Mooring Optimization of Floating Offshore Wind Turbines Using Genetic Algorithm

The renewable energy industry through offshore wind has been growing rapidly with increasing project capacities. To accommodate this huge energy transition demand, the projects are moving towards open sea and deeper water. In line with this trend, various design development and innovation on Floating Offshore Wind Turbine (FOWT) technology are progressing to improve the feasibility as well as the economy of the projects. Mooring system is part of major costs components in Floating Offshore Wind (FOW). Reducing mooring loads is desirable to create more room for optimization of the mooring configuration. This could lead to opportunity for saving on the capital costs, more environmentally friendly solutions through reducing the mooring footprint, as well as better mooring integrity. The innovative polymer spring component has been introduced in the mooring industry that enables significant reduction on the mooring loads if it is properly designed. In this paper, the applicability of polymer spring component in FOW applications is further investigated through mooring case studies utilizing comprehensive design analysis. For this purpose, the VolturnUS-S semi-submersible floating platform design is selected. This reference floating offshore wind turbine is developed by the University of Maine – USA to support the International Energy Agency (IEA) 15-MW reference wind turbine. In total, there are eight individual mooring designs analyzed in this study, which are rooted from two very different base mooring systems. The first base mooring system is a ‘conventional’ steel wire with ground chain semi-taut mooring system. While the second one is based on a ‘more novel’ nylon-based taut mooring system with three different declination angles. For each base mooring configuration above, an alternative mooring configuration is proposed by having the polymer spring as part of the mooring component. Dynamic mooring analysis is performed for both the base and the alternative mooring configurations using the same design load cases, and the results are compared. The mooring assessment is performed using fully coupled dynamic time-domain analysis tools Orcaflex with the hydrodynamic database of the platform generated by the Orcawave. All the proposed mooring systems are designed in compliance with the applicable industrial design codes for the Ultimate Limit State (ULS), while meeting the target allowable platform offsets. It should be noted that this paper focuses on optimizing the mooring system for the ULS scenario only. It is acknowledged that Fatigue Limit States (FLS) may be governing in some projects. The expansion of this work to FLS, however, is outside the scope of this paper but will be explored in future papers. Offshore Ulsan, eastern sea of South Korea is selected as the basis for the operational location for all the case studies presented in this paper. Ulsan and its offshore region are being intensively promoted as one of the hubs for the FOW projects. Hence, the results of this mooring study may provide useful input for the developers of these wind farms to further optimize their mooring systems. The benefits of the polymer spring as a mooring component are demonstrated through the case studies. Promising prospects of combining the polymer spring with synthetic nylon rope for FOW mooring system applications are highlighted. At the end of this paper, conclusions and recommendations for future work are drawn.

Investigation of mooring breakage impact on dynamic responses of a 15 MW floating offshore wind turbine

An equation-based method for fully coupled analyses of floating offshore wind turbine based on Modelica

