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Biofouling, the accumulation of marine organisms on submerged surfaces, presents significant operational challenges across various marine industries. Traditional detection methods are labor intensive and costly, necessitating the development of automated systems for efficient monitoring. The study presented in this paper focuses on detecting biofouling on tidal stream turbine blades using camera-based monitoring. The process begins with dividing the video into a series of images, which are then annotated to identify and select the bounding boxes containing objects to be detected. These annotated images are used to train YOLO version 8 to detect biofouled and clean blades in the images. The proposed approach is evaluated using metrics that demonstrate the superiority of this YOLO version compared to previous ones. To address the issue of misdetection, a data augmentation approach is proposed and tested across different YOLO versions, showing its effectiveness in improving detection quality and robustness. [ABSTRACT FROM AUTHOR]

This paper addresses the issues of maximum power point tracking (MPPT) and fault-tolerant control in tidal steam turbines under complex marine environments. In order to solve the conflicting problems in the existing sliding mode control between dynamic performance and chatter reduction as well as the use of fault estimation link in the fault-tolerant control, which increases the system complexity, an adaptive non-singular fast terminal sliding mode and adaptive robust fault tolerance method (ANFTSMC-ARC) is proposed. First, a speed controller equipped with adaptive non-singular fast terminal sliding mode control (ANFTSMC) is designed to improve the power capture efficiency under swell disturbances. This design achieves fast convergence and circumvents the singularity problem. Then, a new reach law is proposed based on the adaptive hybrid exponential reaching law (AHERL), which ensures high tracking performance while reducing chattering. In addition, considering that the hydraulic pitch system is prone to failure, a fault-tolerant controller with automatically adjustable gain is designed under the adaptive robust scheme. With the help of Lyapunov theory, the closed-loop system is proved to be uniform and ultimately bounded. Finally, comparative simulation results verify the efficiency of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Biofouling, the accumulation of marine organisms on submerged surfaces, presents significant operational challenges across various marine industries. Traditional detection methods are labor intensive and costly, necessitating the development of automated systems for efficient monitoring. The study presented in this paper focuses on detecting biofouling on tidal stream turbine blades using camera-based monitoring. The process begins with dividing the video into a series of images, which are then annotated to identify and select the bounding boxes containing objects to be detected. These annotated images are used to train YOLO version 8 to detect biofouled and clean blades in the images. The proposed approach is evaluated using metrics that demonstrate the superiority of this YOLO version compared to previous ones. To address the issue of misdetection, a data augmentation approach is proposed and tested across different YOLO versions, showing its effectiveness in improving detection quality and robustness.

Tidal stream turbines (TSTs) harness the kinetic energy of tides to generate electricity by rotating the rotor. Biofouling will lead to an imbalance between the blades, resulting in imbalanced torque and voltage across the windings, ultimately polluting the grid. Therefore, rotor condition monitoring is of great significance for the stable operation of the system. Image-based attachment detection algorithms provide the advantage of visually displaying the location and area of faults. However, due to the limited availability of data from multiple machine types and environments, it is difficult to ensure the generalization of the network. Additionally, TST images degrade, resulting in reduced image gradients and making it challenging to extract edge and other features. In order to address the issue of limited data, a novel non-data-driven edge detection algorithm, indexed resemble-normal-line guidance detector (IRNLGD), is proposed for TST rotor attachment fault detection. Aiming to solve the problem of edge features being suppressed, IRNLGD introduces the concept of "indexed resemble-normal-line direction" and integrates multi-directional gradient information for edge determination. Real-image experiments demonstrate IRNLGD's effectiveness in detecting TST rotor edges and faults. Evaluation on public datasets shows the superior performance of our method in detecting fine edges in low-light images. [ABSTRACT FROM AUTHOR]

The style of the underwater images varies according to the region of the sea. However, Tidal Stream Turbine (TST) rotor images captured in the laboratory environment cannot reflect the real underwater environment in image style, resulting in poor generalization of image signal-based fault detection algorithms. Due to the fixed capture position of the camera, the TST rotor image dataset has a high semantic similarity between images, resulting in content loss in conventional image-to-image translation networks. Meanwhile, the one-to-one translation feature in other works cannot meet our requirements. In this work, a Domain Variable Prior-based Multi-style Transfer Network (DVP-MSTN) is proposed to achieve TST rotor image style augmentation. First, the backbone network is trained using a public paired dataset to acquire prior knowledge of domain variable (Knowledge Acquiring, KA). Next, a Multi-domain Transfer Unit (MDT unit) is introduced to enable the conversion of style representations in low-dimensional space. Finally, the prior knowledge is shared to train the MDT unit by fixing the parameters of the backbone network optimized from the KA process (Knowledge Sharing, KS). In addition, an algorithm based on the dark channel of the image is proposed to improve the transfer of low-contrast features. Specifically, a discriminator is used to discriminate the image dark channel to guide the MDT unit to generate low-contrast style representation conditionally. Meanwhile, color loss is employed to preserve the color feature of the image. By controlling the weights of the style code, this method enables control over the image style transfer process, thereby expanding the variety of image styles in the dataset for the purpose of data augmentation. [ABSTRACT FROM AUTHOR]

