Data science for wind energy / / Yu Ding.

• VerfasserIn: Ding, Yu (Electrical and Computer Engineer),
• Place / Publishing House: Boca Raton : : CRC Press,, [2020]
• Year of Publication: 2020
• Edition: 1st ed.
• Language: English
• Physical Description: 1 online resource (425 pages)

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 optim...

Table of 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.