Reliability Analysis for Asset Management of Electric Power Grids

 
 
Standards Information Network (Verlag)
  • 1. Auflage
  • |
  • erschienen am 28. Dezember 2018
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  • 520 Seiten
 
E-Book | PDF mit Adobe-DRM | Systemvoraussetzungen
978-1-119-12518-1 (ISBN)
 
A practical guide to facilitate statistically well-founded decisions in the management of assets of an electricity grid Effective and economic electric grid asset management and incident management involve many complex decisions on inspection, maintenance, repair and replacement. This timely reference provides statistically well-founded, tried and tested analysis methodologies for improved decision making and asset management strategy for optimum grid reliability and availability. The techniques described are also sufficiently robust to apply to small data sets enabling asset managers to deal with early failures or testing with limited sample sets. The book describes the background, concepts and statistical techniques to evaluate failure distributions, probabilities, remaining lifetime, similarity and compliancy of observed data with specifications, asymptotic behavior of parameter estimators, effectiveness of network configurations and stocks of spare parts. It also shows how the graphical representation and parameter estimation from analysis of data can be made consistent, as well as explaining modern upcoming methodologies such as the Health Index and Risk Index. Key features: * Offers hands-on tools and techniques for data analysis, similarity index, failure forecasting, health and risk indices and the resulting maintenance strategies. * End-of-chapter problems and solutions to facilitate self-study via a book companion website. The book is essential reading for advanced undergraduate and graduate students in electrical engineering, quality engineers, utilities and industry strategists, transmission and distribution system planners, asset managers and risk managers.
weitere Ausgaben werden ermittelt
ROBERT ROSS is a professor at TU Delft, director of IWO (Institute for Science & Development, Ede), a professor at HAN University of Applied Sciences and an Asset Management Research Strategist at TenneT (TSO in the Netherlands and part of Germany). At KEMA he worked on reliability and post-failure forensic investigations.
His interests concern reliability statistics, electro-technical materials, sustainable technology and superconductivity. For energy inventions he was granted the 2004 SenterNovem Annual award and nominated 2006 Best Energy Researcher by the World Technology Network.
This book is based on studies at KEMA and IWO, lectures at KIM (Royal Institute for the Navy), and experience with utilities.
  • Cover
  • Title Page
  • Copyright
  • Contents
  • Preface
  • Acknowledgements
  • List of Symbols and Abbreviations
  • About the Companion website
  • Chapter 1 Introduction
  • 1.1 Electric Power Grids
  • 1.2 Asset Management of Electric Power Grids
  • 1.3 Maintenance Styles
  • 1.3.1 Corrective Maintenance
  • 1.3.1.1 CM Inspections
  • 1.3.1.2 CM Servicing
  • 1.3.1.3 CM Replacement
  • 1.3.1.4 Evaluation of CM
  • 1.3.2 Period-Based Maintenance
  • 1.3.2.1 PBM Inspections
  • 1.3.2.2 PBM Servicing
  • 1.3.2.3 PBM Replacement
  • 1.3.2.4 Evaluation of PBM
  • 1.3.3 Condition-Based Maintenance
  • 1.3.3.1 CBM Inspections
