Wind Turbine Blade Monitoring with Brillouin-Based Fiber-Optic Sensors

Coscetta, Agnese; Minardo, Aldo; Olivares, Lucio; Mirabile, M.; Longo, Mario; Damiano, M.; Zeni, Luigi

doi:10.1155/2017/9175342

Cited by 21 publications

(15 citation statements)

References 8 publications

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“…In dynamic sensing applications, the acquisition time must be short enough to catch the relevant information. The required acquisition rate is very much dependent on the intended application, ranging from a few Hz in case of e.g., seismic monitoring [ 12 ] and wind turbine blade monitoring [ 13 , 14 ], up to several kHz in case of e.g., railway traffic monitoring [ 15 ] and pipeline leakage monitoring [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Distributed Dynamic Strain Sensing Based on Brillouin Scattering in Optical Fibers

Coscetta

Minardo

Zeni

2020

Sensors

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Over the past three decades, extensive research activity on Brillouin scattering-based distributed optical fiber sensors has led to the availability of commercial instruments capable of measuring the static temperature/strain distribution over kilometer distances and with high spatial resolution, with applications typically covering structural and environmental monitoring. At the same time, the interest in dynamic measurements has rapidly grown due to the relevant number of applications which could benefit from this technology, including structural analysis for defect identification, vibration detection, railway traffic monitoring, shock events detection, and so on. In this paper, we present an overview of the recent advances in Brillouin-based distributed optical fiber sensors for dynamic sensing. The aspects of the Brillouin scattering process relevant in distributed dynamic measurements are analyzed, and the different techniques are compared in terms of performance and hardware complexity.

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Section: Introductionmentioning

confidence: 99%

Distributed Dynamic Strain Sensing Based on Brillouin Scattering in Optical Fibers

Coscetta

Minardo

Zeni

2020

Sensors

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“…Common sensor types employed for this aim are vibration, acoustic emission, strain, torque, and bending moment sensors. Some past researches on wind turbine fault detection using specific sensors can be found in [16][17][18][19][20][21][22][23][24]. Both two data gathering methods have certain advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection of Wind Turbine Sensors Using Artificial Neural Networks

Gokcen

Barutçu

2018

Journal of Sensors

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This paper proposes a method for sensor validation and fault detection in wind turbines. Ensuring validity of sensor measurements is a significant part in overall condition monitoring as sensor faults lead to incorrect results in monitoring a system's state of health. Although identifying abrupt failures in sensors is relatively straightforward, calibration drifts are more difficult to detect. Therefore, a detection and isolation technique for sensor calibration drifts on the purpose of measurement validation was developed. Temperature sensor measurements from the Supervisory Control and Data Acquisition system of a wind turbine were used for this aim. Low output rate of the measurements and nonlinear characteristics of the system drive the necessity to design an advanced fault detection algorithm. Artificial neural networks were chosen for this purpose considering their high performance in nonlinear environments. The results demonstrate that the proposed method can effectively detect existence of calibration drift and isolate the exact sensor with faulty behaviour.

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“…Changes in fiber temperature or strain will alter the corresponding peak values of gain, frequency shift and line width (full width at half maximum, FWHM) of Brillouin gain spectra. The temperature or strain along the fiber can be measured by analysis of the Brillouin gain spectrum, therefore, fiber distributed sensing based on Brillouin scattering is extensively used in diverse industrial and scientific fields [ 1 , 2 , 3 , 4 , 5 , 6 ]. The most intensively investigated topics with regard to the fiber distributed temperature and strain measurement based on Brillouin scattering include how to improve the accuracy, spatial resolution and widen the measurement range.…”

Section: Introductionmentioning

confidence: 99%

Key Parameter Extraction for Fiber Brillouin Distributed Sensors Based on the Exact Model

Zhi-niu

Zhao

2018

Sensors

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Errors in the extracted key parameters directly influence the errors in the temperature and strain measured by fiber Brillouin distributed sensors. Existing key parameter extraction algorithms for Brillouin gain spectra are mainly based on simplified models, therefore, the extracted parameters may have significant errors. To ensure high accuracy in the extracted key parameters in different cases, and consequently to measure temperature and strain with high accuracy, a key parameter extraction algorithm based on the exact Voigt profile is proposed. The objective function is proposed using the least-squares method. The Levenberg-Marquardt algorithm is used to minimize the objective function and consequently extract the key parameters. The optimization process is presented in detail, at the same time the initial values obtainment method and the convergence criterion are given. The influences of the number of sample points in Gauss-Hermite quadrature on the accuracy and the computation time of the algorithm are investigated and a suggestion about the selection of the number of sample points is given. The direct algorithm, the random algorithm and the proposed algorithm are implemented in Matlab and are used to extract key parameters for abundant numerically generated and measured Brillouin gain spectral signals. The results reveal that the direct algorithm requires less computation time, but its errors are considerably larger than that of the proposed algorithm. The convergence rate of the random algorithm is about 80~90%. The proposed algorithm can converge in all cases. Even for the convergence cases, the computation time and the fitting error of the random algorithm are 1~2 times larger than those of the proposed algorithm.

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Wind Turbine Blade Monitoring with Brillouin-Based Fiber-Optic Sensors

Cited by 21 publications

References 8 publications

Distributed Dynamic Strain Sensing Based on Brillouin Scattering in Optical Fibers

Distributed Dynamic Strain Sensing Based on Brillouin Scattering in Optical Fibers

Fault Detection of Wind Turbine Sensors Using Artificial Neural Networks

Key Parameter Extraction for Fiber Brillouin Distributed Sensors Based on the Exact Model

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