A review of current issues of marine current turbine blade fault detection

doi:10.1016/j.oceaneng.2020.108194

Ocean Engineering

Volume 218, 15 December 2020, 108194

https://doi.org/10.1016/j.oceaneng.2020.108194 Get rights and content

Highlights

•
An overview of marine current turbine (MCT) blade fault detection is given.
•
We analyze the pros & cons of MCT blade fault detection methods.
•
Four research directions for future research work are also suggested.

Abstract

Marine current turbines (MCTs) has progressively attracted wider interest from the industry and many research initiatives due to its potential as a novel world energy resource. However, several technological issues amongst others fault detection of MCT blades still require further progress for their successful implementation. Indeed, power generation is likely to progressively degrade due to blade faults and then causes disruptive disturbance when the marine current generator is connected to the grid. Fault detection of MCT blades still presents several challenges due to the complexity of the underwater environment. It appears that attachment like plankton or biofouling may have a considerable influence on the turbine blade as these may be triggering different imbalance faults. This survey reviews different blade fault types under the condition of wave and turbulence. We also review current blade fault detection methods, including multi-domain approaches. It appears from our study that built-in sensor-based fault detection methods, which use phase currents and voltages across the generator windings, provide several advantages for MCT blade fault detection. It also proposes several trends.

Introduction

The oceans, which account for 70% of the Earth's surface, contain abundant energy (Cheng et al., 2018). With the depletion of oil resources, many countries worldwide have turned their attention to new sources for clean, pollution-free new energy. According to BP World Energy Outlook 2018, global energy demand growth will slow down in 2018–2040, and renewable energy should rapidly develop, increasing by about 33% by 2040 (Launch). Table 1 gives an estimate of the potential marine energy worldwide (Chen et al., 2018a).

As one of the new electrical energy power generation technologies, MCTs have attracted worldwide attention (Khalil et al., 2018; Lee, 2020). Different reviews that consider the design of MCT have been recently published (Chen and Lam, 2015; Rodrigo et al., 2018; Ke et al., 2020a). Specific reviews on fault detection, however, have not yet explicitly been published. Although the maritime domain contains endless renewable energy, wave, and turbulence conditions inevitably disturb turbine operations. Shear flow is one of the triggers for MCT hydro-dynamic asymmetry (Ke et al., 2020b). Meanwhile, the growth of marine organisms or marine pollutants may also cause mechanical faults in blades, and which consequently generate damages to other components of the MCT, such as generators (Whitby and Galde-Loo, 2014; Duhaney and Khoshgoftaar, 2010, 2012; Hu and Du, 2012; Mjit et al., 2011a), bearing (Waters et al., 2012, 2013), and winding's insulation systems (Wang et al., 2019; Liu et al., 2016). Blade faults have a crucial impact on the safety and reliability of MCT systems (Amir Hossein and Bibeau, 2016; Mohammad and Bibeau, 2020; Rosli, 2020; Zhang et al., 2014). Alternatively, blade fault detection approaches have been widely applied to gas turbines or wind turbines (WTs).

This paper aims to analyze the impact of blade faults and discuss open research issues related to MCT blade fault detection. Fault detection techniques are reviewed from sensor and signal-based perspectives. We survey fault detection methods based on used measurements and the different techniques used so far. The rest of the paper is organized as follows. Section 2 describes the root causes of blade fault and models resultant torque imbalance. In Section 3, the blade fault detection methods are reviewed based on whether they use specific sensors or embedded electrical measurements (i.e., phase currents or voltages). Section 4 concludes the paper and draws some perspectives for future works.

Section snippets

Effects of the environment on the blades

The MCT is installed undersea and therefore operates in harsh conditions (seawater corrosion, mud, plankton, biofouling, etc.) with variable environmental parameters such as sudden changes in instantaneous flow velocity (Chen et al., 2012). These harsh working conditions (Malki et al., 2013) favour the appearance of faults or failures.

