Proceedings Article | 15 April 2010
KEYWORDS: Wind measurement, Wind energy, Data mining, 3D visualizations, Visualization, Diagnostics, Spindles, Renewable energy, Feature extraction, Wind turbine technology
Wind is an important renewable energy
source. The energy and economic return from building
wind farms justify the expensive investments in doing so.
However, without an effective monitoring system, underperforming
or faulty turbines will cause a huge loss in
revenue. Early detection of such failures help prevent
these undesired working conditions. We develop three
tests on power curve, rotor speed curve, pitch angle curve
of individual turbine. In each test, multiple states are
defined to distinguish different working conditions,
including complete shut-downs, under-performing states,
abnormally frequent default states, as well as normal
working states. These three tests are combined to reach a
final conclusion, which is more effective than any single
test.
Through extensive data mining of historical data and
verification from farm operators, some state combinations
are discovered to be strong indicators of spindle failures,
lightning strikes, anemometer faults, etc, for fault detection.
In each individual test, and in the score fusion of
these tests, we apply multidimensional scaling (MDS) to
reduce the high dimensional feature space into a 3-dimensional
visualization, from which it is easier to discover
turbine working information. This approach gains a qualitative
understanding of turbine performance status to
detect faults, and also provides explanations on what has
happened for detailed diagnostics.
The state-of-the-art SCADA (Supervisory Control And
Data Acquisition) system in industry can only answer the
question whether there are abnormal working states, and
our evaluation of multiple states in multiple tests is also
promising for diagnostics. In the future, these tests can be
readily incorporated in a Bayesian network for intelligent
analysis and decision support.
