A Synchronous Sampling Based Harmonic Analysis Strategy for Marine Current Turbine Monitoring System under Strong Interference Conditions

Zhang, Milu; Wang, Tianzhen; Tang, Tianhao; Liu, Zhuo; Claramunt, Christophe

doi:10.3390/en12112117

Cited by 11 publications

(6 citation statements)

References 16 publications

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“…The methods in [18], [26] and [27] mainly rely on searching the fault peak near the fault characteristic frequency to detect the imbalance fault. It is needed to manually decide whether there is an imbalance fault.…”

Section: B Imbalance Fault Detection Results and Analysis 1) Detectimentioning

confidence: 99%

“…The imbalance fault signature comes from the torque distortion on the turbine [27]. However, in marine environment, the turbulence and waves will bring interferences to the stator current signal [28].…”

Section: Problem Descriptionmentioning

confidence: 99%

“…All of the above methods have made great contributions to the fault detection, but the continuously changing flow velocity has not been fully considered. The fault characteristic frequency is not constant and changes with the flow velocity [26], [27], which will lead to different fault characteristic frequencies at different times. However, the main objective of the existing imbalance fault detection methods is to highlight the fault characteristic frequency, which means that the stability of these methods needs to be improved under different flow velocity conditions.…”

Section: Introductionmentioning

confidence: 99%

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A Wavelet Threshold Denoising-Based Imbalance Fault Detection Method for Marine Current Turbines

Wang²,

Wang³

et al. 2020

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 detect the fault accurately and quickly to mitigate its effect, 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 approach, the imbalance fault can be detected automatically. The 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 negative rates less than 1% and 5% respectively when using Q statistic. Moreover, the experimental results show that the proposed method has good stability under different flow velocity conditions. INDEX TERMS Hilbert transform, imbalance fault, marine current turbine, principle component analysis, wavelet.

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Section: B Imbalance Fault Detection Results and Analysis 1) Detectimentioning

confidence: 99%

Section: Problem Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Wavelet Threshold Denoising-Based Imbalance Fault Detection Method for Marine Current Turbines

Wang²,

Wang³

et al. 2020

IEEE Access

Self Cite

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“…Early fault detection can identify current status based on historical operating information of process or equipment and give a warning on the abnormal operation, thereby providing enough time to take mitigation measures. Early fault detection strategy has been widely applied in different industrial fields [3][4][5]. For example, a mechanism model was constructed to access the performance of airport landing lightening system in [6]; Odgaard et al [7] developed a fault detection model of the coal mill based on energy balance.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Early Fault Detection Model of Induced Draft Fans Based on Multivariate State Estimation Technique

Guo¹,

Zhang²,

Zhang³

et al. 2021

Preprint

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The induced draft (ID) fan is important auxiliary equipment in the thermal power plant. It is of great significance to monitor the operation of the ID fan for safe and efficient production. In this paper, an adaptive warning model is proposed to detect early faults of ID fans. First, a non-parametric monitoring model is constructed to describe the normal operation states with the multivariate state estimation technique (MSET). Then, an early warning approach is presented to identify abnormal behaviors based on the results of the MSET model. As the performance of the MSET model is heavily influenced by the normal operation data in the historic memory matrix, an adaptive strategy is proposed by using the samples with a high data quality index (DQI) to manage the memory matrix and update the model. The proposed method is applied to a 300 MW coal-fired power plant for early fault detection, and it is compared with the model without an update. Results show that the proposed method can detect the fault earlier and more accurately.

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“…An early fault detection strategy can identify the current status based on historical operating information of process or equipment and give a warning signal on the abnormal operation, thereby providing enough time to take risk mitigation measures. Early fault detection models have been widely applied in different industrial fields [3][4][5][6][7]. Some models are developed according to the physical mechanism; however, it is usually difficult to obtain adequate knowledge for a non-linear complex industrial process, thus affecting the prediction accuracy in condition monitoring.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Early Fault Detection Model of Induced Draft Fans Based on Multivariate State Estimation Technique

Guo

Zhang

et al. 2021

Energies

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The induced draft (ID) fan is an important piece of auxiliary equipment in coal-fired power plants. Early fault detection of the ID fan can provide predictive maintenance and reduce unscheduled shutdowns, thus improving the reliability of the power generation. In this study, an adaptive model was developed to achieve the early fault detection of ID fans. First, a non-parametric monitoring model was constructed to describe the normal operating characteristics with the multivariate state estimation technique (MSET). A similarity index representing operation status was defined according to the prediction deviations to produce warnings of early faults. To deal with the model accuracy degradation because of variant condition operation of the ID fan, an adaptive strategy was proposed by using the samples with a high data quality index (DQI) to manage the memory matrix and update the MSET model, thereby improving the fault detection results. The proposed method was applied to a 300 MW coal-fired power plant to achieve the early fault detection of an ID fan. In addition, fault detection by using the model without an update was also compared. Results show that the update strategy can greatly improve the MSET model accuracy when predicting normal operations of the ID fan; accordingly, the fault can be detected more than 4 h earlier by using the strategy with the adaptive update when compared to the model without an update.

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A Synchronous Sampling Based Harmonic Analysis Strategy for Marine Current Turbine Monitoring System under Strong Interference Conditions

Cited by 11 publications

References 16 publications

A Wavelet Threshold Denoising-Based Imbalance Fault Detection Method for Marine Current Turbines

A Wavelet Threshold Denoising-Based Imbalance Fault Detection Method for Marine Current Turbines

An Adaptive Early Fault Detection Model of Induced Draft Fans Based on Multivariate State Estimation Technique

An Adaptive Early Fault Detection Model of Induced Draft Fans Based on Multivariate State Estimation Technique

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