalent circuits to data-driven approaches, and WT models, ranging from aerodynamic and mechanical representations to simplified electrical equivalents suitable for stability studies. Critically, we then shift focus to the system level by examining energy management systems (EMS) that enable hybrid PV–WT coordination. Unlike prior reviews that emphasize either component accuracy or dispatch strategies alone, this paper highlights the emerging synergy between hybrid PV–WT modeling and EMS architectures. By identifying mismatches between model fidelity and EMS requirements, this review maps a pathway towards more integrated hybrid renewable systems. The discussion synthesizes key trade-offs in scalability, uncertainty handling, and real-time feasibility, underscoring that true potential is unlocked only through intelligent integration of component models and control architectures. Full article

High penetration of distributed photovoltaic (PV) generation has intensified voltage violations and stochastic voltage fluctuations in distribution networks, while existing voltage–var control methods still have limitations in terms of communication dependence, scalability, and edge deployment. To address these issues, this paper proposes a stability-constrained voltage–var self-optimizing control method for distribution networks based on the Bandit-Guided Online Self-Tuning Group Relative Policy Optimization (BOST-GRPO) algorithm. First, based on the LinDistFlow linearized power-flow model, a communication-free, decentralized, and locally observable reinforcement learning control environment is constructed, enabling each node to independently generate reactive power regulation commands using only local voltage measurements. Second, a contraction-mapping-based stability constraint is embedded into the policy output layer, theoretically guaranteeing the local exponential convergence of nodal voltage deviations around the equilibrium point and reducing the risk of voltage instability caused by overly aggressive policy actions. Meanwhile, device capacity constraints are incorporated into the policy output through a tanh-based action mapping, ensuring the physical feasibility of control commands. On this basis, BOST-GRPO realizes the online self-tuning of key hyperparameters within a single training process through a Bandit-guided mechanism, thereby avoiding the repeated training overhead caused by traditional offline hyperparameter tuning. Simulation results on the IEEE 33-bus system show that the proposed method outperforms benchmark reinforcement learning algorithms in final test cost, voltage deviation suppression, steady-state error, and regulation speed. Further tests under sensitivity matrix mismatch, different initial voltage disturbance intensities, and the extended IEEE 69-bus system demonstrate that the proposed method achieves good robustness and scalability. Full article

With the increasing demand for low-carbon energy, automated defect detection using unmanned aerial vehicle (UAV)-based thermal inspection has become essential for maintaining the reliability of photovoltaic systems. However, existing methods still suffer from low-contrast thermal imagery, large-scale variations of defects, and subtle thermal anomalies. To address these challenges, this study proposes Grouped-Hypergraph-Modulation DEIM (GHM-DEIM), a robust end-to-end detection framework based on an improved DEIM architecture. Specifically, a grouped multi-scale aggregation attention network is introduced to enhance global thermal perception and recover discriminative features from blurred backgrounds. In addition, an enhanced encoder incorporating a hypergraph-based context encoding mechanism is designed to model high-order non-local relationships and improve feature representation across different defect scales. Furthermore, a modulation fusion module is employed to adaptively refine multi-scale feature responses and suppress environmental noise interference. Extensive experiments conducted on the ThermoSolar-PV and PV-HSD-2025 datasets demonstrate that the proposed method consistently outperforms state-of-the-art detectors, achieving mAP@50 values of 88.6% and 74.2%, respectively, with improvements of 4.7% and 2.9% over the baseline. These results demonstrate the effectiveness and robustness of GHM-DEIM for UAV-based PV thermal defect inspection. Full article

