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Tianzhen Wang
Shanghai Maritime University, Shanghai, China

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Journal article
Published: 10 June 2021 in Computers & Electrical Engineering
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Microorganisms attached to marine current turbine may induce imbalance faults that badly affect the power generation efficiency. Therefore, it is necessary to conduct attachment recognition for prompt device maintenance. This paper proposes a coarse-fine semantic segmentation network (CSSN) to adaptively recognize the attachment location and size from blurry underwater images. The CSSN contains a deep coarse branch to perform global segmentation and a shallow fine branch to obtain local contours. The two branches are adaptively fused with dynamic weights in the training process. The final segmentation maps are produced by a softmax layer, after which the precise attachment area percentage can be computed. Besides, dropout is applied to estimate the recognition uncertainty that provides intuitive guidance for the maintenance decision. Experimental results show that the proposed method is efficient to recognize the attachment under turbid submerged conditions.

ACS Style

Haiyang Peng; Dingding Yang; Tianzhen Wang; Shreya Pandey; Lisu Chen; Ming Shi; Demba Diallo. An adaptive coarse-fine semantic segmentation method for the attachment recognition on marine current turbines. Computers & Electrical Engineering 2021, 93, 107182 .

AMA Style

Haiyang Peng, Dingding Yang, Tianzhen Wang, Shreya Pandey, Lisu Chen, Ming Shi, Demba Diallo. An adaptive coarse-fine semantic segmentation method for the attachment recognition on marine current turbines. Computers & Electrical Engineering. 2021; 93 ():107182.

Chicago/Turabian Style

Haiyang Peng; Dingding Yang; Tianzhen Wang; Shreya Pandey; Lisu Chen; Ming Shi; Demba Diallo. 2021. "An adaptive coarse-fine semantic segmentation method for the attachment recognition on marine current turbines." Computers & Electrical Engineering 93, no. : 107182.

Journal article
Published: 20 April 2021 in Energies
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This paper presents an innovative approach to the extraction of an indicator for the monitoring of bearing degradation. This approach is based on the principles of the empirical mode decomposition (EMD) and the Hilbert transform (HT). The proposed approach extracts the temporal components of oscillating vibration signals called intrinsic mode functions (IMFs). These components are classified locally from the highest frequencies to the lowest frequencies. By selecting the appropriate components, it is possible to construct a bank of self-adaptive and automatic filters. Combined with the HT, the EMD allows an estimate of the instantaneous frequency of each IMF. A health indicator called the Hilbert marginal spectrum density is then extracted in order to detect and diagnose the degradation of bearings. This approach was validated on two test benches with variable speeds and loads. The obtained results demonstrated the effectiveness of this approach for the monitoring of ball and roller bearings.

ACS Style

Abdenour Soualhi; Bilal El Yousfi; Hubert Razik; Tianzhen Wang. A Novel Feature Extraction Method for the Condition Monitoring of Bearings. Energies 2021, 14, 2322 .

AMA Style

Abdenour Soualhi, Bilal El Yousfi, Hubert Razik, Tianzhen Wang. A Novel Feature Extraction Method for the Condition Monitoring of Bearings. Energies. 2021; 14 (8):2322.

Chicago/Turabian Style

Abdenour Soualhi; Bilal El Yousfi; Hubert Razik; Tianzhen Wang. 2021. "A Novel Feature Extraction Method for the Condition Monitoring of Bearings." Energies 14, no. 8: 2322.

Journal article
Published: 26 February 2021 in Journal of Marine Science and Engineering
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Marine current energy as a kind of renewable energy has gradually attracted more and more attention from many countries. However, the blade imbalance fault of marine current turbines (MCTs) will have an effect on the power production efficiency and cause damage to the MCT system. It is hard to classify the severity of an MCT blade imbalance fault under the condition of the current instability and seafloor noise. This paper proposes a fault classification method based on the combination of variational mode decomposition denoising (VMD denoising) and screening linear discriminant analysis (S-LDA). The proposed method consists of three parts. Firstly, phase demodulation of the collected stator current signal is performed by the Hilbert transform (HT) method. Then, the obtained demodulation signal is denoised by variational mode decomposition denoising (VMD denoising), and the denoised signal is analyzed by power spectral density (PSD). Finally, S-LDA is employed on the power signal to determine the severities of fault classification. The effectiveness of the proposed method is verified by experimental results under different severities of blade imbalance fault. The stator current signatures of experiments with different severities of blade imbalance fault are used to validate the effectiveness of the proposed method. The fault classification accuracy is 92.04% based on the proposed method. Moreover, the experimental results verify that the influence of velocity fluctuation on fault classification can be eliminated.