Renewable energy generation is increasingly important due to serious energy issues. A Doubly Salient Permanent Magnet Generator (DSPMG) can be an interesting candidate for tidal stream renewable energy systems. However, the special structure makes the system nonlinear and strongly coupled even after Park transformation and involves a larger torque ripple. Previous research mainly focused on model-based control for this machine, which is very sensitive to the parameters. Thus, to control the complex systems stably and accurately, two model-free control algorithms, Active Disturbance Rejection-Based Iterative Learning Control (ADRILC) and Active Disturbance Rejection Control–Iterative Learning Control (ADRC–ILC), are proposed for the current and speed control loops of a DSPMG-based Tidal Stream Turbine (TST), respectively. ADRC–ILC uses ADRC to deal with the external non-periodic speed ripple and adopts ILC to reduce the internal periodic speed ripple. ADRILC employs an iterative method to improve the ESO for the enhancement of the convergence rate of ILC. Considering the variable tidal speed, when the speed is above the rated value, Maximum Power Point Tracking (MPPT) must be changed to a power limitation strategy for limiting the generator power to the rated value and extending the system operating range. Thus, Optimal Tip Speed Ratio (OTSR)-based MPPT (for a low tidal current speed) and Leading Angle Flux-Weakening Control (LAFWC) (for a high tidal current speed) strategies are also proposed. According to the simulation results, the proposed ADRC–ILC + ADRILC has the lowest torque ripple, the highest control accuracy, as well as a good current tracking capability and strong robustness. At the rated speed, the proposed method reduces the torque ripple by more than 20% and the speed error by about 80% compared with PI control: the current difference is limited in 2A. The LAFWC proposed for an excessive tidal current speed is effective in conserving the electromagnetic power and increasing the generator speed. [ABSTRACT FROM AUTHOR]

This paper has developed an operation and maintenance (O&M) model for projected 20 MW tidal stream farm case studies at two sites in the northeast Atlantic in France and at EMEC's Fall of Warness site in the UK. The annual energy production, number of incidents, and downtimes of the farms for corrective and planned (preventive) maintenance strategies are estimated using Monte Carlo simulations that vary weather windows, repair vessel availabilities, and mean annual failure rates modelled by Weibull distributions. The trade-offs between the mean annual failure rates, time availability, O&M costs, and energy income minus the variable O&M costs were analysed. For all scenarios, a 5-year planned maintenance strategy could considerably decrease the mean annual failure rates by 37% at both sites and increase the net energy income. Based on a detailed sensitivity analysis, the study has suggested a simple decision-making method that examines how the variation in the mean annual failure rate and changes in spare-part costs would reduce the effectiveness of a preventive maintenance strategy. This work provides insights into the most important parameters that affect the O&M cost of tidal stream turbines and their effect on tidal energy management. The output of the study will contribute to decision-making concerning maintenance strategies. [ABSTRACT FROM AUTHOR]

In the context of harvesting tidal stream energy, which is considered a promising source of renewable energy due to its high energy density, stability, and predictability, this paper proposes a review-based roadmap investigating the use of data-driven techniques, more specifically machine learning-based approaches, to detect and estimate the extent of biofouling in tidal stream turbines. An overview of biofouling and its impact on these turbines will be provided as well as a brief review of current methodologies and techniques for detecting and estimating biofouling. Additionally, recent developments and challenges in the field will be examined, while providing several promising prospects for biofouling detection and estimation in tidal stream turbines. [ABSTRACT FROM AUTHOR]

This paper addresses the issues of maximum power point tracking (MPPT) and fault-tolerant control in tidal steam turbines under complex marine environments. In order to solve the conflicting problems in the existing sliding mode control between dynamic performance and chatter reduction as well as the use of fault estimation link in the fault-tolerant control, which increases the system complexity, an adaptive non-singular fast terminal sliding mode and adaptive robust fault tolerance method (ANFTSMC-ARC) is proposed. First, a speed controller equipped with adaptive non-singular fast terminal sliding mode control (ANFTSMC) is designed to improve the power capture efficiency under swell disturbances. This design achieves fast convergence and circumvents the singularity problem. Then, a new reach law is proposed based on the adaptive hybrid exponential reaching law (AHERL), which ensures high tracking performance while reducing chattering. In addition, considering that the hydraulic pitch system is prone to failure, a fault-tolerant controller with automatically adjustable gain is designed under the adaptive robust scheme. With the help of Lyapunov theory, the closed-loop system is proved to be uniform and ultimately bounded. Finally, comparative simulation results verify the efficiency of the proposed control strategy.