  • 1.3.3.2 CBM Servicing
  • 1.3.3.3 CBM Replacement
  • 1.3.3.4 Introduction to the Health Index
  • 1.3.3.5 Evaluation of CBM
  • 1.3.4 Risk-Based Maintenance
  • 1.3.4.1 Corporate Business Values and the Risk Matrix
  • 1.3.4.2 RBM Inspections
  • 1.3.4.3 RBM Servicing
  • 1.3.4.4 RBM Replacement
  • 1.3.4.5 Evaluation of RBM
  • 1.3.5 Comparison of Maintenance Styles
  • 1.4 Incident Management
  • 1.5 Summary
  • Chapter 2 Basics of Statistics and Probability
  • 2.1 Outcomes, Sample Space and Events
  • 2.2 Probability of Events
  • 2.3 Probability versus Statistical Distributions
  • 2.4 Fundamental Statistical Functions
  • 2.4.1 Failure Distribution F
  • 2.4.2 Reliability R
  • 2.4.3 Probability or Distribution Density f
  • 2.4.4 Probability or Distribution Mass f
  • 2.4.5 Hazard Rate h and the Bath Tub Model
  • 2.4.6 Cumulative Hazard Function H
  • 2.5 Mixed Distributions
  • 2.5.1 Competing Processes
  • 2.5.2 Inhomogeneous Populations
  • 2.5.2.1 Bayes' Theorem
  • 2.5.2.2 Failure Distribution of an Inhomogeneous Population
  • 2.5.3 Early Failures Interpreted as Child Mortality
  • 2.6 Multivariate Distributions and Power Law
  • 2.6.1 Ageing Dose and Power Law
  • 2.6.2 Accelerated Ageing
  • 2.6.3 Multi-Stress Ageing
  • 2.6.4 Cumulative Distribution, Ageing Dose and CBM
  • 2.7 Summary
  • Chapter 3 Measures in Statistics
  • 3.1 Expected Values and Moments
  • 3.1.1 Operations and Means
  • 3.1.2 Bayesian Mean
  • 3.1.3 The Moments of a Distribution
  • 3.1.4 EXTRA: Moment Generating Function
  • 3.1.5 EXTRA: Characteristic Function
  • 3.1.6 Central Moments of a Distribution
  • 3.1.7 The First Four Central and Normalized Moments
  • 3.1.8 Mean, Standard Deviation, and Variance of a Sample
  • 3.2 Median and Other Quantiles
  • 3.3 Mode
  • 3.4 Merits of Mean, Median and Modal Value
  • 3.5 Measures for Comparing Distributions
  • 3.5.1 Covariance
  • 3.5.2 Correlation
  • 3.5.3 Cross-Correlation and Autocorrelation
  • 3.6 Similarity of Distributions
  • 3.6.1 Similarity of Counting in Discrete Sets
  • 3.6.2 Similarity of Two Discrete Distributions
  • 3.6.3 Similarity of Two Continuous Distributions
  • 3.6.4 Significance of Similarity
  • 3.6.5 Singularity Issues and Alternative Similarity Indices
  • 3.7 Compliance
  • 3.8 Summary
  • Chapter 4 Specific Distributions
  • 4.1 Fractions and Ranking
  • 4.1.1 Uniform Distribution
  • 4.1.1.1 Continuous Uniform Distribution Characteristics
  • 4.1.1.2 Discrete Uniform Distribution Characteristics
  • 4.1.1.3 EXTRA: Moment Generating Function and Characteristic Function
  • 4.1.2 Beta Distribution or Rank Distribution
  • 4.1.2.1 Beta Distribution Characteristics
  • 4.1.2.2 EXTRA: Moment Generating Function and Characteristic Function
  • 4.2 Extreme Value Statistics
  • 4.2.1 Weibull Distribution
  • 4.2.1.1 Weibull-2 Distribution
  • 4.2.1.2 Weibull-2 Distribution Moments and Mean
  • 4.2.1.3 Weibull-2 Distribution Characteristics
  • 4.2.1.4 EXTRA: Moment Generating Function
  • 4.2.2 Weibull-3 Distribution
  • 4.2.3 Weibull-1 Distribution
  • 4.2.4 Exponential Distribution
  • 4.2.4.1 Exponential Distribution and Average Hazard Rate
  • 4.2.4.2 Exponential Distribution Characteristics
  • 4.3 Mean and Variance Statistics
  • 4.3.1 Normal Distribution
  • 4.3.1.1 Characteristics of the Normal Distribution
  • 4.3.1.2 EXTRA: Moments, Moment Generating Function and Characteristic Function