While floating offshore wind farms have predominantly concentrated on shallow water applications thus far, it is only a matter of time before they extend into deeper waters, especially in regions without large shallow continental shelves. This evolution is expected to follow the trend of the offshore oil and gas industries. Studies have been underway to explore the feasibility of offshore wind farms in deeper waters. Therefore, further research into Floating Offshore Wind (FOW) technologies for deep-water applications, including advancements in mooring technology, will significantly benefit the offshore wind farm industry. This study aims to further investigate the applications of taut leg mooring technology in mid to deep-water regions. The primary objective is to examine the fatigue behavior of this deep-water mooring system as it relates to water depths, inclination angles, and levels of pre-tension. To accomplish this, sixty scenarios are analyzed, encompassing a range of these parameters to address industry-relevant situations for the near future of floating offshore wind farms. Each scenario is rigorously assessed for both the Ultimate Limit State (ULS) and the Fatigue Limit State (FLS). A streamlined fatigue assessment methodology has been developed and implemented to efficiently evaluate a substantial number of cases while maintaining a high level of accuracy. The findings from these comprehensive case studies reveal general trends and provide valuable insights into the performance of taut leg mooring systems. The study was further enhanced by incorporating design constraints derived from the wind farm layout and spatial efficiency considerations. A typical floating offshore wind farm project utilizes multiple floating offshore wind turbines (FOWTs) to maximize wind energy generation. Increasing the number of turbines installed within the leased area enhances the spatial efficiency of the wind farm. However, optimizing this spatial efficiency is a complex task, as it involves navigating various disciplines and design parameters. This study builds upon the established minimum spacing requirements between turbines, which are dictated by wind turbulence, to introduce further constraints on the selection of mooring configurations and their associated footprints. Additionally, it explores the implications of these minimum spacing guidelines on the overall design of deepwater mooring systems. It was observed that most promising mooring configurations, in terms of spatial efficiency, are predominantly influenced by fatigue concerns. Certain selected cases exhibited significantly low fatigue life, requiring the implementation of mitigation strategies to address these concerns. Several approaches were discussed, including increased component size, the use of additional mooring lines to share loads, and the use of fatigue mitigation devices, such as polymer springs. Notably, since fatigue loading is primarily influenced by both the magnitude of dynamic tension variations and their frequency of occurrence, the findings highlight that mitigating line dynamics is significantly more crucial than simply lowering tension levels. Therefore, the polymer spring emerges as the most effective solution for addressing fatigue issues in mooring systems explored in this study. A high-level cost assessment was conducted based on key selected cases to evaluate the cost trends in deep water mooring systems across different alternative mooring configurations and their associated fatigue mitigation strategies. In particular, the study explores the potential cost savings offered by a three-line mooring configuration that incorporates polymer springs for fatigue mitigation. The key findings and recommendations from this analysis are summarized in the concluding section of this technical paper.

This work focuses on examining the dynamic behavior of large floating offshore wind turbine (FOWT) exposed to extreme (wind and wave with 50-year return period) and accidental (mooring line breakage) loadings. The FOWT considered is 15 MW reference turbine supported on semi-submersible platform stationed using catenary moorings. As the mooring configuration greatly affects the response of FOWT, two different mooring configurations namely non-redundant (three-line) and redundant (six-line) systems were studied and compared. The coupled multi-body dynamic system was solved using Openfast. When simulating the mooring line failure, both the operating and extreme loading conditions were considered. Failure of one mooring was considered at a time. The response of the coupled system due to breakage of the mooring indicates high displacements in surge and sway directions in comparison to the intact system especially for the non-redundant mooring system. Furthermore, change in platform yaw angle and increased tension in the other intact mooring lines were observed. The findings from this study will be helpful in accidental limit state design and preventing failure of similar large FOWT systems. In addition, insights into using non-redundant and redundant mooring configurations for such large structures with respect to extreme and accidental loadings were also discussed.

In off-shore wind turbine, it is difficult to determine the risk of accident caused by the mooring destruction through experiment. In this paper, the authors discuss the risk, with the case of a drifting ship wanders into the wind farm. In the design of a floating offshore wind turbine (FOWT), drift of a FOWT is considered as a serious failure mode and the mooring system must be designed to avoid the failure. The failure of mooring line is not initiated just by extreme environmental load but can be initiated by collision with a drifting ship, which enters the wind farm. This phenomenon is difficult to investigate by a tank experiment. So far, little knowledge exists about the phenomenon. In this research, a simulator to reproduce the collision process of a FOWT and a drift ship and a progressive drift of FOWTs in a wind farm was developed. Using this simulator and statistics of drift incidents of a ship, a procedure to evaluate risk of progressive drifts in a wind farm was established. In that case, second accident that a wind turbine which has started drifting caused by the drifting ship collides with one another wind turbine is expected. As a result, the risk mainly depends on the risk of drifting caused by a large displaced ship. In addition, the risk partly depends on the arrangement of wind farm.