Specific sensor-based fault detection methods

In the case of an MCT, one should implement appropriate sensors to monitor the blade condition: this includes pressure sensors (i.e., to evaluate vessel integrity and inspect the surface of the blade), tachometers (i.e., to measure the rotational velocity of the blades), temperature sensors (i.e., to evaluate if internal blades are overheating compared with other components), and strain gauges (i.e., to control if the blades are deformed, for example attached by plankton) (Edwards et al., 2011;

Perspectives

Table 7 provides a comprehensive comparison of MCT blade fault detection techniques. Their characteristics such as intrusive, complexity and maintenance are respectively addressed, and we suggest the following four directions for future research work:

a)
A fault model of unstable conditions such as surge and turbulence should be established. As mentioned before, the frequent occurrence of waves and turbulence causes blade faults. This happens randomly and leads to non-linear behavior and

Conclusion

The objective of this paper was to give an overview of the existing methods and techniques used for MCT blade fault detection. The methods have been classified depending on whether the measures are collected using specific sensors (e.g., accelerometers, cameras, temperature) or the built-in generator phase currents sensors. It appears that specific sensor-based methods suitable for fault detection of WTs blades are not necessarily suitable for MCTs. Given similar MCT and WT, the Pros & Cons for

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

This research is supported by the National Natural Science Foundation of China (61673260).

References (128)

O. Abdeljaber et al.
Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks
J. Sound Vib.
(2017)
Y. Amirat et al.
EEMD-based notch filter for induction machine bearing faults detection
Appl. Acoust.
(2018)
H. Chen
Wind turbine gearbox fault diagnosis based on improved EEMD and Hilbert square demodulation
Appl. Sci.
(2017)
L. Chen et al.
A review of survivability and remedial actions of tidal current turbines
Renew. Sustain. Energy Rev.
(2015)
U. Elosegui et al.
Novel on-field method for pitch error correction in wind turbines
Energy Procedia
(December 2017)
C. Frost et al.
The effect of tidal flow directionality on tidal turbine performance characteristics
Renew. Energy
(2015)
M. Haneef et al.
Vibration and wear prediction analysis of IC engine bearings by numerical simulation
Wear
(August 2017)
Z. Hu et al.
Reliability analysis for hydrokinetic turbine blades
Renew. Energy
(2012)
X. Jiang
A coarse-to-fine decomposing strategy of VMD for extraction of weak repetitive transients in fault diagnosis of rotating machines
Mech. Syst. Signal Process.
(2019)
J. Kaldellis et al.
Environmental and social footprint of offshore wind energy. Comparison with onshore counterpart
Renew. Energy
(Jul. 2016)

Condition Monitoring and Fault Diagnosis of Tidal Stream Turbines Subjected to Rotor Imbalance Faults

(2016)

M. Allmark et al.

Tidal Steam Turbine Blade Fault Diagnosis Using Time-Frequency Analyses

(2015)

B. Amir Hossein et al.

Frequency analysis of the power output for a vertical axis marine turbine operating in the wake

Ocean. Eng.

(2016)

J. Baqersad et al.

Dynamic Characteristics of a Wind Turbine Blade Using 3D Digital Image Correlation, Health Monitoring Of Structural And Biological Systems 2012

(2012)

M. Barakat et al.

Energetic macroscopic representation of a marine current turbine system with loss minimization control

IEEE Trans. Sustain. Energy

(2018)

H. Benbouzid et al.

Marine renewable energy converters and biofouling: a review on impacts and prevention

Fiber Bragg grating sensors in energy applications

W. David

Bidirectional long short-term memory networks for rapid fault detection in marine hydrokinetic turbines

P. Davies et al.

Evaluation of the Durability of Composite Tidal Turbine Blades

(2012)

I. Diah Pk

Decomposition Wavelet Transform as Identification of Outer Race Bearing Damage through Stator Flow Analysis in Induction Motor. 2019 International Conference on Information and Communications Technology

(2019)

D. Dorrell et al.

Damper windings in induction machines for reduction of unbalanced magnetic pull and bearing wear

IEEE Trans. Ind. Appl.