Defects in photovoltaic (PV) modules, including hotspots, shading, and diode failures, significantly reduce power-generation efficiency and pose safety risks. This study proposes a real-time detection and localisation framework for PV defects based on infrared images acquired by unmanned aerial vehicles (UAVs). A dedicated dataset of 5583 infrared/visible images was constructed under standardised acquisition conditions. An improved rotating-bounding-box detector, termed YOLO-CLO, was developed upon YOLOv8-OBB by introducing a lightweight C3m module and a shared-convolution LSCD-OBB detection head. The proposed detector attains 99.1% mAP@0.5, 96.7% mAP@0.5:0.95, and 59.88 FPS with only 8.52 M parameters and 23.6 GFLOPs, outperforming the baseline in both accuracy and efficiency. A multi-feature image-processing pipeline combining gradient, grayscale, temperature, and morphological cues identifies hotspots, diode failures, and obstructions with detection accuracies of 96.97%, 100%, and 88.89%, respectively. A component-level localisation strategy integrating GNSS metadata, the Hough transform, and an improved K-means clustering algorithm accurately recovers the row–column index of each defective module within an array. Comparative experiments against YOLOv5 and Faster R-CNN confirm the superiority of the proposed framework. The method offers low hardware dependency and is suitable for engineering deployment in large-scale PV power stations. Full article

Aims: To assess the spatiotemporal stability of extra-pulmonary vein (PV) sources in patients with persistent atrial fibrillation (AF). Methods and results: Nine patients (mean age 63 ± 9 years, 55% male) with persistent AF were included who underwent an initial and at least one redo catheter ablation procedure utilizing panoramic atrial mapping (PAM) systems (CardioInsight, electrographic flow (EGF), and/or charge density (CDM) mapping). Procedures were performed in the following combinations: CDM-CDM (1 patient), CDM-EGF (1 patient), EGF-CDM (3 patients), CardioInsight-CDM (1 patient), EGF-EGF (3 patients). We reviewed maps and analyzed the location of AF sources. Spatiotemporal stability was defined as the presence of an AF source of identical location on available maps during the initial and the redo procedure. In 4 patients (44.4%), localization of AF sources mapped at the repeat procedure corresponded with the localization of sources mapped during the index procedure. In two patients, no sources were identified during the second procedure. In the remaining 3 patients, the localization of sources was detected at different locations. Conclusions: Our findings suggest the presence of spatiotemporal stability of AF sources; however, novel sources can also be found during the repeated procedure, consistent with disease progression. Full article

To ensure the reliability and sustained performance of industrial photovoltaic (PV) systems, fault detection frameworks must achieve both high detection accuracy and computational efficiency, particularly for deployment on resource-constrained edge platforms. This work proposes a lightweight and low-latency photovoltaic defect detection framework that integrates DCGAN-based generative augmentation with the proposed GhostViT-YOLOv10n architecture. The augmentation strategy helps address class imbalance, improve representation of rare defects, and enhance generalization capability in electroluminescence (EL) imagery through structured geometric and photometric transformations. The proposed framework integrates lightweight Ghost-based optimization, Cross-Stage Partial Fusion (C2f), Spatial Pyramid Pooling—Fast (SPPF), MobileViT contextual learning, and SimAM-based attention refinement to improve multi-scale feature extraction while maintaining low computational complexity. Experimental evaluation on the PVEL-AD and PV Multi Defect benchmark datasets demonstrates strong detection performance. On the PVEL-AD dataset, the BaseLine achieves a mAP@0.5 of 71.6% with only 2.7 M parameters and 8.4 GFLOPs, while our proposed GhostViT-YOLOv10n framework with DCGAN-enhanced version further improves detection performance to 93.6% mAP@0.5 with only 2.19 M parameters and 6.6 GFLOPs. On the PV Multi Defect dataset, the BaseLine achieves a mAP@0.5 of 74.0% with 2.71 M parameters and 8.4 GFLOPs, and the optimized framework with DCGAN-augmented configuration further improves performance to 95.4% mAP@0.5 with 2.58 M parameters and 7.7 GFLOPs. These results demonstrate the effectiveness of combining lightweight architectural optimization with generative augmentation for improving rare defect representation and multi-scale photovoltaic defect detection. To validate practical deployment feasibility, the optimized framework was deployed on a Raspberry Pi 5 using ONNX Runtime under CPU-only conditions. The deployed model achieved an average inference time of 43.05 ms and a real-time processing speed of 23.23 FPS while maintaining moderate CPU utilization and stable thermal behavior. These deployment results demonstrate the suitability of the proposed framework for lightweight edge-oriented photovoltaic inspection applications without requiring GPU acceleration. All evaluations were conducted exclusively on real test datasets, while synthetic samples were used only during training to improve data diversity and rare defect representation. Overall, the proposed framework provides a balanced solution that combines detection accuracy, computational efficiency, lightweight edge deployment capability, and generative augmentation for practical photovoltaic defect inspection applications with potential suitability for future drone-assisted inspection scenarios. Full article

