On robust optimization of wind farm maintenance and operation under uncertainty

Wind power capacity has been rapidly increasing in the past decade, but the market share of wind energy still remains a small portion of the entire energy market. The amount of electricity generated from wind power has tripled in the past five years [1]. Therefore, wind becomes the second largest source of new power generation in the U.S. after natural gas. However, wind capacity is still just over 1% of world-wide electricity use [1]. The main factor to increase marketability of wind power is the cost. To this end, researchers and practitioners put emphasis on reducing operation costs as well as making new turbines with larger capacity. In this project, I will focus on minimizing operation cost by building an efficient and effective maintenance policy. Every maintenance activity involves skilled laborers, time, and heavy-duty equipment. Thanks to the advent of sensor technology, many turbine manufactures have installed conditionbased monitoring equipment with many sensors within turbines. This monitoring equipment reduces the need to carry out scheduled, and/or corrective maintenance, both of which are very costly. These condition monitoring systems generate sensor signals to represent the turbine states. With the obtained sensor signals, we have to make decisions about which maintenance actions should be taken. The actions will include normal operation, dispatching crew with