Data Science for Wind Energy

ISBN-13: 9780367729097 / Angielski / Miękka / 2020 / 400 str.

Yu Ding

Opis

This book shows how data science methods can improve decision making for wind energy applications. A broad set of data science methods will be covered, and the data science methods will be described in the context of wind energy applications, with specific wind energy examples and case studies.

Spis treści

 

Chapter 1 Introduction

Part I Wind Field Analysis

Chapter 2 A Single Time Series Model

Chapter 3 Spatiotemporal

Chapter 4 Regimeswitching

Part II Wind Turbine Performance Analysis

Chapter 5 Power Curve Modeling and Analysis

Chapter 6 Production Efficiency Analysis

Chapter 7 Quantification of Turbine Upgrade

Chapter 8 Wake Effect Analysis

Chapter 9 Overview of Turbine Maintenance Optimization

Chapter 10 Extreme Load Analysis

Chapter 11 Computer Simulator Based Load Analysis

Chapter 12 Anomaly Detection and Fault Diagnosis

O autorze

Dr. Yu Ding is the Anderson-Interface Chair and Professor in the H. Milton School of Industrial and Systems Engineering at Georgia Tech. Prior to joining Georgia Tech in 2023, he was the Mike and Sugar Barnes Professor of Industrial and Systems Engineering at Texas A&M University and served as Associate Director for Research Engagement of Texas A&M Institute of Data Science. Dr. Ding's research is in the area of data and quality science.  He received the 2019 IISE Technical Innovation Award and 2022 INFORMS Impact Prize for his data science innovations impacting wind energy applications. Dr. Ding is a Fellow of IISE and ASME.  He has served as editor or associate editor for several major engineering data science journals, including as the 14th Editor in Chief of IISE Transactions, for the term of 2021-2024.