Floating offshore wind turbines (FOWTs) have become a promising solution for harnessing wind energy in deeper seas. However, the complex interplay between FOWT layout, mooring line patterns, and wake effects significantly influences the overall performance of a floating offshore wind farm (FOWF). This paper proposes a novel optimization methodology that integrates mooring line constraints into the FOWF layout optimization process. The wake-induced power deficit is considered, whereas the vortices are neglected. The new method considers the constraint areas for each FOWT, which are defined based on both mooring line buffer zones and wind turbine buffer zones. By defining constraint areas, the optimization process ensures that FOWTs are optimally positioned while avoiding interference and collisions. By carefully considering the buffer zones, the power potential of FOWFs with three-line, four-line, and six-line mooring configurations can be improved by 122%, 100%, and 78%, respectively. Then, a genetic algorithm is employed to optimize the FOWT positions and mooring line angles simultaneously. The effectiveness of the proposed method is demonstrated through a case study in Guangdong, resulting in a significant 5% increase in power output potential compared to conventional approaches. This research contributes to the advancement of FOWT layout optimization and provides valuable insights for the design and deployment of future FOWFs.

With the complexity of mechanical manufacturing system design increasing, the traditional single-objective optimization method can no longer meet the needs of multi-objective optimization, such as lightweight, high efficiency, low energy consumption and long life. Multi-objective optimization algorithm is widely used in mechanical design and manufacturing process planning because of its good convergence and diversity. Taking robot motion planning as an example, this paper analyzes the application methods of multi-objective optimization algorithm in mechanical structure design in detail, including problem description, multi-objective optimization model construction, algorithm application and performance analysis. Through the application of Genetic Algorithm (GA), this paper shows how the multi-objective optimization algorithm can balance multiple objectives such as path length, energy consumption and obstacle avoidance effect, and realize more intelligent and efficient solutions in robot motion planning. Performance analysis shows that GA is superior to single-objective optimization and random search algorithm in optimization effect, computational efficiency and stability. In addition, this paper also discusses the influence of the change of target weight and algorithm parameters on the optimization results, which provides valuable reference for future algorithm optimization and practical application. Multi-objective optimization algorithm shows obvious advantages in mechanical manufacturing system, which provides new ideas and methods for realizing more efficient, more environmentally friendly and more intelligent mechanical manufacturing design.

Floating Offshore Wind Turbines (FOWTs) represent a promising avenue for harnessing wind energy in deep-water regions through the use of mooring systems. However, mooring lines account for over 20% of the total construction cost and require careful optimization to ensure both structural stability and economic feasibility. One effective strategy for enhancing mooring performanceis the strategic placement of clump weights along the lines. While beneficial, these clump weights are vulnerable to damage or detachment under harsh marine conditions, potentially compromising the structural integrity and reducing the service life of FOWTs. To address this challenge, we propose a deep learning-based method for real-time detection of clump weight loss using a Long Short-Term Memory (LSTM) network. The training data for the LSTM model are generated using high-fidelity simulations conducted in the open-source software OpenFAST, based on the 5 MW OC3-Hywind spar-buoy FOWT model equipped with clump-weighted mooring lines. The proposed LSTM classifier achieved 86% accuracy on a held-out test split and 73% on a completely unseen dataset, demonstrating both effectiveness in detecting clump weight loss and generalization capability for real-time condition monitoring in FOWT mooring systems.