ACS Style

Jiajia Wei; Tao Xie; Ming Shi; Qianqian He; Tianzhen Wang; Yassine Amirat. Imbalance Fault Classification Based on VMD Denoising and S-LDA for Variable-Speed Marine Current Turbine. Journal of Marine Science and Engineering 2021, 9, 248 .

AMA Style

Jiajia Wei, Tao Xie, Ming Shi, Qianqian He, Tianzhen Wang, Yassine Amirat. Imbalance Fault Classification Based on VMD Denoising and S-LDA for Variable-Speed Marine Current Turbine. Journal of Marine Science and Engineering. 2021; 9 (3):248.

Chicago/Turabian Style

Jiajia Wei; Tao Xie; Ming Shi; Qianqian He; Tianzhen Wang; Yassine Amirat. 2021. "Imbalance Fault Classification Based on VMD Denoising and S-LDA for Variable-Speed Marine Current Turbine." Journal of Marine Science and Engineering 9, no. 3: 248.

Journal article
Published: 19 February 2021 in Journal of Marine Science and Engineering
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Since 1973, studies have explored ocean power generation from different perspectives. However, in the past 45 years, few studies have attempted to comprehensively review the existing studies on ocean power generation using wave energy, tidal current energy, ocean thermal energy, salinity gradient energy, bio-mass energy, and gas hydrates. In this study, we collected 5262 studies published from 1973 to 2018 for scientometric visualization analysis and drew a knowledge map of the ocean power generation field. The results show that the most important contributions to the research of ocean power generation mainly came from the United States, China, Britain, Italy, Spain, Japan, Norway, Germany, France, and Denmark. Ocean power generation research is mainly divided into two stages. From 1973 to 2007, there were relatively few studies and no obvious hot topics. From 2008 to 2018, the knowledge fields mainly focused on ocean biomass power generation, the exploitation of natural gas hydrates, the utilization of wave energy and tidal energy, the research and optimization of energy generators, the storage and management of ocean energy, and numerical simulations of marine climates. In addition, the joint utilization of wind energy and wave energy is also a current research topic of interest, including joint assessment of the two energy potentials, the research and development of equipment, and numerical simulations of joint power generation projects.

ACS Style

Lisu Chen; Wei Li; Jie Li; Qiang Fu; Tianzhen Wang. Evolution Trend Research of Global Ocean Power Generation Based on a 45-Year Scientometric Analysis. Journal of Marine Science and Engineering 2021, 9, 218 .

AMA Style

Lisu Chen, Wei Li, Jie Li, Qiang Fu, Tianzhen Wang. Evolution Trend Research of Global Ocean Power Generation Based on a 45-Year Scientometric Analysis. Journal of Marine Science and Engineering. 2021; 9 (2):218.

Chicago/Turabian Style

Lisu Chen; Wei Li; Jie Li; Qiang Fu; Tianzhen Wang. 2021. "Evolution Trend Research of Global Ocean Power Generation Based on a 45-Year Scientometric Analysis." Journal of Marine Science and Engineering 9, no. 2: 218.

Journal article
Published: 11 December 2020 in Energies
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Marine current energy is attracting more and more attention in the world as a reliable and highly predictable energy resource. However, conventional proportional integral (PI) control will be sensitive to the numerous challenges that exist in a marine current turbine system (MCTs) such as marine current disturbance, torque disturbance and other uncertain parameters. This paper proposes a fuzzy adaptive backstepping control (F-A-BC) approach for a marine current turbine system. The proposed F-A-BC strategy consisted of two parts. First, an adaptive backstepping control approach with the compensation of disturbance and uncertainty was designed to improve anti-interference of the MCT so that the maximum power point tracking (MPPT) was realized. Then, a fuzzy logic control approach was combined to adjust parameters of an adaptive backstepping control approach in real time. The effectiveness of the proposed controller was verified by the simulation of a direct-drive marine current turbine system. The simulation results showed that the F-A-BC has better anti-interference ability and faster convergence compared to the adaptive backstepping control, sliding mode control and fuzzy PI control strategies under disturbances. The error percentage of rotor speed could be reduced by 3.5% under swell effect compared to the conventional controller. Moreover, the robustness of the F-A-BC method under uncertainties was tested and analyzed. The simulation results also indicated that the proposed approach could slightly improve the power extraction capability of the MCTs under variable marine current speed.