Tidal stream turbines (TSTs) harness the kinetic energy of tides to generate electricity by rotating the rotor. Biofouling will lead to an imbalance between the blades, resulting in imbalanced torque and voltage across the windings, ultimately polluting the grid. Therefore, rotor condition monitoring is of great significance for the stable operation of the system. Image-based attachment detection algorithms provide the advantage of visually displaying the location and area of faults. However, due to the limited availability of data from multiple machine types and environments, it is difficult to ensure the generalization of the network. Additionally, TST images degrade, resulting in reduced image gradients and making it challenging to extract edge and other features. In order to address the issue of limited data, a novel non-data-driven edge detection algorithm, indexed resemble-normal-line guidance detector (IRNLGD), is proposed for TST rotor attachment fault detection. Aiming to solve the problem of edge features being suppressed, IRNLGD introduces the concept of “indexed resemble-normal-line direction” and integrates multi-directional gradient information for edge determination. Real-image experiments demonstrate IRNLGD’s effectiveness in detecting TST rotor edges and faults. Evaluation on public datasets shows the superior performance of our method in detecting fine edges in low-light images.

This article deals with the techno-economic optimal sizing of a tidal stream turbine (TST)–battery system. In this study, the TST system consists of a turbine rotor and a permanent magnet synchronous generator (PMSG) associated with a three-phase converter coupled to a DC bus. A battery is used within the system as an energy storage system to absorb excess produced power or cover power deficits. To determine the optimal sizing of the system, an iterative approach was used owing to its ease of implementation, high accuracy, and fast convergence speed, even under environmental constraints such as swell and wave effects. This technique is based on robust energy management, and the recursive algorithm includes the deficiency of power supply probability (DPSP) and the relative excess power generation (REPG) as technical criteria for the system reliability study, and the energy cost (EC) and the total net present cost (TNPC) as economic criteria for the system cost study. As data inputs, the proposed approach used the existing data from the current speed profile, the load, and economic parameters. The desired output is the system component optimal sizing (TST power, and battery capacity). In this paper, the system sizing was studied during a one-year time period to ensure a more reliable and economical system. The results are compared to well-known methods such as genetic algorithms, particle swarm optimization, and software-based (HOMER) approaches. The optimization results confirm the efficiency of the proposed approach in sizing the system, which was simulated using real-world tidal velocity data from a specific deployment site. [ABSTRACT FROM AUTHOR]

This study addresses the biofouling challenges in Tidal Stream Turbines (TSTs) to ensure their reliable and optimal operation. In this context, it is proposed an effective methodology employing a soft voting ensemble transfer learning-based approach for the detection and extent classification of biofouling. The proposed framework incorporates essential components such as data augmentation and pre-processing, including image resizing and data segmentation, forming a comprehensive video image-based approach. To overcome the constraint of limited data, experimental investigations were conducted, resulting in the acquisition of two datasets: one from the TST platform at Shanghai Maritime University (SMU) and the other from the tidal turbulence test facility at Lehigh University (LU). The three prominent convolutional neural network models, namely Visual Geometry Group (VGG), Residual Network (ResNet) and MobileNet, trained on these datasets, demonstrate precise detection and classification of turbine conditions, achieving an accuracy of 83% for the SMU dataset and 90% for the LU dataset. The noted disparity in accuracy for the SMU dataset is attributed to its smaller size, highlighting the significant impact of dataset scale on classification performance. This study provides valuable insight into the development of effective biofouling detection and classification strategies for TST systems. • Provide a soft voting ensemble transfer learning-based tool for real-time biofouling detection and estimation. • Data augmentation using rotation, scaling, flipping, zooming, and brightening of the cropped input images. • Data pre-processing including image resizing and data segmentation using the segment anything model. [ABSTRACT FROM AUTHOR]

Renewable energy generation is increasingly important due to serious energy issues. A Doubly Salient Permanent Magnet Generator (DSPMG) can be an interesting candidate for tidal stream renewable energy systems. However, the special structure makes the system nonlinear and strongly coupled even after Park transformation and involves a larger torque ripple. Previous research mainly focused on model-based control for this machine, which is very sensitive to the parameters. Thus, to control the complex systems stably and accurately, two model-free control algorithms, Active Disturbance Rejection-Based Iterative Learning Control (ADRILC) and Active Disturbance Rejection Control–Iterative Learning Control (ADRC–ILC), are proposed for the current and speed control loops of a DSPMG-based Tidal Stream Turbine (TST), respectively. ADRC–ILC uses ADRC to deal with the external non-periodic speed ripple and adopts ILC to reduce the internal periodic speed ripple. ADRILC employs an iterative method to improve the ESO for the enhancement of the convergence rate of ILC. Considering the variable tidal speed, when the speed is above the rated value, Maximum Power Point Tracking (MPPT) must be changed to a power limitation strategy for limiting the generator power to the rated value and extending the system operating range. Thus, Optimal Tip Speed Ratio (OTSR)-based MPPT (for a low tidal current speed) and Leading Angle Flux-Weakening Control (LAFWC) (for a high tidal current speed) strategies are also proposed. According to the simulation results, the proposed ADRC–ILC + ADRILC has the lowest torque ripple, the highest control accuracy, as well as a good current tracking capability and strong robustness. At the rated speed, the proposed method reduces the torque ripple by more than 20% and the speed error by about 80% compared with PI control: the current difference is limited in 2A. The LAFWC proposed for an excessive tidal current speed is effective in conserving the electromagnetic power and increasing the generator speed.