(2013)

F. Dreier et al.

Interferometric sensor system for blade vibration measurements in turbomachine applications

IEEE Trans. Instrum. Meas.

(2013)

J. Duhaney et al.

Comparing Sensor Fusion Approaches for Ocean Turbine Monitoring and Reliability

(2010)

J. Duhaney et al.

A Study on Class Imbalance in Ocean Turbine Fault Data

(2012)

J. Duhaney et al.

Feature selection on dynamometer data for reliability analysis

J. Duhaney et al.

Applying feature selection to short time wavelet transformed vibration data for reliability analysis of an ocean turbine

Cited by (51)

A rotor attachment detection method based on ADC-CBAM for tidal stream turbine
2024, Ocean Engineering
As tidal stream energy is clean, sustainable, and can be extracted by tidal stream turbine (TST), many countries have paid more attention to it. However, the imbalance faults, caused by the rotor attachment of TST, have an adverse effect on power generation efficiency. Therefore, rotor attachment detection is of great importance for the operation and maintenance of TST. This paper proposes a new method based on asymmetric depthwise convolution and convolution block attention module (ADC-CBAM) to solve this problem. First, rotor images of TST are preprocessed by normalization and centering. Then, asymmetric depthwise convolution (ADC) separates the convolution structure and combines depthwise convolution to extract features and reduce the convolution computation. Furthermore, the convolution block attention module (CBAM) focuses on the feature region to reduce redundant information. Finally, the proposed method is used to detect the rotor attachment. The experimental results demonstrate that the method has higher accuracy and is more lightweight than other state-of-the-art lightweight methods. Moreover, the generalization is verified on the public dataset CIFAR-10.
A model for predicting the unsteady hydrodynamic characteristics on the blades of a horizontal axis tidal turbine
2024, Applied Mathematical Modelling
Tidal current turbines operate in a highly turbulent environment, which results in significant fluctuations in unsteady loads and consequent fatigue and dynamic failures in the rotor. Therefore, accurate calculation of these unsteady loads is crucial. However, the existing models do not take into account the influence of varying relative blade section thickness on dynamic stall when predicting the unsteady characteristics of horizontal axis tidal turbine (HATT) blades. To forecast the unsteady characteristics of the HATT blades, this study developed a method that considers the influence of relative blade thickness on dynamic stall. The modified Leishman-Beddoes (L-B) dynamic stall model, 3D rotational augmentation model, and steady-state BEM model were combined to predict the unsteady hydrodynamic characteristics of blades by synthesizing the transient inlet velocities induced by waves, shear flow and turbulence. In this method, the static parameters required for the modified L-B dynamic stall model were calculated by XFOIL within a range of small angle of attack (AoA) and then extrapolated to cover the entire range of AoA using the Viterna extrapolation method. The required dynamic parameters were obtained by CFD numerical simulation. This method, that we call HATT-UCPM, can consider both the effects of a single element on the unsteady hydrodynamic characteristics of the blades, as well as the combined effects of shear flow, waves, and turbulence on the blades in the actual marine environment. The accuracy of the method was validate through CFD numerical simulation. The results showed that the proposed method has a high consistency with the numerical simulation results, indicating that it has a good accuracy in the prediction of unsteady characteristics of HATT blades. Finally, the study examined several cases to investigate the impacts of shear flow, waves, and turbulence on the unsteady loads of blades, both individually and in combination.
An attachment recognition method based on semi-supervised video segmentation for tidal stream turbines
2024, Ocean Engineering
Tidal stream turbine (TST) is a reliable machine for converting tidal stream energy to electric energy. However, the efficiency of power generation will be severely affected by the increasing marine attachment on TST blades. Accurate identification of attachments is very important for their removal. This paper proposes a semi-supervised video segmentation network (SVSN) to recognize attachments in underwater video sources. To alleviate the labeling burden, only a small number of video frames need manual annotation, and sufficient labeled data are collected by a data augmentation algorithm. For utilizing the remaining video frames, these unlabeled data and the labeled data are used to train the SVSN based on semi-supervised adversarial learning. In this way, these two kinds of data benefit from each other, making the network to learn general features for better generalization ability. The SVSN includes a generator and a discriminator. The generator is a modified SegNet to obtain initial segmentation maps. The discriminator, a simple fully convolutional network, provides pixel-wise confidence maps for the semi-supervised adversarial learning. Besides, it enhances the generator segmentation quality via considering object boundary areas. Experimental results demonstrate that precise attachment recognition and fast uncertainty estimation can be accomplished under harsh submerged conditions.