ACS Style

Xusheng Shen; Tao Xie; Tianzhen Wang. A Fuzzy Adaptative Backstepping Control Strategy for Marine Current Turbine under Disturbances and Uncertainties. Energies 2020, 13, 6550 .

AMA Style

Xusheng Shen, Tao Xie, Tianzhen Wang. A Fuzzy Adaptative Backstepping Control Strategy for Marine Current Turbine under Disturbances and Uncertainties. Energies. 2020; 13 (24):6550.

Chicago/Turabian Style

Xusheng Shen; Tao Xie; Tianzhen Wang. 2020. "A Fuzzy Adaptative Backstepping Control Strategy for Marine Current Turbine under Disturbances and Uncertainties." Energies 13, no. 24: 6550.

Research article
Published: 01 November 2020 in IET Power Electronics
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This study proposes a multi-mode fault-tolerant control (FTC) strategy, for cascaded H-bridge multilevel inverters, to provide the most suitable fault tolerance scheme for different single or double arbitrary insulated gate bipolar transistor (IGBT) faults. Firstly, all faulty modes are classified into three fault clusters according to the number and location of faulty IGBTs. Then, the matching mechanism of the multi-mode FTC strategy corresponding to the three clusters is described, and the appropriate FTC algorithm is selected for each cluster. In particular, a new FTC method has been proposed for Cluster I that includes double IGBT fault in different H-bridges and different groups. This method improves the utilisation efficiency of healthy IGBTs in the faulty H-bridges, and minimises the voltage level drop after the fault. Finally, the multi-mode FTC strategy has been applied to a seven-level inverter through simulation. Results have shown significant improvement of the output voltage waveform symmetry and harmonic distortion reduction for the three clusters. The new FTC method has been applied to an experimental five-level inverter. The obtained results confirm the validity of the methodology. With the proposed strategy, the output voltage has a lower harmonic distortion and a better symmetry thanks to the attenuation of the undesirable DC component.

ACS Style

Tianzhen Wang; Jiahui Zhang; Han Wang; Yide Wang; Demba Diallo; Mohamed Benbouzid. Multi‐mode fault‐tolerant control strategy for cascaded H‐bridge multilevel inverters. IET Power Electronics 2020, 13, 3119 -3126.

AMA Style

Tianzhen Wang, Jiahui Zhang, Han Wang, Yide Wang, Demba Diallo, Mohamed Benbouzid. Multi‐mode fault‐tolerant control strategy for cascaded H‐bridge multilevel inverters. IET Power Electronics. 2020; 13 (14):3119-3126.

Chicago/Turabian Style

Tianzhen Wang; Jiahui Zhang; Han Wang; Yide Wang; Demba Diallo; Mohamed Benbouzid. 2020. "Multi‐mode fault‐tolerant control strategy for cascaded H‐bridge multilevel inverters." IET Power Electronics 13, no. 14: 3119-3126.

Journal article
Published: 24 September 2020 in Entropy
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The conversion of marine current energy into electricity with marine current turbines (MCTs) promises renewable energy. However, the reliability and power quality of marine current turbines are degraded due to marine biological attachments on the blades. To benefit from all the information embedded in the three phases, we created a fault feature that was the derivative of the current vector modulus in a Concordia reference frame. Moreover, because of the varying marine current speed, fault features were non-stationary. A transformation based on new adaptive proportional sampling frequency (APSF) transformed them into stationary ones. The fault indicator was derived from the amplitude of the shaft rotating frequency, which was itself derived from its power spectrum. The method was validated with data collected from a test bed composed of a marine current turbine coupled to a 230 W permanent magnet synchronous generator. The results showed the efficiency of the method to detect an introduced imbalance fault with an additional mass of 80–220 g attached to blades. In comparison to methods that use a single piece of electrical information (phase current or voltage), the fault indicator based on the three currents was found to be, on average, 2.2 times greater. The results also showed that the fault indicator increased monotonically with the fault severity, with a 1.8 times-higher variation rate, as well as that the method is robust for the flow current speed that varies from 0.95 to 1.3 m/s.

ACS Style

Tao Xie; Tianzhen Wang; Demba Diallo; Hubert Razik. Imbalance Fault Detection Based on the Integrated Analysis Strategy for Marine Current Turbines under Variable Current Speed. Entropy 2020, 22, 1069 .

