Analysis of SCADA data for early fault detection with application to the maintenance management of wind turbines
Paper i proceeding, 2016
During the past decade wind turbines have proven to be a promising source of renewable power. Wind turbines are generally placed in remote locations and are subject to harsh environmental conditions throughout their lifetimes. Consequently, the failures in wind turbines are expensive to repair and cause loss of revenue due to long down times. Asset management in wind turbines can aid in assessing and improving the reliability and availability of wind turbines, thereby making them more competitive. Maintenance policies play an important role in asset management and different maintenance models have been developed for wind turbine applications. Typically, mathematical models for maintenance optimization provide either an age based or a condition based preventive maintenance schedule. Age based preventive maintenance schedules provide the owner with the possibility to financially plan for maintenance activities for the entire lifetime of the wind turbine by providing the expected number of replacements for each component. However, age based preventive maintenance schedule may not consume the operating life of the wind turbine components to the maximum. Condition based maintenance scheduling has the advantage of better utilizing the operating life of the components.
This paper proposes a wind turbine maintenance management framework which utilizes operation and maintenance data from different sources to combine the benefits of age based and condition based maintenance scheduling. This paper also presents an artificial neural network (ANN) based condition monitoring method which utilizes data from supervisory control and data acquisition (SCADA) system to detect failures in wind turbine components and systems. The procedures to construct ANN models for condition monitoring application are outlined. In order to demonstrate the effectiveness of the ANN based condition monitoring method it is applied to a case study from a real wind turbine. Furthermore, a mathematical model is presented together with a case study which presents its application within the maintenance management framework. The case study demonstrates the advantage of combining both the age based and condition based maintenance scheduling methods.