Hydrodynamic damping in laminar, transient and turbulent regimes: Analytical and computational study
2023, Ocean Engineering
A study of oscillating flow over a flat plate using RANS modeling was conducted to investigate hydrodynamic damping and its correlation with the unsteady and oscillating boundary layer. It is found that the predictions with the $γ$ SST turbulence model was more consistent with the experimental results compared to the SST turbulence models. Two distinct damping behaviors were observed in the flow. For ${Re}_{δ} < 400$ , the damping remained constant and matched the laminar solution of Stokes’ second problem. In this scenario, the flow regime was quasi-laminar, and an increase in free-stream velocity did not lead to a rise in the fluid damping coefficient. Conversely, for ${Re}_{δ} > 400$ , the increase in ${Re}_{δ}$ induced turbulence during the deceleration phase, resulting in an amplification of the phase difference between displacement and wall shear stress, consequently increasing damping. This trend continued at higher values of $Re δ$ and during the quasi-steady regime. The results indicated that the existence of different damping regions, as reported in previous studies, occurred within a range similar to that of Stokes’ second problem. A transition in the boundary layer regime at higher ${Re}_{δ}$ could be a contributing factor to the variations in damping behavior.
Analysis of the hydrodynamic damping of a hydrofoil with leading edge cavitation using the modal work approach
2023, Ocean Engineering
The objective of this investigation is to evaluate the applicability and accuracy of the modal work approach in predicting the hydrodynamic damping of a hydrofoil with leading edge cavitation. Simulations are carried out at a flow velocity of 6.64 m/s, an attack angle of 10°, and cavitation numbers between 1.04 and 2.02. The grid scale, time step, and mode shape amplitude are carefully verified. Results for the cavitation shedding frequencies and hydrofoil natural frequencies show good agreement between experiment and simulation, with relative errors within 8.76% and 7.12%, respectively. The unsteady characteristics of leading edge cavitation have a significant influence on the modal parameters, such as the variation in natural frequency can reach 32.57% in a cavitation cycle and the corresponding mode shape is also changed. Therefore, a new strategy including unsteady mode analysis and energy dissipation signal filtering is proposed to simulate the hydrodynamic damping ratio with leading edge cavitation. Then, the time-averaged hydrodynamic damping ratio in a cavitation cycle is obtained, with relative errors between 15.11% and 35.57% by comparing them with the experimental results. This study realizes the application of the modal work approach in leading edge cavitation conditions.
Hierarchical localization algorithm for sustainable ocean health in large-scale underwater wireless sensor networks
2023, Sustainable Computing: Informatics and Systems
Being an extensive part of marine study, underwater localization has fascinated widespread consideration in both civil and military. The underwater sensor network localization should differ from existing terrestrial and recent localization techniques, owing to catastrophic ocean conditions, large-scale networks, and node drift, even in a static environment, along with the pretense of adverse challenges. To address these technical difficulties, we propose a hierarchical localization approach with anchor and ordinary nodes localized in two phases. First, anchor nodes are localized with the help of surface buoys, drifted on the water. Second, we propose a threshold that originates from the midpoint of the deployed anchor nodes for ordinary node localization. The nature of the threshold in the proposed technique enables the localization of all nodes in a network by demonstrating that the anchor node can cover 100% of the area after the nodes are deployed. Simulation results show that the proposed scheme gains high energy efficiency and precision, as well as wide localization coverage, which helps in ocean sustainability. In addition, the proposed technique is implemented in the Aqua-sim toolkit of NS-2 for the validation of the simulation results.

