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<p>Supervisory Control and Data Acquisition (SCADA) systems are employed to collect data from sensors and monitor the condition of wind turbines. Thresholds are commonly used to set the alarms, generating many false alarms, downtimes, costs, etc. A real case study is presented to validate the approach. This paper proposes a novel approach based on Fuzzy Logic to analyse the main variables of the SCADA. Pearson’s correlation between variables is employed to reduce the number of variables that are used as inputs in the Fuzzy Logic system. The variables with perfect and strong correlations have been selected as inputs of the Fuzzy system. The signal is studied by considering the difference between the signal and the moving average value because it shows if the signal is close or not to the value in conditions free of faults. The thresholds are used to cluster the data into three groups by a statistical analysis of the new variables, i.e., the variables obtained by the difference between the signal and the moving average value. The approach helps decrease false alarms by using a Fuzzy system. The approach is capable of processing large datasets online. The results have been validated by employing SVM, where the MAPE is analysed between both methods. </p>

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Alarms management with fuzzy logic using wind turbine SCADA systems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年1月14日

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  • Fausto Pedro Garcia Márquez,
  • Tahar Benmessaoud,
  • Kamal Mohammedi,
  • Alberto Pliego Marugán

摘要

Supervisory Control and Data Acquisition (SCADA) systems are employed to collect data from sensors and monitor the condition of wind turbines. Thresholds are commonly used to set the alarms, generating many false alarms, downtimes, costs, etc. A real case study is presented to validate the approach. This paper proposes a novel approach based on Fuzzy Logic to analyse the main variables of the SCADA. Pearson’s correlation between variables is employed to reduce the number of variables that are used as inputs in the Fuzzy Logic system. The variables with perfect and strong correlations have been selected as inputs of the Fuzzy system. The signal is studied by considering the difference between the signal and the moving average value because it shows if the signal is close or not to the value in conditions free of faults. The thresholds are used to cluster the data into three groups by a statistical analysis of the new variables, i.e., the variables obtained by the difference between the signal and the moving average value. The approach helps decrease false alarms by using a Fuzzy system. The approach is capable of processing large datasets online. The results have been validated by employing SVM, where the MAPE is analysed between both methods.