The rapid global expansion of photovoltaic capacity, now exceeding 1 TW, has transformed solar power forecasting from an engineering problem into a financially critical investment decision. Yet virtually all published forecasting studies optimise statistical accuracy metrics without translating improvements into monetised operational value. This paper introduces a unified cost–benefit framework that maps forecast errors across three operationally distinct time horizons onto imbalance costs, arbitrage revenues, and AI deployment costs. The economic conclusions are grounded in Romanian Balancing Market conditions (mean up-regulation price λ⁺ ≈ 85 €/MWh, mean down-regulation price λ⁻ ≈ 42 €/MWh; 15 min settlement interval), a five-year dataset (2018–2022) from a 10 MW utility-scale PV installation in Romania, and an annual AI system cost of 36,000 €/MW decomposed into data infrastructure, cloud GPU compute, and model-monitoring personnel. A Temporal Fusion Transformer ensemble, benchmarked against CNN-LSTM, Informer, and smart-persistence baselines, achieves a 0.38 Skill Score at the day-ahead horizon and a 0.28 Value Score, translating to a net economic benefit of €142,000 per installed MW per annum after full AI system cost deduction. While the framework is designed to be reusable across markets, all reported economic values are specific to the stated Romanian market parameters and should be recalibrated for other regulatory jurisdictions. Full article

Photovoltaic (PV) module faults, such as hotspots, diode short circuits, occlusions, and shadows, degrade power generation efficiency and safety. Existing manual inspection and single-modality methods show limited robustness under complex conditions, especially with illumination variations and weak thermal responses, while most deep learning approaches fail to exploit the complementarity of visible and infrared modalities. To address this issue, a dual-modality visible–infrared fusion framework based on YOLO11 is proposed, integrating a multi-scale pyramid pooling and dilated convolution module (MSPPD), a gradient-aware fusion module (GAFusion), and a dynamic convolution and element-wise scaling detection head (Detect-DEhead). GAFusion enhances cross-modal structural consistency and reduces feature misalignment and information loss during fusion by introducing gradient-aware feature interaction. Shape-IoU loss is employed to improve localization accuracy. The proposed method improves mean average precision (mAP)@0.5 from 86.7% to 88.1%, while reducing parameters, computational cost, and model size from 4.3 M to 3.7 M, 11.42 GFLOPs to 9.37 GFLOPs, and 9.1 MB to 7.9 MB, respectively. With Shape-IoU, mAP@0.5 reaches 88.4%, and recall increases from 78.5% to 84.9%. Experiments on the FLIR Thermal dataset achieve gains of 2.2%, 1.6%, and 2.7% in precision, recall, and mAP@0.5. The method achieves an effective trade-off between accuracy and efficiency for intelligent PV module inspection. Full article

