Data science for wind energy / Yu Ding.

Format:

Book

Author/Creator:

Ding, Yu (Electrical and Computer Engineer), author.

Language:

English

Subjects (All):

Wind power--Data processing.

Wind power.

Physical Description:

1 online resource (425 pages)

Edition:

1st ed.

Place of Publication:

Boca Raton : CRC Press, [2020]

Summary:

Data Science for Wind Energy provides an in-depth discussion on how data science methods can improve decision making for wind energy applications, near-ground wind field analysis and forecast, turbine power curve fitting and performance analysis, turbine reliability assessment, and maintenance optimization for wind turbines and wind farms. A broad set of data science methods covered, including time series models, spatio-temporal analysis, kernel regression, decision trees, kNN, splines, Bayesian inference, and importance sampling. More importantly, the data science methods are described in the context of wind energy applications, with specific wind energy examples and case studies. Features Provides an integral treatment of data science methods and wind energy applications Includes specific demonstration of particular data science methods and their use in the context of addressing wind energy needs Presents real data, case studies and computer codes from wind energy research and industrial practice Covers material based on the author's ten plus years of academic research and insights

Contents:

Cover

Half Title

Title Page

Copyright Page

Dedication

Table of Contents

Foreword

Preface

Acknowledgments

Chapter 1: Introduction

1.1 WIND ENERGY BACKGROUND

1.2 ORGANIZATION OF THIS BOOK

1.2.1 Who Should Use This Book

1.2.2 Note for Instructors

1.2.3 Datasets Used in the Book

Part I: Wind Field Analysis

Chapter 2: A Single Time Series Model

2.1 TIME SCALE IN SHORT- TERM FORECASTING

2.2 SIMPLE FORECASTING MODELS

2.2.1 Forecasting Based on Persistence Model

2.2.2 Weibull Distribution

2.2.3 Estimation of Parameters in Weibull Distribution

2.2.4 Goodness of Fit

2.2.5 Forecasting Based on Weibull Distribution

2.3 DATA TRANSFORMATION AND STANDARDIZATION

2.4 AUTOREGRESSIVE MOVING AVERAGE MODELS

2.4.1 Parameter Estimation

2.4.2 Decide Model Order

2.4.3 Model Diagnostics

2.4.4 Forecasting Based on ARMA Model

2.5 OTHER METHODS

2.5.1 Kalman Filter

2.5.2 Support Vector Machine

2.5.3 Artificial Neural Network

2.6 PERFORMANCE METRICS

2.7 COMPARING WIND FORECASTING METHODS

Chapter 3: Spatio temporal Models

3.1 COVARIANCE FUNCTIONS AND KRIGING

3.1.1 Properties of Covariance Functions

3.1.2 Power Exponential Covariance Function

3.1.3 Kriging

3.2 SPATIO-TEMPORAL AUTOREGRESSIVE MODELS

3.2.1 Gaussian Spatio-temporal Autoregressive Model

3.2.2 Informative Neighborhood

3.2.3 Forecasting and Comparison

3.3 SPATIO-TEMPORAL ASYMMETRY AND SEPARABILITY

3.3.1 Definition and Quantification

3.3.2 Asymmetry of Local Wind Field

3.3.3 Asymmetry Quantification

3.3.4 Asymmetry and Wake Effect

3.4 ASYMMETRIC SPATIO-TEMPORAL MODELS

3.4.1 Asymmetric Non-separable Spatio-temporal Model

3.4.2 Separable Spatio-temporal Models

3.4.3 Forecasting Using Spatio-temporal Model

3.4.4 Hybrid of Asymmetric Model and SVM

3.5 CASE STUDY.

Chapter 4: Regime-switching Methods for Forecasting

4.1 REGIME-SWITCHING AUTOREGRESSIVE MODEL

4.1.1 Physically Motivated Regime Definition

4.1.2 Data-driven Regime Determination

4.1.3 Smooth Transition between Regimes

4.1.4 Markov Switching between Regimes

4.2 REGIME-SWITCHING SPACE-TIME MODEL

4.3 CALIBRATION IN REGIME SWITCHING METHOD

4.3.1 Observed Regime Changes

4.3.2 Unobserved Regime Changes

4.3.3 Framework of Calibrated Regime-switching

4.3.4 Implementation Procedure

4.4 CASE STUDY

4.4.1 Modeling Choices and Practical Considerations

4.4.2 Forecasting Results

Part II: Wind Turbine Performance Analysis

Chapter 5: Power Curve Modeling and Analysis

5.1 IEC BINNING: SINGLE-DIMENSIONAL POWER CURVE

5.2 KERNEL-BASED MULTI-DIMENSIONAL POWER CURVE

5.2.1 Need for Nonparametric Modeling Approach

5.2.2 Kernel Regression and Kernel Density Estimation

5.2.3 Additive Multiplicative Kernel Model

5.2.4 Bandwidth Selection

5.3 OTHER DATA SCIENCE METHODS

5.3.1 k-Nearest Neighborhood Regression

5.3.2 Tree-based Regression

5.3.3 Spline-based Regression

5.4 CASE STUDY

5.4.1 Model Parameter Estimation

5.4.2 Important Environmental Factors Affecting Power Output

5.4.3 Estimation Accuracy of Different Models

Chapter 6: Production Efficiency Analysis and Power Curve

