Data science for wind energy

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...

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Detalles Bibliográficos
Otros Autores:	Ding, Yu (Electrical and Computer Engineer), author (author)
Formato:	Libro electrónico
Idioma:	Inglés
Publicado:	Boca Raton : CRC Press [2020]
Edición:	1st ed
Materias:	Wind power > Data processing.
Ver en Biblioteca Universitat Ramon Llull:	https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009798468706719

Tabla de Contenidos:

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.

Data science for wind energy

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