Ultra-large-scale offshore photovoltaic (PV) installations require efficient and reliable construction-phase inspection to ensure installation integrity and compliance with engineering specifications. As the deployment scale expands to thousands of platforms and millions of photovoltaic modules, conventional manual inspection becomes labor-intensive, time-consuming, and increasingly prone to omission errors. This study presents an autonomous inspection framework based on AI-driven computer vision for the detection and localization of missing photovoltaic modules in offshore PV systems. The proposed framework integrates high-resolution UAV-acquired RGB imagery, YOLOv8-based object detection, geographic coordinate transformation, spatial deduplication, and deterministic grid-based indexing to convert aerial observations into structured engineering inspection records. Each detected missing module is automatically assigned a unique platform identifier together with row–column coordinates, enabling engineering-level localization while eliminating redundant detections caused by overlapping UAV imagery. The proposed framework was validated using a dataset comprising 2800 annotated UAV images collected from a 1 GW offshore photovoltaic project. The experimental results revealed a recall of 96.15%, an F1-score of 98.04%, and a manual verification consistency of 96.83%. Geographic deduplication eliminated duplicate grid records, while the average processing time of 1.12 s per image demonstrates the computational feasibility of the framework for large-scale offshore deployment. The results confirm that integrating deep learning-based visual detection with geographic spatial mapping enables reliable, scalable, and engineering-oriented verification of missing photovoltaic modules during construction-phase inspection, thereby supporting standardized and data-driven acceptance workflows for large-scale renewable energy infrastructure. Full article

As a critical component of sustainable energy systems, the efficient maintenance of photovoltaic (PV) panels is essential. While deep learning is an important approach for PV panel defect detection, the high complexity of existing models and their substantial computational demand make deployment on edge platforms difficult. This paper studies an acceleration method for photovoltaic panel defect detection on the Zynq-7020 heterogeneous platform. We design DARE-YOLO, a lightweight network for photovoltaic panel defect detection, together with a Zynq-based accelerator. In DARE-YOLO, we introduce RepConv and a lightweight single-path backbone to reduce the memory bandwidth overhead caused by multi-branch structures. We further design a Dilated Context Block (DCB) and a Dual-scale Decoupled Head (DDH), which effectively improve the detection accuracy of DARE-YOLO. On the Zynq platform, we develop the accelerator through a mixed fixed-point quantization strategy, a custom convolution IP core, and pipeline unrolling. These optimizations reduce data access latency, improve computational parallelism, and increase computational throughput. Experimental results show that DARE-YOLO achieves 93.84% mAP@0.5 with only 6.4 M parameters. The accelerator has a total on-board power consumption of only 1.95 W, while delivering a throughput of 37.5 GOPS, an energy efficiency of 19.23 GOPS/W. The image inference latency is 661.3 ms. This low-power, high-efficiency co-design paradigm ensures the long-term reliability of renewable energy facilities. Full article

To overcome the challenges of processing soft-brittle potassium dihydrogen phosphate (KDP) crystals, this study proposes a back-propagation (BP) neural network model for the rapid prediction of the ion beam removal function using Faraday cup scanning data (a method that measures the spatial distribution of ion beam current density). By correlating current density measurements with point etching experiment results, the model accurately maps both the linear relationship (R² = 0.98) between peak removal rate and peak current density, and the non-linear relationship between the full width at half maximum (FWHM) of the beam and the removal function. The predicted removal function demonstrates high accuracy, with a volume removal rate error of just 2.56% compared to experimental results. Furthermore, this method drastically reduces calculation time from approximately 2 h (required by the conventional point-etching experiment, which involves iterative vacuum cycling, etching, and ex situ interferometry) to just 2 min, significantly improving efficiency. Applied to the ion beam polishing of a 50 mm × 50 mm × 10 mm KDP crystal, the model proved highly effective. The surface figure error was corrected from an initial 0.298λ peak-to-valley (PV) and 0.0496λ root-mean-square (RMS) to 0.167λ PV and 0.036λ RMS, where λ (632.8 nm) is the wavelength of the He-Ne laser used for interferometric surface measurement, achieving a convergence ratio (defined as the ratio of initial PV to final PV) of 1.78. This research provides a high-efficiency, high-precision technical solution for manufacturing KDP components for inertial confinement fusion (ICF) applications. Full article

