EAGER-DynamicData: Minimizing Wind Farm Operation and Maintenance Cost Using Data-Driven Models

Texas State University - San Marcos, San Marcos TX

Investigators

Abstract

The growth in wind farm installations in the past years has led to an increase in the number of wind turbines reaching the end of their manufacturing warranties. Therefore, the wind industry is now faced with a rising cost of unscheduled maintenance which is increasing operation and maintenance expenditures. This research project will develop new operation and maintenance strategies for improving the reliability of wind farm systems so that wind energy cost can be reduced. Wind turbines operate under harsh conditions that lead to wind turbine component failures, which are difficult to predict. Consequently, it is challenging to schedule maintenance actions so that such component failures can be avoided or minimized. Because of the continuously escalating cost of wind farm operation and management in the United States, devising methods for using available wind turbine sensors data is critical to decreasing wind farms operational costs. To accomplish this objective, this research considers new maintenance scheduling models and algorithms that take into account data uncertainties in turbines status, weather conditions, and the availability of the resources needed to perform maintenance. If successful, this exploratory research will enable faster initial maintenance response and better utilization of limited maintenance resources in wind energy systems. The efficient utilization of costly resources will foster competitiveness and will contribute towards reducing the cost of wind energy, achieve electricity price stability, and reduce dependency on global fuel markets. There is a need to establish guidelines to reduce operation and maintenance costs in wind energy systems. In pursuit of this goal, the objective of this research is to establish how stochastic online data-enabled models and algorithms can lead to wind turbine rapid damage detection and failure reduction. Stochastic online optimization is a suitable framework for this problem since it explicitly assimilates stochastic data that evolve over time into the optimization model, enabling robust decisions to be made sequentially prior to observing the entire stochastic data stream. The project motivation comes from the need for a data-driven methodology for maintenance planning in wind energy. The proposed work will address basic scientific and engineering challenges toward the successful derivation of a data-driven stochastic online optimization algorithm for the operation and maintenance of wind energy systems. This research will advance the state-of-the-art in wind farm operation and maintenance by contributing computational and data-enabled concepts, models and algorithms. The outcomes of this research will extend how maintenance is scheduled in wind energy systems to a new level beyond the current state of practice by introducing: 1) data-driven optimization models for maintenance and resource scheduling and 2) new algorithms for stochastic online optimization. In particular, the new data-driven methodology, integrating stochastic programing with stochastic online optimization, which are usually treated in isolation, will give rise to a new and viable approach for stochastic data assimilation into decision-making under uncertainty for complex multi-entity engineered systems such as wind farms.