AMA Style

Tao Xie, Tianzhen Wang, Demba Diallo, Hubert Razik. Imbalance Fault Detection Based on the Integrated Analysis Strategy for Marine Current Turbines under Variable Current Speed. Entropy. 2020; 22 (10):1069.

Chicago/Turabian Style

Tao Xie; Tianzhen Wang; Demba Diallo; Hubert Razik. 2020. "Imbalance Fault Detection Based on the Integrated Analysis Strategy for Marine Current Turbines under Variable Current Speed." Entropy 22, no. 10: 1069.

Journal article
Published: 13 August 2020 in Energies
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With the wide application of lithium batteries, battery fault prediction and health management have become more and more important. This article proposes a method for predicting the remaining useful life (RUL) of lithium-ion batteries to avoid a series of safety problems caused by continuing to use the battery after reaching its service life threshold. Since the battery capacity is not easy to obtain online, we propose that some measurable parameters should be used in the battery discharge cycle to estimate battery capacity. Then, the estimated capacity is used to replace the measured value of the particle filter (PF) based on the Kendall rank correlation coefficient (KCCPF) to predict the RUL of the lithium batteries. Simulation results show that the proposed method has high prediction accuracy, stability, and practical value.

ACS Style

Diju Gao; Yong Zhou; Tianzhen Wang; Yide Wang. A Method for Predicting the Remaining Useful Life of Lithium-Ion Batteries Based on Particle Filter Using Kendall Rank Correlation Coefficient. Energies 2020, 13, 4183 .

AMA Style

Diju Gao, Yong Zhou, Tianzhen Wang, Yide Wang. A Method for Predicting the Remaining Useful Life of Lithium-Ion Batteries Based on Particle Filter Using Kendall Rank Correlation Coefficient. Energies. 2020; 13 (16):4183.

Chicago/Turabian Style

Diju Gao; Yong Zhou; Tianzhen Wang; Yide Wang. 2020. "A Method for Predicting the Remaining Useful Life of Lithium-Ion Batteries Based on Particle Filter Using Kendall Rank Correlation Coefficient." Energies 13, no. 16: 4183.

Research article
Published: 17 July 2020 in Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering
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To diagnose the attachment of marine current turbine, this article proposes a method based on convolutional neural network and the concepts of depthwise separable convolution to achieve feature extraction. The method consists of three steps: data preprocessing, feature extraction and fault diagnosis. This method can diagnose the fault degree of blade imbalance and uniform attachment in underwater environment with strong currents and complex spatiotemporal variability. It can extract distinct image feature in harsh marine environments by using a convolutional neural network. In addition, this method is robust for the recognition of blurred pictures under high-speed rotation.

ACS Style

Bin Xin; Yilai Zheng; Tianzhen Wang; Lisu Chen; Yide Wang. A diagnosis method based on depthwise separable convolutional neural network for the attachment on the blade of marine current turbine. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 2020, 1 .

AMA Style

Bin Xin, Yilai Zheng, Tianzhen Wang, Lisu Chen, Yide Wang. A diagnosis method based on depthwise separable convolutional neural network for the attachment on the blade of marine current turbine. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering. 2020; ():1.

Chicago/Turabian Style

Bin Xin; Yilai Zheng; Tianzhen Wang; Lisu Chen; Yide Wang. 2020. "A diagnosis method based on depthwise separable convolutional neural network for the attachment on the blade of marine current turbine." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering , no. : 1.

Journal article
Published: 05 June 2020 in Energies
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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).

ACS Style

Lotfi Saidi; Mohamed Benbouzid; Demba Diallo; Yassine Amirat; Elhoussin Elbouchikhi; Tianzhen Wang. Higher-Order Spectra Analysis-Based Diagnosis Method of Blades Biofouling in a PMSG Driven Tidal Stream Turbine. Energies 2020, 13, 2888 .

AMA Style

Lotfi Saidi, Mohamed Benbouzid, Demba Diallo, Yassine Amirat, Elhoussin Elbouchikhi, Tianzhen Wang. Higher-Order Spectra Analysis-Based Diagnosis Method of Blades Biofouling in a PMSG Driven Tidal Stream Turbine. Energies. 2020; 13 (11):2888.

Chicago/Turabian Style

Lotfi Saidi; Mohamed Benbouzid; Demba Diallo; Yassine Amirat; Elhoussin Elbouchikhi; Tianzhen Wang. 2020. "Higher-Order Spectra Analysis-Based Diagnosis Method of Blades Biofouling in a PMSG Driven Tidal Stream Turbine." Energies 13, no. 11: 2888.

