Abstract
Wind farm operators are overwhelmed by a large amount of supervisory control and data acquisition (SCADA) alarms when faults occur. This paper presents an online root fault identification method for SCADA alarms to assist operators in wind turbine fault diagnosis. The proposed method is based on the similarity analysis between an unknown alarm vector and the feature vectors of known faults. The alarm vector is obtained from segmented alarm lists, which are filtered and simplified. The feature vector, which is a unique signature representing the occurrence of a fault, is extracted from the alarm lists belonging to the same fault. To mine the coupling correspondence between alarms and faults, we define the weights of the alarms in each fault. The similarities is measured by the weighted Euclidean distance and the weighted Hamming distance, respectively. One year of SCADA alarms and maintenance records are used to verify the proposed method. The results show that the performance of the weighted Hamming distance is better than that of the weighted Euclidean distance; 84.1% of alarm lists are labeled with the right root fault.
Highlights
As wind power installations continue worldwide, wind power is in a rapid transition toward becoming a fully commercialized, unsubsidized technology
This paper aims to solve the above problems existing in wind turbine supervisory control and data acquisition (SCADA) alarms and assist the operator in wind-turbine fault diagnosis
Motivated by the shortcomings of the existing methods in the wind power domain, this paper proposes an online fault diagnosis method for SCADA alarms based on a similarity analysis
Summary
As wind power installations continue worldwide, wind power is in a rapid transition toward becoming a fully commercialized, unsubsidized technology. It is vital to reduce the levelized wind power energy cost for enhancing the competitiveness of wind farms during the transition to fully commercial, market-based operations. Due to the remote and harsh operational environment, the operation and maintenance (O&M) costs of wind farms are high. For an offshore wind farm, the O&M costs account for up to 14–30% [2]. Modern wind turbines use hundreds of sensors and actuators as parts of their many control loops. This situation can result in a large number of measured variables and their corresponding configured alarms. A wind turbine SCADA system is integrated with an alarm function, which monitors the condition of wind turbines and their subcomponents
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