View all citing articles on Scopus

View full text

ReviewA review of current issues of marine current turbine blade fault detection

Highlights

Abstract

Introduction

Section snippets

Effects of the environment on the blades

Specific sensor-based fault detection methods

Perspectives

Conclusion

Declaration of competing interest

Acknowledgement

J. Sound Vib.

Appl. Acoust.

Appl. Sci.

Renew. Sustain. Energy Rev.

Energy Procedia

Renew. Energy

Wear

Renew. Energy

Mech. Syst. Signal Process.

Renew. Energy

Ocean. Eng.

Ocean. Eng.

Ocean. Eng.

Renew. Energy

Renew. Energy

Energy

Ocean. Eng.

Renew. Sustain. Energy Rev.

Renew. Sustain. Energy Rev.

Mech. Syst. Signal Process.

Int. J. Mar. Energy

Appl. Math. Model.

Mech. Syst. Signal Process.

Ocean. Eng.

Time-Frequency Analysis of TST Drive Shaft Torque for TST Blade Fault Diagnosis

Condition Monitoring and Fault Diagnosis of Tidal Stream Turbines Subjected to Rotor Imbalance Faults

Tidal Steam Turbine Blade Fault Diagnosis Using Time-Frequency Analyses

Frequency analysis of the power output for a vertical axis marine turbine operating in the wake

Ocean. Eng.

Dynamic Characteristics of a Wind Turbine Blade Using 3D Digital Image Correlation, Health Monitoring Of Structural And Biological Systems 2012

Energetic macroscopic representation of a marine current turbine system with loss minimization control

IEEE Trans. Sustain. Energy

Marine renewable energy converters and biofouling: a review on impacts and prevention

Marine Hydrokinetic Turbine Blade Fault Signature Analysis Using Continuous Wavelet Transform

A joint feature position detection-based sensorless position estimation scheme for switched reluctance motors

IEEE Trans. Ind. Electron.

Full-field dynamic strain on wind turbine blade using digital image correlation techniques and limited sets of measured data from photogrammetric targets

Exp. Tech.

fault diagnosis for temperature signal of turbine blade based on LS-SVM

Appl. Mech. Mater.

Attraction, challenge, and current status of marine current energy

IEEE Access

Deep PCA based real-time incipient fault detection and diagnosis methodology for electrical drive in high-speed trains

IEEE Trans. Veh. Technol.

Marine tidal current systems: state of the art. 2012

IEEE Int. Symp. Ind. Electron.

An imbalance fault detection algorithm for variable-speed wind turbines: a deep learning approach

Energies

Continues record global ocean warming

Adv. Atmos. Sci.

Fiber Bragg grating sensors in energy applications

Bidirectional long short-term memory networks for rapid fault detection in marine hydrokinetic turbines

Evaluation of the Durability of Composite Tidal Turbine Blades

Decomposition Wavelet Transform as Identification of Outer Race Bearing Damage through Stator Flow Analysis in Induction Motor. 2019 International Conference on Information and Communications Technology

Damper windings in induction machines for reduction of unbalanced magnetic pull and bearing wear

IEEE Trans. Ind. Appl.

Interferometric sensor system for blade vibration measurements in turbomachine applications

IEEE Trans. Instrum. Meas.

Comparing Sensor Fusion Approaches for Ocean Turbine Monitoring and Reliability

A Study on Class Imbalance in Ocean Turbine Fault Data

Feature selection on dynamometer data for reliability analysis

Applying feature selection to short time wavelet transformed vibration data for reliability analysis of an ocean turbine

Review
A review of current issues of marine current turbine blade fault detection