This dissertation develops a new methodology and associated solution tools to
achieve optimal operations and maintenance strategies for wind turbines, helping
reduce operational costs and enhance the marketability of wind generation. The
integrated framework proposed includes two optimization models for enabling decision
support capability, and one discrete event-based simulation model that characterizes
the dynamic operations of wind power systems. The problems in the optimization
models are formulated as a partially observed Markov decision process to determine
an optimal action based on a wind turbine's health status and the stochastic weather
conditions.
The rst optimization model uses homogeneous parameters with an assumption
of stationary weather characteristics over the decision horizon. We derive a set of
closed-form expressions for the optimal policy and explore the policy's monotonicity.
The second model allows time-varying weather conditions and other practical aspects.
Consequently, the resulting strategy are season-dependent. The model is solved using
a backward dynamic programming method. The bene ts of the optimal policy are
highlighted via a case study that is based upon eld data from the literature and
industry. We nd that the optimal policy provides options for cost-e ective actions,
because it can be adapted to a variety of operating conditions.
Our discrete event-based simulation model incorporates critical components, such
as a wind turbine degradation model, power generation model, wind speed model,
and maintenance model. We provide practical insights gained by examining di erent
maintenance strategies. To the best of our knowledge, our simulation model is the
rst discrete-event simulation model for wind farm operations.
Last, we present the integration framework, which incorporates the optimization
results in the simulation model. Preliminary results reveal that the integrated model
has the potential to provide practical guidelines that can reduce the operation costs
as well as enhance the marketability of wind energy.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2010-05-7791 |
| Date | 2010 May 1900 |
| Creators | Byon, Eunshin |
| Contributors | Ding, Yu |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | thesis, text |
| Format | application/pdf |
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