  • 4.3.1.3 EXTRA: Central Limit Theorem
  • 4.3.2 Lognormal Distribution
  • 4.3.2.1 Characteristics of the Lognormal Distribution
  • 4.3.2.2 EXTRA: Moment Generating Function and Characteristic Function
  • 4.3.2.3 Lognormal versus Weibull
  • 4.4 Frequency and Hit Statistics
  • 4.4.1 Binomial Distribution
  • 4.4.1.1 Mean and Variance
  • 4.4.1.2 Characteristics of the Binomial Distribution
  • 4.4.1.3 EXTRA: Moment Generating Function
  • 4.4.2 Poisson Distribution
  • 4.4.2.1 Characteristics of the Poisson Distribution
  • 4.4.2.2 Derivation of the Poisson Distribution
  • 4.4.2.3 Homogeneous Poisson Process
  • 4.4.2.4 Non-Homogeneous Poisson Process
  • 4.4.2.5 Poisson versus Binomial Distribution
  • 4.4.3 Hypergeometric Distribution
  • 4.4.3.1 Mean and Variance of the Hypergeometric Distribution
  • 4.4.3.2 Characteristics of the Hypergeometric Distribution
  • 4.4.4 Normal Distribution Approximation of the Binomial Distribution
  • 4.4.5 Multinomial Distribution
  • 4.4.5.1 Mean, Variances and Moment Generating Function
  • 4.4.6 Multivariate Hypergeometric Distribution
  • 4.5 Summary
  • Chapter 5 Graphical Data Analysis
  • 5.1 Data Quality
  • 5.2 Parameter-Free Graphical Analysis
  • 5.2.1 Basic Graph of a Population Sample
  • 5.2.2 Censored Data
  • 5.2.3 Kaplan-Meier Plot
  • 5.2.4 Confidence Intervals Around a Known Distribution
  • 5.2.5 Confidence Intervals with Data
  • 5.2.6 Alternative Confidence Intervals
  • 5.3 Model-Based or Parametric Graphs
  • 5.4 Weibull Plot
  • 5.4.1 Weibull Plot with Expected Plotting Position
  • 5.4.2 Weibull Plot with Median Plotting Position
  • 5.4.3 Weibull Plot with Expected Probability Plotting Position
  • 5.4.4 Weibull Plot with Censored Data
  • 5.4.5 Confidence Intervals in Weibull Plots
  • 5.5 Exponential Plot
  • 5.5.1 Exponential Plot with Expected Plotting Position
  • 5.5.2 Exponential Plot with Median Plotting Position
  • 5.5.3 Exponential Plot with Censored Data
  • 5.5.4 Exponential Plot with Confidence Intervals
  • 5.6 Normal Distribution
  • 5.6.1 Normal Plot with Expected Plotting Position
  • 5.6.2 Normal Probability Plot with Confidence Intervals
  • 5.6.3 Normal Plot and Lognormal Data
  • 5.7 Power Law Reliability Growth
  • 5.7.1 Duane and Crow AMSAA Plots and Models
  • 5.7.2 NHPP Model in Duane and Crow AMSAA Plots
  • 5.8 Summary
  • Chapter 6 Parameter Estimation
  • 6.1 General Aspects with Parameter Estimation
  • 6.1.1 Fundamental Properties of Estimators
  • 6.1.1.1 Bias
  • 6.1.1.2 Efficiency
  • 6.1.1.3 Consistency
  • 6.1.2 Why Work with Small Data Sets?
  • 6.1.3 Asymptotic Behaviour of Estimators
  • 6.2 Maximum Likelihood Estimators
  • 6.2.1 ML with Uncensored Data
  • 6.2.2 ML for Sets Including Censored Data
  • 6.2.3 ML for the Weibull Distribution
  • 6.2.3.1 ML Estimators for Weibull-2 Uncensored Data
  • 6.2.3.2 ML Estimators for Weibull-2 Censored Data
  • 6.2.3.3 Expected ML Estimators for the Weibull-2 Distribution
  • 6.2.3.4 Formulas for Bias and Scatter
  • 6.2.3.5 Effect of the ML Estimation Bias in Case of Weibull-2
  • 6.2.4 ML for the Exponential Distribution
  • 6.2.5 ML for the Normal Distribution
  • 6.3 Linear Regression
  • 6.3.1 The LR Method
  • 6.3.1.1 LR by Unweighted Least Squares
  • 6.3.1.2 LR by Weighted Least Squares
  • 6.3.1.3 LR with Censored Data
  • 6.3.1.4 LR with Fixed Origin
  • 6.3.1.5 Which is the (Co)variable?