Journal article
Published: 13 May 2020 in Energies
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This paper deals with a comparative study of two phasor estimators based on the least square (LS) and the linear Kalman filter (KF) methods, while assuming that the fundamental frequency is unknown. To solve this issue, the maximum likelihood technique is used with an iterative Newton–Raphson-based algorithm that allows minimizing the likelihood function. Both least square (LSE) and Kalman filter estimators (KFE) are evaluated using simulated and real power system events data. The obtained results clearly show that the LS-based technique yields the highest statistical performance and has a lower computation complexity.

ACS Style

Yassine Amirat; Zakarya Oubrahim; Hafiz Ahmed; Mohamed Benbouzid; Tianzhen Wang. Phasor Estimation for Grid Power Monitoring: Least Square vs. Linear Kalman Filter. Energies 2020, 13, 2456 .

AMA Style

Yassine Amirat, Zakarya Oubrahim, Hafiz Ahmed, Mohamed Benbouzid, Tianzhen Wang. Phasor Estimation for Grid Power Monitoring: Least Square vs. Linear Kalman Filter. Energies. 2020; 13 (10):2456.

Chicago/Turabian Style

Yassine Amirat; Zakarya Oubrahim; Hafiz Ahmed; Mohamed Benbouzid; Tianzhen Wang. 2020. "Phasor Estimation for Grid Power Monitoring: Least Square vs. Linear Kalman Filter." Energies 13, no. 10: 2456.

Journal article
Published: 04 March 2020 in Electronics
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In this paper, an effective strategy is presented to realize IGBT open-circuit fault diagnosis for closed-loop cascaded photovoltaic (PV) grid-connected inverters. The approach is based on the analysis of the inverter output voltage time waveforms in healthy and faulty conditions. It is mainly composed of two parts. The first part is to select the similar faults based on Euclidean distance and set the specific labels. The second part is the classification based on Principal Component Analysis and Support Vector Machine. The classification is done in two steps. In the first, similar faults are grouped to do the preliminary diagnosis of all fault types. In the second step the similar faults are discriminated. Compared with existing fault diagnosis strategies for several fundamental periods and under different external environments, the proposed strategy has better robustness and higher fault diagnosis accuracy. The effectiveness of the proposed fault diagnosis strategy is assessed through simulation results.

ACS Style

Wenyi Yuan; Tianzhen Wang; Demba Diallo; Claude Delpha. A Fault Diagnosis Strategy Based on Multilevel Classification for a Cascaded Photovoltaic Grid-Connected Inverter. Electronics 2020, 9, 429 .

AMA Style

Wenyi Yuan, Tianzhen Wang, Demba Diallo, Claude Delpha. A Fault Diagnosis Strategy Based on Multilevel Classification for a Cascaded Photovoltaic Grid-Connected Inverter. Electronics. 2020; 9 (3):429.

Chicago/Turabian Style

Wenyi Yuan; Tianzhen Wang; Demba Diallo; Claude Delpha. 2020. "A Fault Diagnosis Strategy Based on Multilevel Classification for a Cascaded Photovoltaic Grid-Connected Inverter." Electronics 9, no. 3: 429.

Journal article
Published: 10 February 2020 in IEEE Access
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Blade imbalance fault caused by the marine organisms is considered as the most important fault in marine current turbines. Therefore, it is important to accurately detect the fault in time to correct it, minimize the downtime, and maximize the productivity. Imbalance fault detection methods using generator stator current signals have attracted attentions due to their low cost, operability and stability compared to the ones using vibration analysis. However, it is difficult to extract the fault signature and automatically detect the imbalance fault under different flow velocity conditions. In this paper, a wavelet threshold denoising-based imbalance fault detection method using the stator current is proposed. The signal is analyzed through three consecutive steps: the parameters offline setting based on wavelet threshold denoising, the Hilbert transform method and the Principle Component Analysis-based detection method. With this method, the imbalance fault can be detected automatically. The proposed approach of imbalance fault detection is assessed under different flow velocity conditions and validated using an experimental platform. The results are promising with false alarm and false negatives rates less than 1respectively when using Q statistic. Moreover, the experimenta l% and 5% respectively when using Q statistic. Moreover, the experimental results show that the proposed method has good stability under different flow velocity conditions.