The studies on floating offshore wind turbines (FOWTs) have been increasing over recent decades due to the growing interest in offshore renewable energy. The present paper proposes a barge platform with four moonpools to support the Technical University of Denmark 10 MW wind turbine for a designed water depth of 60 m. A 4 × 2 mooring system with eight mooring lines is also proposed for the barge platform. The main dimensions of the barge platform are optimally selected with respect to its preliminary hydrodynamic properties and potential financial benefit. The proposed barge-type FOWT is then demonstrated to be aligned with the DNV standard requirements in terms of its intact and damage stability. Furthermore, coupled time-domain simulations are conducted for the proposed barge FOWT with mooring under the selected environmental and operational conditions by using Simo-Riflex-AeroDyn (SRA). Through decay test simulations, the natural periods of the barge-type FOWT are demonstrated to be within the DNV recommended ranges. The proposed mooring system is also benchmarked with the 3 × 3 mooring concept that was used for a 3 MW barge-type FOWT installed in Kitakyushu. The response magnitudes of the barge platform and mooring line tension are similar to both mooring systems, and thus the 4 × 2 mooring system is preferred due to its lower cost. In addition, the proposed barge platform is preliminarily demonstrated to be able to survive for the 50-year extreme environmental conditions under parked wind turbine status, as well as the normal environmental conditions under the operating status.

The stability of mooring systems in floating offshore wind turbines (FOWTs) is essential, as any deterioration affects platform performance and overall efficiency. Machinelearning monitoring of FOWTs requires comprehensive dynamic response data derived from various mooring systems. Data collection should encompass a variety of health states, operational scenarios, and metocean conditions. Data is scarce in practice, especially concerning damage-associated information. Additionally, the existing imbalanced dataset lacks clear labels, making it difficult to differentiate between healthy (majority) and damage-associated (minority) instances. We propose a novel deep generative model (DGM) to efficiently deal with the challenge of unsupervised labeling of imbalanced data in the initial stage. It will subsequently be used to augment the labeled data from the previous stage and generate damage-associated data for a new (target) mooring system based solely on the healthy records of that system. The conditional hierarchical variational autoencoder (CHVAE) is developed using a diffusion probabilistic architecture during a pretrain-finetune training procedure. During the pre-training stage, trained on imbalanced, unlabeled data, we employed an unsupervised feature engineering approach in the latent space created to identify the healthy (majority) and damaged (minority) data. In the fine-tuning stage, the nonlinear relationship between healthy and minority-damaged responses of a mooring system is obtained, considered as the source domain across diverse sea states. Subsequently, by employing the healthy data from the target mooring system, the CHVAE can generate real-scale damaged responses of the system across diverse operational and environmental conditions. An analysis is conducted to evaluate the similarity between the simulated records produced by OpenFast on the OC4-DeepCWind FOWT benchmark and those generated by the CHVAE framework, utilizing visual, statistical, and behavioral methods. Furthermore, the proposed DGM’s performance is first assessed using MNIST benchmark image dataset to evaluate its effectiveness for unsupervised labeling and data augmentation. The generated records for unobserved random sea states closely resemble real-world dynamic behaviors during downstream binary classification, illustrating the effectiveness and versatility of the proposed DGM, CHVAE.

The optimal placement method of sensors is carried out based on improved genetic algorithm for solving the sensors optimal placement problem of the health monitoring system for long-span railway bridge. Dual structure coding method is introduced to improve the individual encoding method in the Genetic Algorithm. Adaptive partial matching crossover and inversus mutation method is adopted in the optimal preservation strategy, and the probabilities of crossover and mutation are changed automatically according to the fitness value for obtaining the global optimal solution of the sensor placement. So some defects in other Genetic Algorithm applied in the optimal placement of sensors for major bridge structure, such as slow convergence speed and easily falling into local optimum etc., are overcome, and the convergence is ensured. Then the optimal sensors placement of the health monitoring system for one certain long-span railway steel truss cable-stayed bridge is taken as the example to verify the proposed improved genetic algorithm. The result shows that the proposed method has better global optimization, computational efficiency and reliability in compare with the Simple Genetic Algorithm and General Genetic Algorithm, and can be applied to the actual railway cable-stayed bridge health monitoring system for the optimal sensors placement.