  • 6.3.2 LR for the Weibull Distribution
  • 6.3.2.1 LR by Unweighted LS for the Weibull Distribution
  • 6.3.2.2 LR by Weighted LS for the Weibull Distribution
  • 6.3.2.3 Processing Censored Data with the Adjusted Rank Method
  • 6.3.2.4 EXTRA: Processing Censored Data with the Adjusted Plotting Position Method
  • 6.3.2.5 Expected LS and WLS Estimators for the Weibull-2 Distribution
  • 6.3.2.6 Formulas for Bias and Scatter for LS and WLS
  • 6.3.2.7 Comparison of Bias and Scatter in LS, WLS and ML
  • 6.3.3 LR for the Exponential Distribution
  • 6.3.3.1 LR by Unweighted LS for the Exponential Distribution
  • 6.3.3.2 LR by Weighted LS for the Exponential Distribution
  • 6.3.3.3 Processing Censored Data with the Adjusted Rank Method
  • 6.3.3.4 EXTRA: Processing Censored Data with the Adjusted Plotting Position Method
  • 6.3.3.5 Expected LS and WLS Estimator for the Exponential Distribution
  • 6.3.4 LR for the Normal Distribution
  • 6.3.4.1 LR by Unweighted LS for the Normal Distribution
  • 6.3.4.2 Processing Censored Data with the Adjusted Rank Method
  • 6.3.4.3 EXTRA: Processing Censored Data with the Adjusted Plotting Position Method
  • 6.3.4.4 Expected LS Estimators for the Normal Distribution
  • 6.3.5 LR Applied to Power Law Reliability Growth
  • 6.4 Summary
  • Chapter 7 System and Component Reliability
  • 7.1 The Basics of System Reliability
  • 7.2 Block Diagrams
  • 7.3 Series Systems
  • 7.4 Parallel Systems and Redundancy
  • 7.5 Combined Series and Parallel Systems, Common Cause
  • 7.6 EXTRA: Reliability and Expected Life of k-out-of-n Systems{\textit {k}-out-of-\textit {n} system}{System!\textit {k}-out-of-\textit {n}}
  • 7.7 Analysis of Complex Systems
  • 7.7.1 Conditional Method
  • 7.7.2 Up-table Method
  • 7.7.3 EXTRA: Minimal Paths and Minimum Blockades
  • 7.8 Summary
  • Chapter 8 System States, Reliability and Availability
  • 8.1 States of Components and Systems
  • 8.2 States and Transition Rates of One-Component Systems
  • 8.2.1 One-Component System with Mere Failure Behaviour
  • 8.2.2 One-Component System with Failure and Repair Behaviour
  • 8.3 System State Probabilities via Markov Chains
  • 8.3.1 Component and System States
  • 8.3.2 System States and Transition Rates for Failure and Repair
  • 8.3.3 Differential Equations Based on the State Diagram
  • 8.3.4 Differential Equations Based on the Transition Matrix
  • 8.4 Markov-Laplace Method for Reliability and Availability
  • 8.5 Lifetime with Absorbing States and Spare Parts
  • 8.6 Mean Lifetimes MTTFF and MTBF
  • 8.7 Availability and Steady-State Situations
  • 8.8 Summary
  • Chapter 9 Application to Asset and Incident Management
  • 9.1 Maintenance Styles
  • 9.1.1 Period-Based Maintenance Optimization for Lowest Costs
  • 9.1.1.1 Case Description
  • 9.1.1.2 References to Introductory Material
  • 9.1.1.3 PBM Cost Optimization Analysis
  • 9.1.1.4 Remarks
  • 9.1.2 Corrective versus Period-Based Replacement and Redundancy
  • 9.1.2.1 Case Description
  • 9.1.2.2 References to Introductory Material
  • 9.1.2.3 Analysis of Corrective versus Period-Based Replacement and Redundancy
  • 9.1.2.4 Remarks
  • 9.1.3 Condition-Based Maintenance
  • 9.1.3.1 References to Introductory Material
  • 9.1.3.2 Analysis of Condition versus Period-Based Replacement
  • 9.1.3.3 Remarks
  • 9.1.4 Risk-Based Maintenance
  • 9.1.4.1 References to Introductory Material
  • 9.1.4.2 Analysis of Risk versus Condition-Based Maintenance
  • 9.1.4.3 Remarks
  • 9.2 Health Index
  • 9.2.1 General Considerations of Health Index
  • 9.2.1.1 References to Introductory Material on Health Index Concept Considerations
  • 9.2.1.2 Analysis of the Health Index Concept
  • 9.2.1.3 Remarks
  • 9.2.2 Combined Health Index
  • 9.2.2.1 References to Introductory Material
