Adaptive Extreme Load Estimation in Wind Turbines

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143051/1/6.2017-0679.pd

Bayesian spline method for assessing extreme loads on wind turbines

This study presents a Bayesian parametric model for the purpose of estimating the extreme load on a wind turbine. The extreme load is the highest stress level imposed on a turbine structure that the turbine would experience during its service lifetime. A wind turbine should be designed to resist such a high load to avoid catastrophic structural failures. To assess the extreme load, turbine structural responses are evaluated by conducting field measurement campaigns or performing aeroelastic simulation studies. In general, data obtained in either case are not sufficient to represent various loading responses under all possible weather conditions. An appropriate extrapolation is necessary to characterize the structural loads in a turbine's service life. This study devises a Bayesian spline method for this extrapolation purpose, using load data collected in a period much shorter than a turbine's service life. The spline method is applied to three sets of turbine's load response data to estimate the corresponding extreme loads at the roots of the turbine blades. Compared to the current industry practice, the spline method appears to provide better extreme load assessment.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS670 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Simulation and Optimization of Wind Farm Operations under Stochastic Conditions

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

Change‐Point Detection on Solar Panel Performance Using Thresholded LASSO

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135028/1/qre2077.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135028/2/qre2077_am.pd

Projecting the Most Likely Annual Urban Heat Extremes in the Central United States

Climate studies based on global climate models (GCMs) project a steady increase in annual average temperature and severe heat extremes in central North America during the mid-century and beyond. However, the agreement of observed trends with climate model trends varies substantially across the region. The present study focuses on two different locations: Des Moines, IA and Austin, TX. In Des Moines, annual extreme temperatures have not increased over the past three decades unlike the trend of regionally-downscaled GCM data for the Midwest, likely due to a “warming hole” over the area linked to agricultural factors. This warming hole effect is not evident for Austin over the same time period, where extreme temperatures have been higher than projected by regionally-downscaled climate (RDC) forecasts. In consideration of the deviation of such RDC extreme temperature forecasts from observations, this study statistically analyzes RDC data in conjunction with observational data to define for these two cities a 95% prediction interval of heat extreme values by 2040. The statistical model is constructed using a linear combination of RDC ensemble-member annual extreme temperature forecasts with regression coefficients for individual forecasts estimated by optimizing model results against observations over a 52-year training period

Condition-based joint maintenance optimization for a large-scale system with homogeneous units

A joint maintenance policy that simultaneously repairs multiple units is useful for large-scale systems where the setup cost to initiate the maintenance is generally higher than the repair costs. This study proposes a new method for scheduling maintenance activities in a large-scale system with homogeneous units that degrade over time. Specifically, we consider the maintenance type that renews all units at each maintenance activity, which is practically applicable for systems where the units need to be regularly maintained. To make the analysis computationally tractable, we discretize the health condition of each unit into a finite number of states. The proposed optimization formulation triggers the maintenance activity based on the fraction of units at each degradation state. Based on relevant asymptotic theories, we analytically obtain the optimal threshold in the fraction of units at each state that minimizes the long-run average maintenance cost. Our implementation results with a wide range of parameter settings show that the proposed maintenance strategy is more cost-effective than alternative strategies.114Nsciescopu

Reliability Evaluation of Large-Scale Systems With Identical Units

The reliability assessment of a large-scale system that considers its units' degradation is challenging due to the resulting dimensionality problem. We propose a new methodology that allows us to overcome difficulties in analyzing large-scale system dynamics, and devise analytical methods for finding the multivariate distribution of the dynamically changing system condition. When each unit's degradation condition can be classified into a finite number of states, and the transition distribution from one state to another is known, we obtain the asymptotic distribution of the number of units at each degradation state using fluid and diffusion limits. Specifically, we use a uniform acceleration technique, and obtain the time-varying mean vector and the covariance matrix of the number of units at multiple degradation states. When a state transition follows a non-Markovian deterioration process, we integrate phase-type distribution approximations with the fluid and diffusion limits. We show that, with any transition time distributions, the distribution of the number of units at multiple degradation conditions can be approximated by the multivariate Gaussian distribution as the total number of units gets large. The analytical results enable us to perform probabilistic assessment of the system condition during the system's service life. Our numerical studies suggest that the proposed methods can accurately characterize the stochastic evolution of the system condition over time.X1133Nsciescopu