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  • A SCADA-Data-Driven Condition Monitoring Method of Wind Turbine Generators
    To detect early faults or abnormal conditions of wind turbine generator components, a wind turbine generator condition monitoring framework based on the fusion of cascaded SAE abnormal condition monitoring and LightGBM abnormal condition classification is proposed The framework consists of two parts
  • A SCADA-Data-Driven Condition Monitoring Method of Wind Turbine Generators
    To detect early faults or abnormal conditions of wind turbine generator components, a wind turbine generator condition monitoring framework based on the fusion of cascaded SAE
  • A review of SCADA-based condition monitoring for wind turbines via . . .
    This survey systematically reviews SCADA-based wind turbine condition monitoring methods within five years, emphasizing neural networks as key approaches, and structures the discussion around three core aspects: data preprocessing, classification models, and regression models
  • SCADA data based condition monitoring of wind turbines
    This paper dwells upon the techniques methods algorithms developed, to carry out diagnosis and prognosis of the faults, based upon SCADA data Subsequently data driven approaching for SCADA data interpretation has been reviewed and an artificial intelligence (AI) based framework for fault diagnosis and prognosis of WTs using SCADA data is proposed
  • A SCADA-Data-Driven Condition Monitoring Method of Wind Turbine . . .
    The results of the case study show that the proposed condition monitoring has high anomaly recognition capability: the cascaded SAE method has strong anti-interference properties and can capture the early abnormal conditions of wind turbine generators; LightGBM has a faster training speed than other classifiers with guaranteed abnormality classi
  • A SCADA-Data-Driven Condition Monitoring Method of Wind Turbine Generators
    To detect early faults or abnormal conditions of wind turbine generator components, a wind turbine generator condition monitoring framework based on the fusion of cascaded SAE abnormal condition monitoring and LightGBM abnormal condition classification is proposed
  • Condition Monitoring of Wind Turbine Generators Using SCADA Data . . .
    In this article, an ensemble approach is proposed to detect anomalies and diagnose faults in wind turbines Historical SCADA data collected from healthy wind turbines are used to model their normal behaviors and build a Mahalanobis space as a reference space
  • Using SCADA Data for Wind Turbine Condition Monitoring: A . . . - MDPI
    Artificial intelligence (AI) techniques can convert SCADA data into information that can be used for early detection of WT failures This work presents a systematic literature review (SLR) with the aim to assess the use of SCADA data and AI for CM of WTs
  • Wind turbine condition monitoring by the approach of SCADA data . . .
    The major contributions of this paper include: (1) develop an effective method for processing raw SCADA data; (2) propose an alternative condition monitoring technique based on investigating the correlations among relevant SCADA data; and (3) realise the quantitative assessment of the health condition of a turbine under varying operational
  • A SCADA-Data-Driven Condition Monitoring Method of Wind Turbine Generators
    To detect early faults or abnormal conditions of wind turbine generator components, a wind turbine generator condition monitoring framework based on the fusion of cascaded SAE abnormal condition monitoring and LightGBM abnormal condition classification is proposed The framework consists of two parts
  • SCADA data based condition monitoring of wind turbines - Springer
    WTs are equipped with some kind of condition monitoring device system, which provides the information about the device to the central data base i e , supervisory control and data acquisition (SCADA) data base These devices sys-tems make use of data processing techniques methods in order to detect and predict faults The information provided
  • A Condition Monitoring and Fault Isolation System for Wind Turbine . . .
    First, a covariate-adjusted preprocessing procedure is pro-posed to account for the various working conditions of the wind turbine Next, we construct a global monitoring statistic based on





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