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Development of indicators for the detection of equipment malfunctions and degradation estimation based on digital signals (alarms and events) from operation SCADA

Author

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  • Rodríguez-López, Miguel A.
  • López-González, Luis M.
  • López-Ochoa, Luis M.
  • Las-Heras-Casas, Jesús

Abstract

Certain mechanical and electrical components, such as generators, exhibit degradation phenomena, which may develop slowly over time or suddenly. The current trend in this field of research is to develop malfunction detection indicators from analog signals recorded by operation supervisory control and data acquisition (SCADA), creating behavioral models of the equipment and the development of a series of status indicators. These models and indicators are used to detect malfunctions when operation SCADA are unable to detect an abnormality, thus determining that the component is beginning to degrade when certain normal limits are exceeded. However, the digital signals from operation SCADA have great potential for providing additional information that could be used to detect possible malfunction. Detection must be accompanied by a study of the remaining life of a component so that the remaining useful life of the component before failure can be estimated before losing its functionality. If SCADA can detect a malfunction and determine when the component will break, the operator will have valuable time to intervene prior to failure at an optimum time. This is particularly important in installations with difficult access, such as offshore wind farms.

Suggested Citation

  • Rodríguez-López, Miguel A. & López-González, Luis M. & López-Ochoa, Luis M. & Las-Heras-Casas, Jesús, 2016. "Development of indicators for the detection of equipment malfunctions and degradation estimation based on digital signals (alarms and events) from operation SCADA," Renewable Energy, Elsevier, vol. 99(C), pages 224-236.
    • Handle: RePEc:eee:renene:v:99:y:2016:i:c:p:224-236
    DOI: 10.1016/j.renene.2016.06.056
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    1. Angel Gil & Miguel A. Sanz-Bobi & Miguel A. Rodríguez-López, 2018. "Behavior Anomaly Indicators Based on Reference Patterns—Application to the Gearbox and Electrical Generator of a Wind Turbine," Energies, MDPI, vol. 11(1), pages 1-15, January.
    2. Miguel A. Rodríguez-López & Luis M. López-González & Luis M. López-Ochoa & Jesús Las-Heras-Casas, 2018. "Methodology for Detecting Malfunctions and Evaluating the Maintenance Effectiveness in Wind Turbine Generator Bearings Using Generic versus Specific Models from SCADA Data," Energies, MDPI, vol. 11(4), pages 1-22, March.
    3. Cristian Velandia-Cardenas & Yolanda Vidal & Francesc Pozo, 2021. "Wind Turbine Fault Detection Using Highly Imbalanced Real SCADA Data," Energies, MDPI, vol. 14(6), pages 1-26, March.

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