ACS Style

Zhichao Li; Tianzhen Wang; Yide Wang; Yassine Amirat; Mohamed Benbouzid; Demba Diallo. A Wavelet Threshold Denoising-Based Imbalance Fault Detection Method for Marine Current Turbines. IEEE Access 2020, 8, 29815 -29825.

AMA Style

Zhichao Li, Tianzhen Wang, Yide Wang, Yassine Amirat, Mohamed Benbouzid, Demba Diallo. A Wavelet Threshold Denoising-Based Imbalance Fault Detection Method for Marine Current Turbines. IEEE Access. 2020; 8 (99):29815-29825.

Chicago/Turabian Style

Zhichao Li; Tianzhen Wang; Yide Wang; Yassine Amirat; Mohamed Benbouzid; Demba Diallo. 2020. "A Wavelet Threshold Denoising-Based Imbalance Fault Detection Method for Marine Current Turbines." IEEE Access 8, no. 99: 29815-29825.

Journal article
Published: 04 February 2020 in Applied Sciences
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Lithium-ion battery on-line monitoring is challenging due to the unmeasurable characteristic of its internal states. Up to now, the most effective approach for battery monitoring is to apply advanced estimation algorithms based on equivalent circuit models. Besides, a usual method for estimating slowly varying unmeasurable parameters is to include them in the state vector with the zero-time derivative condition, which constitutes the so-called extended equivalent circuit model and has been widely used for the battery state and parameter estimation. Although various advanced estimation algorithms are applied to the joint estimation and dual estimation frameworks, the essence of these estimation frameworks has not been changed. Thus, the improvement of the battery monitoring result is limited. Therefore, a new battery monitoring structure is proposed in this paper. Firstly, thanks to the superposition principle, two sub-models are extracted. For the nonlinear one, an observability analysis is conducted. It shows that the necessary conditions for local observability depend on the battery current, the initial value of the battery capacity, and the square of the derivative of the open circuit voltage with respect to the state of charge. Then, the obtained observability analysis result becomes an important theoretical support to propose a new monitoring structure. Commonly used estimation algorithms, namely the Kalman filter, extended Kalman filter, and unscented Kalman filter, are selected and employed for it. Apart from providing a simultaneous estimation of battery open circuit voltage, more rapid and less fluctuating battery capacity estimation are the main advantages of the new proposed monitoring structure. Numerical studies using synthetic data have proven the effectiveness of the proposed framework.

ACS Style

Jianwen Meng; Moussa Boukhnifer; Demba Diallo; Tianzhen Wang. A New Cascaded Framework for Lithium-Ion Battery State and Parameter Estimation. Applied Sciences 2020, 10, 1009 .

AMA Style

Jianwen Meng, Moussa Boukhnifer, Demba Diallo, Tianzhen Wang. A New Cascaded Framework for Lithium-Ion Battery State and Parameter Estimation. Applied Sciences. 2020; 10 (3):1009.

Chicago/Turabian Style

Jianwen Meng; Moussa Boukhnifer; Demba Diallo; Tianzhen Wang. 2020. "A New Cascaded Framework for Lithium-Ion Battery State and Parameter Estimation." Applied Sciences 10, no. 3: 1009.

Journal article
Published: 03 June 2019 in Energies
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Affected by high density, non-uniform, and unstructured seawater environment, fault detection of Marine Current Turbine (MCT) faces various fault features and strong interferences. To solve these problems, a harmonic analysis strategy based on zero-crossing estimation and Empirical Mode Decomposition (EMD) filter banks is proposed. First, the detection problems of rotor imbalance fault under strong interference conditions are described through an analysis of the fault mechanism and operation environment of MCT. Therefore, against various fault features, a zero-crossing estimation is proposed to calculate instantaneous frequency. Last, and in order to solve the problem that the frequency and amplitude of the operating parameters are partially or completely covered by interference, a band-pass filter based on EMD is used, together with a characteristic frequency selected by a Pearson correlation coefficient. This strategy can accurately detect the multiplicative faults under strong interference conditions, and can be applied to the MCT fault detection system. Theoretical and experimental results verify the effectiveness of the proposed strategy.

ACS Style

Milu Zhang; Tianzhen Wang; Tianhao Tang; Zhuo Liu; Christophe Claramunt. A Synchronous Sampling Based Harmonic Analysis Strategy for Marine Current Turbine Monitoring System under Strong Interference Conditions. Energies 2019, 12, 2117 .