Developing reliable tidal-energy turbines of a large size and capacity links to preservation of the structural safety and stability of the blades. In this study, a bidirectional fluid–structure coupling method was applied to analyze the hydrodynamic performance and structural characteristics of the blade of a tidal-stream turbine. Analyses were conducted on the transient and stable structural stresses, fatigue, and deformations under the influence of water depth and turbine rotational speed. The performance predictions with and without fluid–structure coupling are similar to measurements. The water-depth change has little effect on the stress and deformation change of the blade, while the turbine-speed change has the most significant effect on it. When the turbine just starts, the blade will be subject to a sudden change load. This is due to the increase in turbine speed, resulting in the sudden load. Similar to the trend of blade stress, the blade safety factor is lower near the root of the blade, and the turbine-speed change has a more significant impact on the blade structure's safety. However, the number of stress cycles in the blade at different rotational speeds is within the safety range. [ABSTRACT FROM AUTHOR]

Power extraction has become a critical consideration in tidal stream turbine (TST) systems. In practice, the lumped disturbances under varying tidal current conditions may deteriorate the maximum power point tracking (MPPT) performance and cumulate fatigue damage over-rated power. Besides, the conventional pitch controllers are sensitive to parameter uncertainties of the nonlinear TST system. In this paper, a novel output power internal control strategy based on pseudo-tip-speed ratio and adaptive genetic algorithm (PTSR-AGA) is proposed to improve the anti-interference ability and reliability. The proposed control scheme consists of two parts. The first part proposes the PTSR method for MPPT to predict the TST's operating point which contributes reducing the logical errors assigned to swell disturbances. The second part designed an AGA for the optimization of the pitch controller to conduct its angle delay. A reduced pitch control strategy is applied to the preprocessing of the pitch controller to reduce the mechanical wear over the rated power. The comparative simulation results validate the TST system can obtain a higher power efficiency of energy capture and a smoother power output with the proposed control strategies at full range of tidal current speed. [ABSTRACT FROM AUTHOR]

This paper has developed an operation and maintenance (O&M) model for projected 20 MW tidal stream farm case studies at two sites in the northeast Atlantic in France and at EMEC’s Fall of Warness site in the UK. The annual energy production, number of incidents, and downtimes of the farms for corrective and planned (preventive) maintenance strategies are estimated using Monte Carlo simulations that vary weather windows, repair vessel availabilities, and mean annual failure rates modelled by Weibull distributions. The trade-offs between the mean annual failure rates, time availability, O&M costs, and energy income minus the variable O&M costs were analysed. For all scenarios, a 5-year planned maintenance strategy could considerably decrease the mean annual failure rates by 37% at both sites and increase the net energy income. Based on a detailed sensitivity analysis, the study has suggested a simple decision-making method that examines how the variation in the mean annual failure rate and changes in spare-part costs would reduce the effectiveness of a preventive maintenance strategy. This work provides insights into the most important parameters that affect the O&M cost of tidal stream turbines and their effect on tidal energy management. The output of the study will contribute to decision-making concerning maintenance strategies.

In the context of harvesting tidal stream energy, which is considered a promising source of renewable energy due to its high energy density, stability, and predictability, this paper proposes a review-based roadmap investigating the use of data-driven techniques, more specifically machine learning-based approaches, to detect and estimate the extent of biofouling in tidal stream turbines. An overview of biofouling and its impact on these turbines will be provided as well as a brief review of current methodologies and techniques for detecting and estimating biofouling. Additionally, recent developments and challenges in the field will be examined, while providing several promising prospects for biofouling detection and estimation in tidal stream turbines.

Malaysia has a great potential to harness energy in water due to its long coastline within the South China Sea and the Straits of Malacca. Malaysia’s energy mix could be improved using marine current energy devices (MCEDs) to replace fossil fuel and it is predictable energy compare to hydropower, solar photovoltaic (PV), and biomass. However, MCEDs is not been fully developed in Malaysia. The objectives of this paper are to provide a useful background for policymakers or researchers in the types of MCEDs and potential sites location of MCEDs that are applicable in Malaysia. This review also discusses the issues and challenges of MCEDs in Malaysia. Five types of MCEDs were discussed including tidal range device, tidal stream turbine, wave energy converter, ocean thermal energy conversion, and salinity gradient energy. These MCEDs are compared for their suitability of application in Malaysia. Among all MCEDs, tidal stream turbine is identified as high potential and commercially viable in Malaysia. However, ocean characteristics in Malaysia are low kinetic energy-flux density, low current speed, low tide, and shallow water; only fulfill the minimum requirement of tidal stream turbine making the tidal stream energy resources not significant enough to contribute to the nation’s energy mix. Therefore, using diffuser augmented tidal stream turbines to increase the flow velocity should be studied thoroughly.

