PhD defence by Meik Schlechtingen
The cost of energy generated from wind power plants (particular if located offshore) is challenging societies in terms of desiring cheaper and more environmentally friendly generated electrical energy. The high cost reduction targets can be aided by broad application of condition monitoring systems, which bear the potential to support plant owners reducing turbine downtime and lowering costs.
In this research a global condition monitoring system is proposed, which provides a platform to take advantage of the different information sources available to operators.
One of the most common sources for information about the component condition is Supervisory Control And Data Acquisition (SCADA) data, e.g. temperature, current or voltage measurements from different components.
Using newly developed Adaptive Neuro-Fuzzy Interference System (ANFIS) models, a normal behavior model based approach is taken to extract information from these data, which otherwise are covered by the high signal variance. A novelty here is the application of ANFIS models in this context, which is traditionally covered by neural networks or regression based approaches. Advantages in training speed and back traceability of results are the motivators to apply these promising model types.
Methods proposed in literature usually take an autoregressive approach, i.e. a delayed version of the output signal is used as input alongside other highly correlated signals. In this research, a full signal reconstruction method is proposed in which the output signal is entirely reconstructed by using other correlated signals. Benefits in fault visibility and lead-time to failure estimates are observed.
The Ph.d. defense takes place in Building 306, Auditorium 38, DTU Lyngby, the 20th June from 13.00 to 17.00.