Driven by global energy transition goals, the large-scale development of offshore wind power imposes rigid requirements for professionalism, standardization, and timeliness on feasibility study reports (FSR). Traditional manual compilation and existing automated methods fail to meet these requirements due to interdisciplinary complexity, poor process controllability, and insufficient domain adaptation. To address these challenges, this paper proposes a configurable and interpretable offshore wind FSR generation system built on a three-tier framework that encompasses “data support, process orchestration, and quality assurance”. The system integrates a YAML-based workflow architecture, multi-level prompt engineering, and a comprehensive evaluation system. Notably, the introduced “Cyclic Aggregation Mode” enables the iterative generation and logical summarization of multi-subproject data, effectively distinguishing this system from traditional linear text generation models. Experimental results demonstrate that the proposed “Retrieval-Augmented Generation (RAG) + Large-scale Language Model (LLM) + Workflow” system outperforms baseline models with key metrics including semantic consistency (0.6592), information coverage (0.3908), structural compliance (0.5123), and an overall score (0.5965). Ablation studies validate the independent contributions of the RAG and Workflow components, thereby establishing the “RAG + LLM + Workflow” paradigm for intelligent professional document generation. This work addresses core challenges related to controllability, accuracy, and interpretability in high-stakes decision-making scenarios while providing a reusable technical pathway for the automated feasibility demonstration of offshore wind power projects.
Marine renewable energy systems, particularly offshore wind and photovoltaic (PV) installations, generate large volumes of heterogeneous maintenance texts. However, the resulting knowledge remains fragmented due to dispersed sources, diverse formats, and domain-specific terminology. To address these challenges, this study proposes a large-scale language model assisted methodology for constructing a multi-source heterogeneous knowledge graph for intelligent operation and maintenance (O&M). The method integrates unified document preprocessing, domain-oriented prompt engineering, large-scale language model–based entity and relation extraction, and multi-level entity normalization. It systematically transforms unstructured documents (e.g., standards, procedures, manuals, inspection records, and environmental reports) into structured triples, enabling the construction of a dynamically evolving O&M knowledge graph. A rigorous ablation study on real-world offshore wind and PV datasets demonstrates that the proposed workflow exhibits exceptional robustness against OCR noise (e.g., scanned artifacts, stamps, and signatures) and substantially improves extraction volume, accuracy, and coverage compared with traditional methods. In particular, combining high-quality preprocessing and optimized prompts yields the most reliable and semantically coherent results. The study provides a practical technical pathway for automated knowledge management in marine renewable energy and offers a foundation for future applications in intelligent diagnostics, predictive maintenance, and digital-twin systems.
The objective of this study is to conduct a review of recycled-carbon-fibre (rCF) wind turbine blades’ feasibility, through a comparison of global and Australian wind sector waste, and a comparison of virgin-carbon-fibre (vCF) with rCF wind turbine blades’ greenhouse-gas GHG-emissions, and, recommend an approach for carbon-fibre CF-use in the fledgling Australian offshore wind industry, based on global-warning-potential GWP. This study assesses the life-cycle GHG-emissions of virgin-carbon-fibre wind turbine blades versus recycled-carbon-fibre wind turbine blades, in both non-structural and structural configurations. All production, use and recycling is assessed in terms of a West Australian context, in which the functional unit is three turbine blades used on an onshore wind farm, towards potential applicability for (as yet, non-existent) offshore WA fields. An approach incorporating a GaBi/Sphera database-study provides a timely screening/preliminary study, in which it was found that non-structural recycled carbon fibre wind turbine blades had very similar GHG emission levels compared to standard virgin carbon fibre blades, with sensitivity analysis revealing that in worst-case scenarios, non-structural carbon fibre has higher GHG emissions. Structurally recycled carbon fibre blades performed significantly better than standard virgin carbon fibre wind turbine blades with a 56% reduction in GHG emissions; savings were not affected significantly by parameter changes during sensitivity analysis. It is evident that recycled-carbon-fibre can significantly reduce wind turbine blades’ GWP and contribute to the circular economy in the fledgling West Australian offshore-wind-turbine sector.