Recently, the output of Computational Fluid Dynamic (CFD) prediction on tidal stream turbine systems has been receiving great attention owing to the vast and untapped tidal stream potential. For several years, many publications have documented the accuracy of CFD methods for steady flows, but not for unsteady flows. A challenging area persisting in the computational field is its dependence on large computing resources, and the unsteady nature of usual tidal streams. To overcome this problem, researchers have proposed modelling simpler, representative devices or combined CFD with alternative predictive methods. Nevertheless, the turbulent modelling has been a critical issue in the CFD methods and great effort has been devoted to the study of turbulence and wave effect on the wake and functioning of the turbine. The present paper is a review of CFD application on tidal stream turbine performance in both steady and unsteady flows. The performance of the turbine predicted by actuator and blade resolved methods, has been in accordance with laboratory observations. Findings of the wake have been both consistent and inconsistent with measurements, arising from interpretation of the blade force, fluid-solving method and onset flow model, and downstream range distance. With regards to arrays, preliminary work shows turbine arrangement can have profound effects in the onset flow, and in consequence, the performance of adjacent turbine rows. The results reported appear to support the wind similarity assumption, such as wake Gaussian velocity distributions and fluctuating turbine output in unsteady flows. Under the influence of surface waves, the wake recovers faster than steady flow condition due to the flow's convective acceleration, but the mean performance stays almost equal. The findings in this paper give a critical review and insight of important implications for future turbine research, such as experimental validation of the turbine prediction output in small and large arrays.

This article deals with the techno-economic optimal sizing of a tidal stream turbine (TST)–battery system. In this study, the TST system consists of a turbine rotor and a permanent magnet synchronous generator (PMSG) associated with a three-phase converter coupled to a DC bus. A battery is used within the system as an energy storage system to absorb excess produced power or cover power deficits. To determine the optimal sizing of the system, an iterative approach was used owing to its ease of implementation, high accuracy, and fast convergence speed, even under environmental constraints such as swell and wave effects. This technique is based on robust energy management, and the recursive algorithm includes the deficiency of power supply probability (DPSP) and the relative excess power generation (REPG) as technical criteria for the system reliability study, and the energy cost (EC) and the total net present cost (TNPC) as economic criteria for the system cost study. As data inputs, the proposed approach used the existing data from the current speed profile, the load, and economic parameters. The desired output is the system component optimal sizing (TST power, and battery capacity). In this paper, the system sizing was studied during a one-year time period to ensure a more reliable and economical system. The results are compared to well-known methods such as genetic algorithms, particle swarm optimization, and software-based (HOMER) approaches. The optimization results confirm the efficiency of the proposed approach in sizing the system, which was simulated using real-world tidal velocity data from a specific deployment site.

Developing reliable tidal-energy turbines of a large size and capacity links to preservation of the structural safety and stability of the blades. In this study, a bidirectional fluid–structure coupling method was applied to analyze the hydrodynamic performance and structural characteristics of the blade of a tidal-stream turbine. Analyses were conducted on the transient and stable structural stresses, fatigue, and deformations under the influence of water depth and turbine rotational speed. The performance predictions with and without fluid–structure coupling are similar to measurements. The water-depth change has little effect on the stress and deformation change of the blade, while the turbine-speed change has the most significant effect on it. When the turbine just starts, the blade will be subject to a sudden change load. This is due to the increase in turbine speed, resulting in the sudden load. Similar to the trend of blade stress, the blade safety factor is lower near the root of the blade, and the turbine-speed change has a more significant impact on the blade structure’s safety. However, the number of stress cycles in the blade at different rotational speeds is within the safety range.

Power extraction has become a critical consideration in tidal stream turbine (TST) systems. In practice, the lumped disturbances under varying tidal current conditions may deteriorate the maximum power point tracking (MPPT) performance and cumulate fatigue damage over-rated power. Besides, the conventional pitch controllers are sensitive to parameter uncertainties of the nonlinear TST system. In this paper, a novel output power internal control strategy based on pseudo-tip-speed ratio and adaptive genetic algorithm (PTSR-AGA) is proposed to improve the anti-interference ability and reliability. The proposed control scheme consists of two parts. The first part proposes the PTSR method for MPPT to predict the TST’s operating point which contributes reducing the logical errors assigned to swell disturbances. The second part designed an AGA for the optimization of the pitch controller to conduct its angle delay. A reduced pitch control strategy is applied to the preprocessing of the pitch controller to reduce the mechanical wear over the rated power. The comparative simulation results validate the TST system can obtain a higher power efficiency of energy capture and a smoother power output with the proposed control strategies at full range of tidal current speed.

This paper presents a method for the probabilistic evaluation of the failure rates of mechanical components in a typical power take-off (PTO) system of a horizontal-axis tidal stream turbine (HATT). The method is based on a modification of the method of the influence factors, when base failure rates, relevant influence factors and, subsequently, resulting failure rates are treated as random variables. The prior (i.e., initial) probabilistic distribution of the failure rates of a HATT component is generated using data for similar components from other industries, while taking into account actual characteristics of the component and site-specific operating and environmental conditions of the HATT. A posterior distribution of the failure rate is estimated numerically based on a Bayesian approach as new information about the component performance in an operating HATT becomes available. The posterior distribution is then employed to obtain the updated mean and lower and upper confidence limits of the failure rate. The proposed method is illustrated by applying it to the evaluation of the failure rates of two key components of the PTO system of a typical HATT—main seal and main bearing. In particular, it is shown that uncertainty associated with the method itself has a major influence on the failure rate evaluation. The proposed method is useful for the reliability assessment of both PTO designs of new HATTs and PTO systems of operating HATTs.

