As more offshore wind energy projects are implemented, the risk of interactions between farms becomes more pronounced. While reduced surface roughness over water enhances airflow stability, it can also extend wake effects on downstream turbines. The study aims to enhance the understanding of wake interactions and efficiency variations based on the distance between neighboring farms. To assess the impact of neighboring farms across different scenarios and features, a methodology is developed to achieve computational optimality using an open-source Python-based library, PyWake, then verified by a well-established CFD software, Meteodyn. Then, the methodology is applied to a Brazilian offshore wind project currently under licensing as a reference point. The results indicate a 1–3% reduction in Annual Energy Production following the current Brazilian regulation for onshore projects of 20 times the blade tip height, as the minimum distance. This reduction translates to an approximate 3% increase in the Levelized Cost of Energy and a nearly 24% decrease in Net Present Value. These findings are crucial for offshore wind energy planning and its sustainable growth, indicating the need to define a minimum distance for the regulatory bodies. This would not only avoid future disputes but also enhance investor confidence.
Background
The transfer of offshore wind farm workers between transport vessels and wind turbines is a hazardous operation with a disproportionately high occurrence of "high potential" incidents. Motion sickness has been reported to affect offshore wind farm worker well-being, and has been identified as a job demand, especially during crew transfer and ladder-climbing operations.
This scoping review sought to determine the extent to which current research defines, describes, and quantifies MS among offshore wind farm workers and to identify relevant research gaps.
Methods
Using terms related to motion sickness and offshore wind farm operations, searches were conducted of the PubMed, Scopus, and Web of Science databases. Studies published in English between 1990 and 2024 were included.
Results
795 articles were retrieved, of which 11 articles met the inclusion criteria. The included articles describe MS as a job demand but do not clearly define it in the research context. Consequently, it remains unclear which symptoms of MS constitute a job demand and how workers are affected. Additionally, indications of motion sickness prevalence are required, using a clear definition which accounts for the wide range of subjective symptoms other than vomiting.
No research appears to have been carried out where motion sickness among wind farm workers has been studied as a broad occupational health issue within the offshore wind energy sector.
Conclusions
This review identifies significant research gaps concerning motion sickness among offshore wind farm workers. Motion sickness-related issues have either been overlooked, studied in isolation, or insufficiently addressed. These issues constitute empirical, methodological, and knowledge gaps, necessitating a need for systematic studies that address these research gaps in the context of the offshore wind energy sector.
Subsea power cables are crucial for transmitting electrical power between offshore installations, islands, and onshore infrastructure. The demand for these cables has surged with the expansion of offshore wind farms. Despite mechanisation, divers are still needed for tasks such as installation, inspection, and remedial work, facing hazards like entanglement, equipment damage, and those to the environment. Therefore, analyzing accidents in diving operations during subsea cable installation is essential to develop safety measures that protect divers and ensure successful installations. This document reports an analysis of the hazards and accident events linked to diving operations during subsea cable installation. Few risk assessments of these operations have been made publicly available.
Various methods can be used to analyze diving accidents, but this document reports on the use of the Accident Anatomy (AA) method. The AA method combines fault trees and cause-consequence diagrams to map accident causes and consequences. In the AA method, evidence-based (post-accident) analysis is used jointly with predictive analysis to identify deviations from normal conditions that could lead to accidents.
To exhaust the identification of hazards, the AA method is additionally powered by an error mode classification checklist, which classifies errors that produce similar effects on a system. Analysts used this checklist to identify hazards for each basic diving operation task identified.
As a data source, 163 documents were analyzed, including accident records, regulations, manuals, and scientific papers. Basic tasks associated with diving operations are identified, along with hazards for each task. Predictive analysis identifies potential events and unwanted consequences when normal conditions (specified in safety procedures and specifications) deviate. The unwanted consequences that were found include delays, technical problems, injuries, and fatalities. Ultimately, safety measures are identified for each basic task to reduce the effects of hazards.
Large and remote offshore wind farms (OWFs) usually use voltage source converter (VSC) systems to transmit electrical power to the main network. Submarine high-voltage direct current (HVDC) cables are commonly used as transmission links. As they are liable to insulation breakdown, fault location in the HVDC cables is a major issue in these systems. Exact fault location can significantly reduce the high cost of submarine HVDC cable repair in multi-terminal networks. In this paper, a novel method is presented to find the exact location of the DC faults. The fault location is calculated using extraction of new features from voltage signals of cables' sheaths and a trained artificial neural network (ANN). The results obtained from a simulation of a three-terminal HVDC system in power systems computer-aided design (PSCAD) environment show that the maximum percentage error of the proposed method is less than 1%.