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

• Published in: Renewable energy Vol. 99; pp. 224 - 236
• Main Authors: Rodríguez-López, Miguel A., López-González, Luis M., López-Ochoa, Luis M., Las-Heras-Casas, Jesús
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2016
• Subjects: Digital signals; Failure detection; Remaining life; SCADA data; Status monitoring; Wind turbines; Failure detection; Wind turbines; Digital signals; SCADA data; Remaining life; Status monitoring
• ISSN: 0960-1481; 1879-0682
• DOI: 10.1016/j.renene.2016.06.056

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. •Equipment normal behavior models were developed using alarms and digital signals.•Weighing alarms operation states the methodology determines bearing degradation.•To develop the methodology, statistical ratios and moving means were used jointly.•Shifts in the degradation indicators suggest malfunctions in the generator bearing.•The digital indicator shows excellent capacity for detecting abnormal degradations.