Abstract Tidal turbines are subject to large hydrodynamic loads from combinations of currents and waves, which contribute significantly to fatigue, extreme loading and power flow requirements. Physical model testing enables these loads and power fluctuations to be assessed and understood in a controlled and repeatable environment. In this work, a 1:15 scale tidal turbine model is utilised to further the fundamental understanding of the influence of waves on tidal turbines. A wide range of regular waves are generated in both following-current and opposing-current conditions. Wave frequencies range from 0.31 Hz to 0.55 Hz & wave heights from 0.025 m to 0.37 m in a fixed 0.81 m/s current velocity. Waves are selected and programmed specifically to facilitate frequency domain analysis, and techniques are employed to isolate the effect of non-linear waves on turbine power and thrust. Results demonstrate that wave action induces large variations in turbine power and thrust compared to current only conditions. For the range of conditions tested, peak values of thrust and power exceed current-only values by between 7%–65% and 13%–160% respectively. These wave-induced fluctuations are shown to increase with wave amplitude and decrease with wave frequency. Following wave conditions exhibit greater variations than opposing for waves with the same wave height and frequency due to the lower associated wavenumbers. A model is developed and presented to aid the understanding of the high-order harmonic response of the turbine to waves, which is further demonstrated using steady state coefficients under assumptions of pseudo-stationarity. This approach is proven to be effective at estimating wave-induced power and thrust fluctuations for the combinations of waves, currents and turbine state tested. The outcome of which shows promise as a rapid design tool that can evaluate the effect of site-specific wave–current conditions on turbine performance. [ABSTRACT FROM AUTHOR]

Most electrical machines and drive signals are non-Gaussian and are highly nonlinear in nature. A useful set of techniques to examine such signals relies on higher-order statistics (HOS) spectral representations. They describe statistical dependencies of frequency components that are neglected by traditional spectral measures, namely the power spectrum (PS). One of the most used HOS is the bispectrum where examining higher-order correlations should provide further details and information about the conditions of electric machines and drives. In this context, the stator currents of electric machines are of particular interest because they are periodic, nonlinear, and cyclostationary. This current is, therefore, well adapted for analysis using bispectrum in the designing of an efficient condition monitoring method for electric machines and drives. This paper is, therefore, proposing a bispectrum-based diagnosis method dealing the with tidal stream turbine (TST) rotor blades biofouling issue, which is a marine environment natural process responsible for turbine rotor unbalance. The proposed bispectrum-based diagnosis method is verified using experimental data provided from a permanent magnet synchronous generator (PMSG)-based TST experiencing biofouling emulated by attachment on the turbine blade. Based on the achieved results, it can be concluded that the proposed diagnosis method has been very successful. Indeed, biofouling imbalance-related frequencies are clearly identified despite marine environmental nuisances (turbulences and waves).

This study mainly investigates the current and speed control strategies of a doubly-fed induction generator (DFIG), which is applied to a tidal stream turbine (TST). Indeed, DFIG using integer-order PI (IOPI) controller has been widely proposed in the applications with a similar system, especially in wind energy conversion system (WECS). However, these conventional controllers cannot deal with the problems caused by the parameter variations satisfactorily under complex and harsh operation conditions, and may even deteriorate the performance. As a result, a fractional-order PI (FOPI) controller is considered to improve the efficiency and performance of DFIG-based TST in this paper. The FOPI controller, developed from the traditional IOPI controller and the fractional calculus theory, has a lot of prominent merits in many aspects, such as robustness, stability, and dynamic performance. In this paper, the proposed control strategies are embedded into the whole TST model which contains the tidal stream turbine, and the generator. The obtained simulation results demonstrate the prominent effectiveness and advantages of the proposed strategies compared with the conventional IOPI controller in terms of overshoot, static error, adjustment time, and robustness. It implies that FOPI controller could be a good candidate in TST applications.

Tidal stream turbines are subject to complex flow conditions, particularly when installed in staggered array configurations where the downstream turbines are affected by the wake and/or bypass flow of upstream turbines. This work presents, for the first time, methods for and results from the physical testing of three 1/15 scale instrumented turbines configured in a closely-spaced staggered array, and demonstrates experimentally that increased power extraction can be achieved through reduced array separation. A comprehensive set of flow measurements was taken during several weeks testing in the FloWave Ocean Energy Research Facility, with different configurations of turbines installed in the tank in a current of 0.8 m/s, to understand the effect that the front turbines have on flow through the array and on the inflow to the centrally placed rearmost turbine. Loads on the turbine structure, rotor, and blade roots were measured along with the rotational speed of the rotor to assess concurrently in real-time the effects of flow and array geometry on structural loading and performance. Operating in this closely-spaced array was found to improve the power delivered by the rear turbine by 5.7–10.4% with a corresponding increase in the thrust loading on the rotor of 4.8–7.3% around the peak power operating point. The experimental methods developed and results arising from this work will also be useful for further scale-testing elsewhere, validating numerical models, and for understanding the performance and loading of full-scale tidal stream turbines in arrays.

