Adaptive Extreme Load Estimation in Wind Turbines

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

Structure-Regularized Partition-Regression Models for Nonlinear System-Environment Interactions

abstract: Under different environmental conditions, the relationship between the design and operational variables of a system and the system’s performance is likely to vary and is difficult to be described by a single model. The environmental variables (e.g., temperature, humidity) are not controllable while the variables of the system (e.g. heating, cooling) are mostly controllable. This phenomenon has been widely seen in the areas of building energy management, mobile communication networks, and wind energy. To account for the complicated interaction between a system and the multivariate environment under which it operates, a Sparse Partitioned-Regression (SPR) model is proposed, which automatically searches for a partition of the environmental variables and fits a sparse regression within each subdivision of the partition. SPR is an innovative approach that integrates recursive partitioning and high-dimensional regression model fitting within a single framework. Moreover, theoretical studies of SPR are explicitly conducted to derive the oracle inequalities for the SPR estimators which could provide a bound for the difference between the risk of SPR estimators and Bayes’ risk. These theoretical studies show that the performance of SPR estimator is almost (up to numerical constants) as good as of an ideal estimator that can be theoretically achieved but is not available in practice. Finally, a Tree-Based Structure-Regularized Regression (TBSR) approach is proposed by considering the fact that the model performance can be improved by a joint estimation on different subdivisions in certain scenarios. It leverages the idea that models for different subdivisions may share some similarities and can borrow strength from each other. The proposed approaches are applied to two real datasets in the domain of building energy. (1) SPR is used in an application of adopting building design and operational variables, outdoor environmental variables, and their interactions to predict energy consumption based on the Department of Energy’s EnergyPlus data sets. SPR produces a high level of prediction accuracy and provides insights into the design, operation, and management of energy-efficient buildings. (2) TBSR is used in an application of predicting future temperature condition which could help to decide whether to activate or not the Heating, Ventilation, and Air Conditioning (HVAC) systems in an energy-efficient manner.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201

Computationally Efficient Reliability Evaluation With Stochastic Simulation Models.

Thanks to advanced computing and modeling technologies, computer simulations are becoming more widely used for the reliability evaluation of complex systems. Yet, as simulation models represent physical systems more accurately and utilize a large number of random variables to reflect various uncertainties, high computational costs remain a major challenge in analyzing the system reliability. The objective of this dissertation research is to provide new solutions for saving computational time of simulation-based reliability evaluation that considers large uncertainties within the simulation. This dissertation develops (a) a variance reduction technique for stochastic simulation models, (b) an uncertainty quantification method for the variance reduction technique, and (c) an adaptive approach of the variance reduction technique. First, among several variance reduction techniques, importance sampling has been widely used to improve the efficiency of simulation-based reliability evaluation using deterministic simulation models. In contrast to deterministic simulation models whose simulation output is uniquely determined given a fixed input, stochastic simulation models produce random outputs. We extend the theory of importance sampling to efficiently estimate a system's reliability with stochastic simulation models. Second, to quantify the uncertainty of the reliability estimation with stochastic simulation models, we can repeat the simulation experiment multiple times. It, however, multiplies computational burden. To overcome this, we establish the central limit theorem for the reliability estimator with stochastic simulation models, and construct an asymptotically valid confidence interval using data from a single simulation experiment. Lastly, theoretically optimal importance sampling densities require approximations in practice. As a candidate density to approximate the optimal density, a mixture of parametric densities can be used in the cross-entropy method that aims to minimize the cross-entropy between the optimal density and the candidate density. We propose an information criterion to identify an appropriate number of mixture densities. This criterion enables us to adaptively find the importance sampling density as we gather data through an iterative procedure. Case studies, using computationally intensive aeroelastic wind turbine simulators developed by the U.S. Department of Energy (DOE)'s National Renewable Energy Laboratory (NREL), demonstrate the superiority of the proposed approaches over alternative methods in estimating the system reliability using stochastic simulation models.PhDIndustrial and Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/120894/1/yjchoe_1.pd

Modelling and control of a novel single phase generator based on a three phase cage rotor induction machine

This thesis develops the mathematical modelling and the closed loop control of a single-phase induction generator based on a three-phase cage rotor machine suitable for renewable energy conversion. Comprehensive dynamic and steady state models are developed in stationary ‘αβ’ reference frame and the accuracy is verified by comparing the theoretical results with the laboratory experimental results. Closed loop feedback regulator is designed to regulate the output voltage and frequency at the rated conditions