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  • 1.
    Cui, Yue
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electromagnetic Engineering.
    Bangalore, Pramod
    Greenbyte AB, Gothenburg, Sweden.
    Bertling Tjernberg, Lina
    KTH, School of Electrical Engineering and Computer Science (EECS), Electromagnetic Engineering.
    An anomaly detection approach based on machine learning and scada data for condition monitoring of wind turbines2018In: 2018 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2018Conference paper (Refereed)
    Abstract [en]

    This paper presents an anomaly detection approach using machine learning to achieve condition monitoring for wind turbines. The approach applies the information in supervisory control and data acquisition systems as data input. First, machine learning is used to estimate the temperature signals of the gearbox component. Then the approach analyzes the deviations between the estimated values and the measurements of the signals. Finally, the information of alarm logs is integrated with the previous analysis to determine the operation states of wind turbines. The proposed approach has been tested with the data experience of a 2MW wind turbine in Sweden. The result demonstrates that the approach can detect possible anomalies before the failure occurrence. It also certifies that the approach can remind operators of the possible changes inside wind turbines even when the alarm logs do not report any alarms.

  • 2.
    Cui, Yue
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electromagnetic Engineering.
    Bangalore, Pramod
    Greenbyte AB, Gothenburg, Sweden..
    Bertling Tjernberg, Lina
    KTH, School of Electrical Engineering and Computer Science (EECS), Electromagnetic Engineering.
    An Anomaly Detection Approach Using Wavelet Transform and Artificial Neural Networks for Condition Monitoring of Wind Turbines' Gearboxes2018In: 2018 POWER SYSTEMS COMPUTATION CONFERENCE (PSCC), IEEE , 2018Conference paper (Refereed)
    Abstract [en]

    This paper presents an anomaly detection approach using artificial neural networks and the wavelet transform for the condition monitoring of wind turbines. The method aims to attain early anomaly detection and to prevent possible false alarms under healthy operations. In the approach, nonlinear autoregressive neural networks are used to estimate the temperature signals of the gearbox. The Mahalanobis distances are then calculated to measure the deviations between the current states and healthy operations. Next, the wavelet transform is applied to remove noisy signals in the distance values. Finally, the operation information is considered together with the refined distance values to detect potential anomalies. The proposed approach has been tested with the real data of three 2 MW wind turbines in Sweden. The results show that the approach can detect possible anomalies before failure events occur and avoid reporting alarms under healthy operations.

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