Tidal stream energy is acquiring more attention as a future potential renewable energy source. Considering the harsh submarine environment, the main challenges that face the tidal stream turbine (TST) industry are cost and reliability. Hence, simple and reliable technologies, especially considering the drivetrain, are preferred. The multibrid drivetrain configuration with only a single stage gearbox is one of the promising concepts for TST systems. In this context, this paper proposes the design optimization of a multibrid permanent magnet generator (PMG), the design of a planetary gearbox, and afterwards analyzes the multibrid concept cost-effectiveness for TST applications. Firstly, the system analytical model, which consists of a single-stage gearbox and a medium speed PMG, is presented. The optimization methodology is afterwards highlighted. Lastly, the multibrid system optimization results for different gear ratios including the direct-drive topology are discussed and compared where the suitable gear ratio (topology) is investigated. The achieved results show that the multibrid concept in TST applications seems more attractive than the direct-drive one especially for high power ratings.

This paper details a hydrodynamic model based on Blade Element Momentum Theory (BEMT) developed to assess ’conventional’ 3-bladed tidal stream turbines (TSTs), adapted here to analyse an ’unconventional’ case of a ducted and open centre device. Validations against a more detailed coupled Reynolds averaged computational fluid dynamics (RANS-BEM) model shows excellent agreement, of within 2% up to the peak power condition, with associated computational times in the order of a few minutes on a single core. The paper demonstrates the application of hydrodynamic forces into a structural analysis tool, in order to assess blade stress distributions of a generic hubless turbine. Incorporation of parameters such as non-uniform inflows and blade weight forces are investigated, with their effects on stress profiles presented. Key findings include: i) the adapted BEMT model replicates the majority of turbine performance characteristics estimated through previous CFD assessments; ii) the proposed model reduces the computational effort by several orders of magnitude compared to the reference coupled CFD, making it suitable for engineering assessments iii) blade stress distribution profiles are quantified, detailing concentration zones and cyclic values for use in fatigue analyses. This work forms part of a greater project aimed to develop a suite of analytical tools to perform engineering assessments of bi-directional ducted TSTs.

Flow characteristics in coastal regions are strongly influenced by the topography of the seabed and understanding the fluid dynamics is necessary before installation of tidal stream turbines (TST). In this paper, the bathymetry of a potential TST deployment site is used in the development of the a CFD (Computational Fluid Dynamics) model. The steady state k-ϵ and transient Large Eddy Simulation (LES) turbulence methods are employed and compared. The simulations are conducted with a fixed representation of the ocean surface, i.e., a rigid lid representation. In the vicinity of Horse Rock a study of the pressure difference shows that the small change in height of the water column is negligible, providing confidence in the simulation results. The stream surface method employed to visualise the results has important inherent characteristics that can enhance the visual perception of complex flow structures. The results of all cases are compared with the flow data transect gathered by an Acoustic Doppler Current Profiler (ADCP). It has been understood that the k-ϵ method can predict the flow pattern relatively well near the main features of the domain and the LES model has the ability to simulate some important flow patterns caused by the bathymetry.

This paper documents the predictive capability of rotating blade-resolved unsteady Reynolds averaged Navier-Stokes (URANS) and Improved Delayed Detached Eddy Simulation (IDDES) computations for tidal stream turbine performance and intermediate wake characteristics. Ansys/Fluent and OpenFOAM simulations are performed using mixed-cell, unstructured grids consisting of up to 11 million cells. The thrust, power and intermediate wake predictions compare reasonably well within 10% of the experimental data. For the wake predictions, OpenFOAM performs better than Ansys/Fluent, and IDDES better than URANS when the resolved turbulence is triggered. The primary limitation of the simulations is under prediction of the wake diffusion towards the turbine axis, which in return is related to the prediction of turbulence in the tip-vortex shear layer. The shear-layer involves anisotropic turbulent structures; thus, hybrid RANS/LES models, such as IDDES, are preferred over URANS. Unfortunately, IDDES fails to accurately predict the resolved turbulence in the near-wake region due to the modeled stress depletion issue.

It is important to understand tidal stream turbine performance and flow field, if tidal energy is to advance. The operating condition of a tidal stream turbine with a supporting structure has a significant impact on its performance and wake recovery. The aim of this work is to provide an understanding of turbine submerged depth that governs the downstream wake structure and its recovery to the free-stream velocity profile. An experimentally validated numerical model, based on a computational fluid dynamics (CFD) tool, was present to obtain longitudinal, transverse and vertical velocity profiles. Wake characteristics measurements have been carried out in an open channel at Hohai University. The results indicate that varying the turbine proximity to the water surface introduces differential mass flow rate around the rotor that could make the wake persist differently downstream. CFD shows the same predicted wake recovery tendency with the experiments, and an agreement from CFD and experiments is good in the far-wake region. The results presented demonstrate that CFD is a good tool to simulate the performance of tidal turbines particularly in the far-wake region and that the turbine proximity to the water surface has an effect on the wake recovery. [ABSTRACT FROM AUTHOR]