Rooftop photovoltaic (PV) deployment in historic European cities is often treated as an energy-maximization task, with limited attention to distributive equity and cultural heritage constraints. Linking rooftop PV deployment to sustainable development requires screening approaches that address not only renewable electricity generation, but also social inclusion, resource efficiency, and the protection of cultural heritage. This study presents an open-data, GIS-based screening framework to support Renewable Energy Community targeting in Bologna, Italy. Roof polygons from OpenStreetMap (51,950 roofs) were combined with a cultural-heritage Web Feature Service (892 assets) to classify roofs as protected and excluded (645 roofs), heritage-sensitive within a 25 m buffer (4352 roofs), or unconstrained. A conservative PV proxy estimated usable roof area and annual generation using fixed system parameters and a PVGIS-derived specific yield (1359.39 kWh kWp⁻¹ yr⁻¹). Social conditions were represented with the composite fragility index and 2024 population totals to compute normalized per capita PV potential. Three prioritization strategies were compared: S1 (technical opportunity), S2 (equal-weight energy–equity score), and S3 (S2 moderated by heritage concentration). After heritage conditioning, the citywide potential totals 7.38 million m² usable area (≈1.11 GWp; ≈1.51 TWh·yr⁻¹), and per capita potential ranges from 2.33 to 4.55 MWh·cap⁻¹·yr⁻¹. Equity weighting shifts priority toward more vulnerable areas, while heritage-dense areas remain lower-ranked. The workflow outputs transparent rules, priority maps, and reproducible layers for sustainability-oriented municipal decision-making by connecting urban decarbonization, energy justice, efficient use of existing rooftops, and heritage-compatible renewable energy planning. Full article

Distributed photovoltaic (PV) systems play an important role in supporting low-carbon and sustainable distribution network development; however, their high-penetration integration can cause nodal overvoltage, as midday PV generation often exceeds local demand and net reverse power flows accumulate along feeders. Existing studies mainly focus on technical mitigation, while entity-level responsibility quantification and its translation into differentiated mitigation tasks remain insufficiently explored. This paper proposes an overvoltage mitigation method integrating Shapley-value-based responsibility quantification with active–passive-responsibility-driven task allocation. Reverse power flow quotas are determined using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) by considering the differentiated impacts of PV entities on power flow distribution and nodal voltage response. The load-weighted voltage deviation index is adopted as the coalition cost function, and the Shapley value is used to quantify each entity’s overvoltage responsibility share. Active and passive responsibilities are characterized through the net reverse power ratio and reactive power–voltage sensitivity, respectively, establishing a mapping mechanism from responsibility shares to active power curtailment and reactive power regulation tasks. A multi-objective optimization model that jointly considers line losses, PV curtailment, fairness deviation, and task-allocation deviation is constructed and solved using the whale optimization algorithm. Case studies on the IEEE 33-bus system show that the proposed method reduces the maximum network voltage from 1.25 pu to 1.052 pu and eliminates all overvoltage violations. Compared with a cluster-based scheme, the 24-h cumulative line loss is reduced by 30.6%, and the fairness deviation is significantly lowered, thereby supporting the sustainable, economical, and equitable operation of distribution networks with high distributed PV penetration. Full article

Under China’s carbon peaking and carbon neutrality goals, building-integrated photovoltaics (BIPV) are a key option for low-carbon urban transition. However, how urban morphology shapes effective PV potential in severe cold cities remains poorly understood. Previous work focuses on single buildings or citywide resource mapping and rarely yields actionable planning controls. Using Harbin as a case, this study integrates GIS with explainable machine learning to relate building-cluster morphology to effective PV generation potential. An XGBoost model is interpreted with SHAP and partial dependence analysis to quantify factor importance and response ranges. Building density (BD) and floor area ratio (FAR) are the dominant predictors, ranking above the other morphological indicators. PV density peaks at moderate BD (≈0.20–0.35) under medium-to-high development intensity, and it increases when building distribution is moderately even (NNI ≈ 1.3–1.5) with moderate height differentiation. These coupled responses define a Morphological Sweet Spot, indicating that higher PV performance depends on coordinated morphological configurations rather than on any single parameter. The framework provides an interpretable, data-driven basis for building-cluster BIPV assessment and for translating model outputs into morphology-based planning guidance for low-carbon renewal in severe cold regions. Full article