AMA Style

Milu Zhang, Tianzhen Wang, Tianhao Tang, Zhuo Liu, Christophe Claramunt. A Synchronous Sampling Based Harmonic Analysis Strategy for Marine Current Turbine Monitoring System under Strong Interference Conditions. Energies. 2019; 12 (11):2117.

Chicago/Turabian Style

Milu Zhang; Tianzhen Wang; Tianhao Tang; Zhuo Liu; Christophe Claramunt. 2019. "A Synchronous Sampling Based Harmonic Analysis Strategy for Marine Current Turbine Monitoring System under Strong Interference Conditions." Energies 12, no. 11: 2117.

Journal article
Published: 09 April 2019 in Energies
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This paper deals with open switch Fault Detection and Diagnosis (FDD) in three-level Neutral Point Clamped (NPC) inverter for electrical drives. The approach is based on the already available phase current time series measurements for different operating conditions (motor speed, load, and environment noise). Both fault detection and classification are studied and the efficiency performances of the proposed selected features are shown. For the fault detection, we focus on the first four statistical moments and the extracted features and then the Cumulative Sum (CUSUM) algorithm as the feature analysis technique to improve the performances. For the classification study, we propose to couple the knowledge on the faulty system brought by the statistical moments and the Kullback-Leibler divergence particularly suitable for the detection of incipient changes. The Principal Component Analysis (PCA) is then used to perform the classification. A 2D framework is obtained, which allows the faults to be classified efficiently within the considered operating conditions for all the selected fault durations.

ACS Style

Mehdi Baghli; Claude Delpha; Demba Diallo; Abdelhamid Hallouche; David Mba; Tianzhen Wang. Three-Level NPC Inverter Incipient Fault Detection and Classification using Output Current Statistical Analysis. Energies 2019, 12, 1372 .

AMA Style

Mehdi Baghli, Claude Delpha, Demba Diallo, Abdelhamid Hallouche, David Mba, Tianzhen Wang. Three-Level NPC Inverter Incipient Fault Detection and Classification using Output Current Statistical Analysis. Energies. 2019; 12 (7):1372.

Chicago/Turabian Style

Mehdi Baghli; Claude Delpha; Demba Diallo; Abdelhamid Hallouche; David Mba; Tianzhen Wang. 2019. "Three-Level NPC Inverter Incipient Fault Detection and Classification using Output Current Statistical Analysis." Energies 12, no. 7: 1372.

Journal article
Published: 17 March 2019 in Information
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This paper presents an approach to detect and classify the faults in complex systems with small amounts of available data history. The methodology is based on the model fusion for fault detection and classification. Moreover, the database is enriched with additional samples if they are correctly classified. For the fault detection, the kernel principal component analysis (KPCA), kernel independent component analysis (KICA) and support vector domain description (SVDD) were used and combined with a fusion operator. For the classification, extreme learning machine (ELM) was used with different activation functions combined with an average fusion function. The performance of the methodology was evaluated with a set of experimental vibration data collected from a test-to-failure bearing test rig. The results show the effectiveness of the proposed approach compared to conventional methods. The fault detection was achieved with a false alarm rate of 2.29% and a null missing alarm rate. The data is also successfully classified with a rate of 99.17%.

ACS Style

Tianzhen Wang; Jingjing Dong; Tao Xie; Demba Diallo; Mohamed Benbouzid. A Self-Learning Fault Diagnosis Strategy Based on Multi-Model Fusion. Information 2019, 10, 116 .

AMA Style

Tianzhen Wang, Jingjing Dong, Tao Xie, Demba Diallo, Mohamed Benbouzid. A Self-Learning Fault Diagnosis Strategy Based on Multi-Model Fusion. Information. 2019; 10 (3):116.

Chicago/Turabian Style

Tianzhen Wang; Jingjing Dong; Tao Xie; Demba Diallo; Mohamed Benbouzid. 2019. "A Self-Learning Fault Diagnosis Strategy Based on Multi-Model Fusion." Information 10, no. 3: 116.

Article
Published: 17 February 2019 in Sensors
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The development and application of marine current energy are attracting more and more attention around the world. Due to the hardness of its working environment, it is important and difficult to study the fault diagnosis of a marine current generation system. In this paper, an underwater image is chosen as the fault-diagnosing signal, after different sensors are compared. This paper proposes a diagnosis method based on the sparse autoencoder (SA) and softmax regression (SR). The SA is used to extract the features and SR is used to classify them. Images are used to monitor whether the blade is attached by benthos and to determine its corresponding degree of attachment. Compared with other methods, the experiment results show that the proposed method can diagnose the blade attachment with higher accuracy.