  • 9.2.2.2 Analysis of the Combined Health Index Concept
  • 9.3 Testing and Quality Assurance
  • 9.3.1 Accelerated Ageing to Reduce Child Mortality
  • 9.3.2 Tests with Limited Test Object Numbers and Sizes
  • 9.4 Incident Management (Determining End of Trouble)
  • 9.4.1 Component Failure Data and Confidence Intervals
  • 9.4.1.1 References to Introductory Material
  • 9.4.1.2 Analysis of the Case
  • 9.4.1.3 Remarks
  • 9.4.2 Failures in a Cable with Multiple Problems and Stress Levels
  • 9.4.2.1 References to Introductory Material
  • 9.4.2.2 Analysis of the Case
  • 9.4.2.3 Remarks
  • 9.4.3 Case of Cable Joints with Five Early Failures
  • 9.4.3.1 References to Introductory Material
  • 9.4.3.2 Analysis of the Case
  • 9.4.3.3 Prognosis Using a Weibull Plot and Confidence Intervals
  • 9.4.3.4 Estimation of Sample Size Using the Similarity Index
  • 9.4.3.5 Redundancy and Urgency
  • 9.4.3.6 Remarks
  • 9.4.4 Joint Failure Data with Five Early Failures and Large Scatter
  • 9.4.4.1 References to Introductory Material
  • 9.4.4.2 Analysis of the Case
  • 9.4.4.3 Prognosis Using a Weibull Plot and Confidence Intervals
  • 9.4.4.4 Estimation of Sample Size Using the Similarity Index
  • 9.4.4.5 Remarks
  • Chapter 10 Miscellaneous Subjects
  • 10.1 Basics of Combinatorics
  • 10.1.1 Permutations and Combinations
  • 10.1.2 The Gamma Function
  • 10.2 Power Functions and Asymptotic Behaviour
  • 10.2.1 Taylor and Maclaurin Series
  • 10.2.2 Polynomial Fitting
  • 10.2.2.1 Polynomial Interpolation
  • 10.2.2.2 Polynomial and Linear Regression
  • 10.2.3 Power Function Fitting
  • 10.3 Regression Analysis
  • 10.4 Sampling from a Population and Simulations
  • 10.4.1 Systematic Sampling
  • 10.4.2 Numerical Integration and Expected Values
  • 10.4.3 Ranked Samples with Size n and Confidence Limits
  • 10.4.3.1 Behaviour of Population Fractions
  • 10.4.3.2 Confidence Limits for Population Fractions
  • 10.4.4 Monte Carlo Experiments and Random Number Generators
  • 10.4.5 Alternative Sampling and Fractals
  • 10.5 Hypothesis Testing
  • 10.6 Approximations for the Normal Distribution
  • 10.6.1 Power Series
  • 10.6.2 Power Series Times Density f(y)
  • 10.6.3 Inequalities for Boxing R(y) and h(y) for Large y
  • 10.6.4 Polynomial Expression for F(y)
  • 10.6.5 Power Function for the Reliability Function R(y)
  • 10.6.6 Wrap-up of Approximations
  • Appendix A Weibull Plot
  • Appendix B Laplace Transforms
  • Appendix C Taylor Series
  • Appendix D SI Prefixes
  • Appendix E Greek Characters
  • Appendix F Standard Weibull and Exponential Distribution
  • Appendix G Standardized Normal Distribution
  • Appendix H Standardized Lognormal Distribution
  • Appendix I Gamma Function
  • Appendix J Plotting Positions
  • J.1 Expected Ranked Probability for n = 1,?.?, 30
  • J.2 Expected Ranked Probability for n = 31,?.?, 45
  • J.3 Expected Ranked Probability for n = 45,?.?, 60
  • J.4 Median Ranked Probability FM,i,n for n =?1,?.?, 30
  • J.5 Median Ranked Probability FM,i,n for n = 31,?.?, 45
  • J.6 Median Ranked Probability FM,i,n for n =?46,?.?, 60
  • J.7 Probability of Expected Ranked Weibull Plotting Position F() for n = 1,?.?, 30
  • J.8 Probability of Expected Ranked Weibull Plotting Position F() for n = 31,?.?, 45
  • J.9 Probability of Expected Ranked Weibull Plotting Position F() for n = 46,?.?, 60
  • J.10 Expected Ranked Weibull Plotting Position for n = 1,?.?, 30
  • J.11 Expected Ranked Weibull Plotting Position for n = 31,?.?, 45
  • J.12 Expected Ranked Weibull Plotting Position for n = 46,?.?, 60
  • J.13 Weights for Linear Regression of Weibull-2 Data for n = 1,?.?, 30
  • J.14 Weights for Linear Regression of Weibull-2 Data for n = 31,?.?, 45
  • J.15 Weights for Linear Regression of Weibull-2 Data for n = 46,?.?, 60
  • References