6.1 THREE EFFICIENCY METRICS

6.1.1 Availability

6.1.2 Power Generation Ratio

6.1.3 Power Coefficient

6.2 COMPARISON OF EFFICIENCY METRICS

6.2.1 Distributions

6.2.2 Pairwise Differences

6.2.3 Correlations and Linear Relationships

6.2.4 Overall Insight

6.3 A SHAPE-CONSTRAINED POWER CURVE MODEL

6.3.1 Background of Production Economics

6.3.2 Average Performance Curve

6.3.3 Production Frontier Function and Effi ciency Metric

6.4 CASE STUDY.

Chapter 7: Quantification of Turbine Upgrade

7.1 PASSIVE DEVICE INSTALLATION UPGRADE

7.2 COVARIATE MATCHING BASED APPROACH

7.2.1 Hierarchical Subgrouping

7.2.2 One-to-One Matching

7.2.3 Diagnostics

7.2.4 Paired t-tests and Upgrade Quantification

7.2.5 Sensitivity Analysis

7.3 POWER CURVE-BASED APPROACH

7.3.1 The Kernel Plus Method

7.3.2 Kernel Plus Quantification Procedure

7.3.3 Upgrade Detection

7.3.4 Upgrade Quantification

7.4 AN ACADEMIA-INDUSTRY CASE STUDY

7.4.1 The Power-vs-Power Method

7.4.2 Joint Case Study

7.4.3 Discussion

7.5 COMPLEXITIES IN UPGRADE QUANTIFICATION

Chapter 8: Wake Effect Analysis

8.1 CHARACTERISTICS OF WAKE EFFECT

8.2 JENSEN'S MODEL

8.3 A DATA BINNING APPROACH

8.4 SPLINE-BASED SINGLE-WAKE MODEL

8.4.1 Baseline Power Production Model

8.4.2 Power Diff erence Model for Two Turbines

8.4.3 Spline Model with Non-negativity Constraint

8.5 GAUSSIAN MARKOV RANDOM FIELD MODEL

8.6 CASE STUDY

8.6.1 Performance Comparison of Wake Models

8.6.2 Analysis of Turbine Wake Effect

Part III: Wind Turbine Reliability Management

Chapter 9: Overview of Wind Turbine Maintenance Opti- mization

9.1 COST- EFFECTIVE MAINTENANCE

9.2 UNIQUE CHALLENGES IN TURBINE MAINTENANCE

9.3 COMMON PRACTICES

9.3.1 Failure Statistics-Based Approaches

9.3.2 Physical Load-Based Reliability Analysis

9.3.3 Condition-Based Monitoring or Maintenance

9.4 DYNAMIC TURBINE MAINTENANCE OPTIMIZATION

9.4.1 Partially Observable Markov Decision Process

9.4.2 Maintenance Optimization Solutions

9.4.3 Integration of Optimization and Simulation

9.5 DISCUSSION

Chapter 10: Extreme Load Analysis

10.1 FORMULATION FOR EXTREME LOAD ANALYSIS

10.2 GENERALIZED EXTREME VALUE DISTRIBUTIONS

10.3 BINNING METHOD FOR NONSTATIONARY GEV DISTRIBUTION.

10.4 BAYESIAN SPLINE-BASED GEV MODEL

10.4.1 Conditional Load Model

10.4.2 Posterior Distribution of Parameters

10.4.3 Wind Characteristics Model

10.4.4 Posterior Predictive Distribution

10.5 ALGORITHMS USED IN BAYESIAN INFERENCE

10.6 CASE STUDY

10.6.1 Selection of Wind Speed Model

10.6.2 Pointwise Credible Intervals

10.6.3 Binning versus Spline Methods

10.6.4 Estimation of Extreme Load

10.6.5 Simulation of Extreme Load

Chapter 11: Computer Simulator-Based Load Analysis

11.1 TURBINE LOAD COMPUTER SIMULATION

11.1.1 NREL Simulators

11.1.2 Deterministic and Stochastic Simulators

11.1.3 Simulator versus Emulator

11.2 IMPORTANCE SAMPLING

11.2.1 Random Sampling for Reliability Analysis

11.2.2 Importance Sampling Using Deterministic Simulator

11.3 IMPORTANCE SAMPLING USING STOCHASTIC SIMULATORS

11.3.1 Stochastic Importance Sampling Method 1

11.3.2 Stochastic Importance Sampling Method 2

11.3.3 Benchmark Importance Sampling Method

11.4 IMPLEMENTING STOCHASTIC IMPORTANCE SAMPLING

11.4.1 Modeling the Conditional POE

11.4.2 Sampling from Importance Sampling Densities

11.4.3 The Algorithm

11.5 CASE STUDY

11.5.1 Numerical Analysis

11.5.2 NREL Simulator Analysis

Chapter 12: Anomaly Detection and Fault Diagnosis

12.1 BASICS OF ANOMALY DETECTION

12.1.1 Types of Anomalies

12.1.2 Categories of Anomaly Detection Approaches

12.1.3 Performance Metrics and Decision Process

12.2 BASICS OF FAULT DIAGNOSIS

12.2.1 Tree-Based Diagnosis

12.2.2 Signature-Based Diagnosis

12.3 SIMILARITY METRICS

12.3.1 Norm and Distance Metrics

12.3.2 Inner Product and Angle-Based Metrics

12.3.3 Statistical Distance

12.3.4 Geodesic Distance

12.4 DISTANCE-BASED METHODS

12.4.1 Nearest Neighborhood-based Method

12.4.2 Local Outlier Factor.

12.4.3 Connectivity-based Outlier Factor

12.4.4 Subspace Outlying Degree

12.5 GEODESIC DISTANCE BASED METHOD

12.5.1 Graph Model of Data

12.5.2 MST Score

12.5.3 Determine Neighborhood Size

12.6 CASE STUDY

12.6.1 Benchmark Cases

12.6.2 Hydropower Plant Case

Bibliography

Index.

Notes:

Includes bibliographical references.

CC BY-NC-ND

Description based on print version record.

ISBN:

1-5231-3446-1

0-429-49097-6

0-429-95650-9

0-429-95651-7

9780429490972

OCLC:

1103917723