The number of installed offshore wind turbines is continuously growing worldwide in recent years. Offshore wind farms are generally located near the coast close to traffic lanes and are exposed to the risk of collisions from visiting and passing ships. Potential consequences of collisions may vary from local structural damage to the detachment of turbine nacelles and rotors, and even tower collapse and capsizing of the turbine platform, causing significant economic loss and fatalities.This paper investigates ship collision responses of a semi-submersible floating offshore wind turbine (FOWT), i.e. the OO-STAR floater with the DTU 10 MW blades, using the nonlinear finite element code USFOS. The OO-STAR floater is made of post-tensioned concrete designed by Dr. techn. Olav Olsen. The striking ships are selected to be a modern supply vessel of 7500 tons and a shuttle tanker of 150,000 tons, representing respectively service/coastal merchant vessels and large passing vessels. Modelling of the FOWT in USFOS is described in detail including the OO-STAR floater, the DTU 10 MW turbine blade, the turbine tower, and the mooring system. The modelled hydrodynamic loads include buoyancy loads and motion induced radiation loads using the Morrison equation. The effects of external waves and currents are assumed to be small and ignored in all directions. Eigenmode analysis of the turbine model is performed to verify the established model.Global collision response analyses of the FOWT were performed in both parked and operating conditions. The ship resistance is modelled as nonlinear springs in USFOS containing force displacement curves simulated in LS-DYNA. In operating conditions, wind loads are introduced including wind thrust loads and wind induced torque to rotate the turbine blades. The changes of upstream wind speeds and rotor/wake interactions during collisions are neglected. The results are discussed with respect to energy absorption of the ship and the FOWT, and structural responses of the FOWT including global motions, nacelle accelerations, tower clearance, tower vibrations, and responses of the mooring system.

The advancement towards utilizing renewable energy sources is crucial for mitigating environmental issues such as air pollution and climate change. Offshore wind turbines, particularly floating offshore wind turbines (FOWTs), are developed to harness the stronger, steadier winds available over deep waters. Accurate metocean data forecasts, encompassing wind speed and wave height, are crucial for offshore wind farms’ optimal placement, operation, and maintenance and contribute significantly to FOWT’s efficiency, safety, and lifespan. This study examines the application of three machine learning (ML) models, including Facebook Prophet, Seasonal Autoregressive Integrated Moving Average with Exogenous Factors (SARIMAX), and long short-term memory (LSTM), to forecast wind speeds and significant wave heights, using data from a buoy situated in the Pacific Ocean. The models are evaluated based on their ability to predict 1-, 3-, and 30-day future wind speed and wave height values, with performances assessed through Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) metrics. Among the models, LSTM displayed superior performance, effectively capturing the complex temporal dependencies in the data. Incorporating exogenous variables, such as atmospheric conditions and gust speed, further refined the predictions.The study’s findings highlight the potential of machine learning (ML) models to enhance the integration and reliability of renewable energy sources through accurate metocean forecasting.

This paper presents the development, testing and validity of an active mooring simulator for basin testing of scale model floating offshore wind turbines (FOWTs). The mooring simulator uses closed loop force control and a lookup table to actuate linear and non-linear mooring response for a semi-submersible 11MW FOWT at 1:56.7 scale. Presented is the validation of the system performance on the bench and against traditional springs in the basin. Three mooring systems were actuated by the mooring simulator: 1) Linearized polyester semi-taut mooring, 2) Non-linear polyester semi-taut mooring, 3) Non-linear catenary mooring. Characterization and verification of the simulated mooring system was done by direct comparison to an actual spring system. Validation results include linear actuated free decays and irregular wave tests in comparison to traditional spring setup. Non-linear mooring responses were also simulated under irregular sea states specified by ABS guidelines. The control system uses a proportional-integrator (PI) controller with active force feedback and a lookup table. The force-displacement relationship of the represented mooring systems includes static and dynamic stiffness components developed from OrcaFlex simulations. This relationship is fed into the controller for the active mooring system via a lookup table. The actuation of non-linear force displacement relationships allows for basin test results to be more accurate to the increasingly non-linear mooring systems being developed.