A detailed experimental investigation of the wake propagation behind a horizontal axis turbine with three blades was conducted in a recirculating water flume. An Acoustic Doppler Velocimeter was employed to measure the time varying velocities at fifteen depths across the width of the open flume to obtain the three-dimensional velocity and turbulence fields within the length of 20 Rotor Diameters downstream. The experimental results indicated that velocity reduction in the wake was caused by both the kinetic energy extraction and blockage effects of the tidal stream turbine rotor and stanchion. The maximum velocity deficit occurred at the wake core due to a lower tip speed ratio and blockage of the hub. The wake strip gradually enlarged with the distance downstream, with less mixing in the transverse direction. The wake zone shifted towards the water surface in the vertical direction, which mainly resulted from the merging of two wakes induced by turbine rotor and stanchion. The wake rotation was also observed, and the maximum circumfluence velocity was approximately 20% of the stream-wise velocity; thus, it had a significant influence on the process of near wake mixing. Furthermore, the wake turbulence of the turbine was strong and anisotropic, which would have an impact on the behaviour of other turbines located downstream if they were in a turbine array. In addition, the turbine stanchion had a visible influence on the wake structure, especially near the wake, which should not be neglected when studying the wake characteristics. [ABSTRACT FROM AUTHOR]

In order to obtain the maximum amount of energy from tidal stream extraction devices, deployment in large arrays should be studied. The area of seabed with favorable conditions is fairly limited; therefore layout spacing has to be optimized. In this paper a feasibility study for a novel experimental facility, suitable for the testing of an array of tidal devices, is presented. To avoid space and scale limitations of towing tanks, testing is proposed to be performed in large lakes or calm seas using a self-propelled vessel, which will carry an array of devices with variable spacing, creating relevant speed differences and measuring their performance and loading. Using hydrodynamic scaling laws, an appropriate size for test turbines and the range of vessel speed was determined to fulfill experimental requirements. Computational fluid dynamic simulations, using the actuator disc method, have suggested a suitable turbine array configuration to resemble real application conditions. A simplified model of the vessel was analyzed using the finite elements method to determine the main scantlings. The hull resistance calculated by empirical formulae was found to be negligible compared to the resistance of the tested turbine. It was confirmed that turbine size and speed determined by scaling laws are also reasonable from a propulsion point of view.

Flow characteristics in coastal regions are strongly influenced by the topography of the seabed and understanding the fluid dynamics is necessary before installation of tidal stream turbines (TST). In this paper, the bathymetry of a potential TST deployment site is used in the development of the a CFD (Computational Fluid Dynamics) model. The steady state k-ϵ and transient Large Eddy Simulation (LES) turbulence methods are employed and compared. The simulations are conducted with a fixed representation of the ocean surface, i.e., a rigid lid representation. In the vicinity of Horse Rock a study of the pressure difference shows that the small change in height of the water column is negligible, providing confidence in the simulation results. The stream surface method employed to visualise the results has important inherent characteristics that can enhance the visual perception of complex flow structures. The results of all cases are compared with the flow data transect gathered by an Acoustic Doppler Current Profiler (ADCP). It has been understood that the k-ϵ method can predict the flow pattern relatively well near the main features of the domain and the LES model has the ability to simulate some important flow patterns caused by the bathymetry. [ABSTRACT FROM AUTHOR]

The mean wake of a three-bladed horizontal axis tidal stream turbine operating at maximum power coefficient has been investigated experimentally in a wide flume with width 11 times the depth, providing minimal restriction to transverse wake development and behaviour of large-scale horizontal turbulence structures. This is an important first stage for understanding wake interaction in turbine arrays and hence large-scale power generation. The rotor diameter has a typical value of 60% of the depth and the thrust coefficient is representative of a full-scale turbine. The shear layers originating from the rotor tip circumference show classic linear expansion downstream, with the rate of a plane shear layer vertically and 1.5 times that horizontally. These shear layers merge by around 2.5 diameters downstream forming a self-similar two-dimensional wake beyond eight diameters downstream with a virtual origin at two diameters downstream of the rotor plane. The spreading rate is somewhat less than that for solid bodies. The detailed velocity measurements made in the near wake show rotation and vorticity similar to that measured previously for wind and marine turbines although with asymmetry associated with bed and surface proximity. The longitudinal circulation in a transverse plane is conserved at about 1% of the swept circulation from the blade tip within two diameters downstream, the extent of detailed measurement. Turbines are usually designed using blade element momentum theory in which velocities at the rotor plane are characterised by axial and tangential induction factors and it is now possible to see how this idealisation relates to actual velocities. The axial induction factor corresponds to velocity deficits at 0.4–0.8 radii from the rotor axis across the near wake while the tangential induction factor at the rotor plane corresponds to velocities at 0.4–0.6 radii between 1–2 diameters downstream, indicating some general correspondence. For the two-dimensional self-similar far wake the two parameters defining the centreline velocity deficit and the transverse velocity profiles are likely to be insensitive to Reynolds number in turbulent conditions. [ABSTRACT FROM AUTHOR]