ACS Style

Yilai Zheng; Tianzhen Wang; Bin Xin; Tao Xie; Yide Wang. A Sparse Autoencoder and Softmax Regression Based Diagnosis Method for the Attachment on the Blades of Marine Current Turbine. Sensors 2019, 19, 826 .

AMA Style

Yilai Zheng, Tianzhen Wang, Bin Xin, Tao Xie, Yide Wang. A Sparse Autoencoder and Softmax Regression Based Diagnosis Method for the Attachment on the Blades of Marine Current Turbine. Sensors. 2019; 19 (4):826.

Chicago/Turabian Style

Yilai Zheng; Tianzhen Wang; Bin Xin; Tao Xie; Yide Wang. 2019. "A Sparse Autoencoder and Softmax Regression Based Diagnosis Method for the Attachment on the Blades of Marine Current Turbine." Sensors 19, no. 4: 826.

Journal article
Published: 01 January 2019 in Mechanical Systems and Signal Processing
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Marine current power is a new renewable energy source, it is significant to keep the marine current turbine healthy. But harsh marine conditions, such as regular thermal cycling, high humidity and heavy salt mist, bring severe challenges to the marine current turbine, especially to the winding insulation system. Therefore, it is of great importance to perform fault detection on the insulation system. In this paper, a Modified Extended Kalman Filter (M-EKF) fault detection strategy based on the electrical parametric model derived from the RLC network modeling of Roebel bars is proposed. The proposed fault detection strategy mainly includes two parts. The first step is to establish reasonable state space equations for the model structure extracted to monitor the state of the insulation system. In the second step, the conventional continuous EKF is modified to follow more accurately the state of the marine current turbine. Simulation and experiment results show that the proposed method can realize timely monitoring of the winding insulation system of marine current turbine with good performance.

ACS Style

Tianzhen Wang; Lei Liu; Jiahui Zhang; Emmanuel Schaeffer; Yide Wang. A M-EKF fault detection strategy of insulation system for marine current turbine. Mechanical Systems and Signal Processing 2019, 115, 269 -280.

AMA Style

Tianzhen Wang, Lei Liu, Jiahui Zhang, Emmanuel Schaeffer, Yide Wang. A M-EKF fault detection strategy of insulation system for marine current turbine. Mechanical Systems and Signal Processing. 2019; 115 ():269-280.

Chicago/Turabian Style

Tianzhen Wang; Lei Liu; Jiahui Zhang; Emmanuel Schaeffer; Yide Wang. 2019. "A M-EKF fault detection strategy of insulation system for marine current turbine." Mechanical Systems and Signal Processing 115, no. : 269-280.

Journal article
Published: 01 July 2018 in Energies
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In this paper, a hybrid ship powered by diesel generator sets and power batteries in series is considered. By analyzing the characteristics of hybrid ship cycle operating conditions, the load power of the hybrid ship under load uncertainty is firstly predicted. Then, considering the economy, emissions and continuous navigation time (endurance) of the hybrid ship, an energy optimization strategy based on the predicted load power is proposed to achieve the goal of minimum fuel consumption, minimum emissions and maximum endurance of ship operation. The experimental results show that, compared with the fuzzy logic rules based strategy, the fuel economy of the ship is increased by 9.6% and the ship’s endurance is increased by 24% for the proposed strategy.

ACS Style

Diju Gao; Xuyang Wang; Tianzhen Wang; Yide Wang; Xiaobin Xu. An Energy Optimization Strategy for Hybrid Power Ships under Load Uncertainty Based on Load Power Prediction and Improved NSGA-II Algorithm. Energies 2018, 11, 1699 .

AMA Style

Diju Gao, Xuyang Wang, Tianzhen Wang, Yide Wang, Xiaobin Xu. An Energy Optimization Strategy for Hybrid Power Ships under Load Uncertainty Based on Load Power Prediction and Improved NSGA-II Algorithm. Energies. 2018; 11 (7):1699.

Chicago/Turabian Style

Diju Gao; Xuyang Wang; Tianzhen Wang; Yide Wang; Xiaobin Xu. 2018. "An Energy Optimization Strategy for Hybrid Power Ships under Load Uncertainty Based on Load Power Prediction and Improved NSGA-II Algorithm." Energies 11, no. 7: 1699.