  • Index
  • EULA
Robert Ross?s book gives a deep insight in useful statistical analysis methods for asset management practice. It covers all the basics and specific distributions in a structured and understandable way, before it sets out to give its insight into system and component reliability. I particularly liked the way the subject matter is structured in small and understandable topics. This way it?s easy to ?pick and mix? throughout the different subject matters of the book to acquire the relevant knowledge. One of the other strong suits of the book is the application to real life asset and incident management. Robert links theory and practice together in a way which really shows the value of a statistical and reliability driven approach to asset management. - Marcel Hooijmans, Sr. Specialist Asset Management, Stedin DSO, The Netherlands This is a well-grounded book that is really good to read! It is informative and accessible and something that would be suitable as a source book for a more general course on engineering statistics and not one specifically directed towards electric power grid assets. Overall the book develops a very solid statistical basis of use in engineering and, particularly reliability analysis. I believe the book is of such general value that it could be used as part of a manufacturing engineering course with relative ease. I think this should probably be on the bookcase of anyone working in asset management of utilities. The material is presented with a logical and paced approach, taking the reader through basic statistics to some quite advanced concepts. - Professor Alistair Duffy, Professor of Electromagnetics and Director of the Institute of Engineering Sciences at De Montfort University, Leicester, UK

Robert Ross creates a comprehensive interface between the statistical analysis and the Asset Management tasks and problems of the electric grid. Many practical examples give a clear and easy understanding of the different subjects?the book is suitable for a direct entry into the topic. Later it can also be used as a reference work, particularly regarding the synoptic tables. This makes the book ideal for students as well as for practical use by asset managers. - Dr. Horst Gunter Bender, TenneT TSO GmbH, Germany

The book features ten chapters, with the main focus on the fundamentals of statistics. The way the book is written makes it possible to be used as supplement to lectures at universities about asset management because of the following points. Each chapter features an introductory paragraph and a section at the end with a summary of the topics covered by that chapter. Furthermore, each chapter provides exemplary questions and exercises which facilitate understanding of the chapter. Additionally, the author supports his argumentation with easily understandable practical examples from the field of electrical engineering. - Nicholas Hill, TU Braunschweig, Germany

Anybody working in asset and incident management of electric power grids will greatly welcome this book if they want to understand and apply the mathematics used for assessing the reliability and availability of components and systems. The author makes a decisive step forward in presenting the knowledge and skills needed for analyzing failure data and constructing reliable systems. Throughout the book, readers can taste the thorough experience of Ross both as a practitioner and as a researcher in the field of reliability analysis. This makes the book a must-have for engineers, asset managers and risk managers who are interested in decision analysis for managing assets of electric power grids. - Rene Janssen, Associate Professor of Mathematics and Operations Research at the Netherlands Defence Academy

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