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1. |
Record Nr. |
UNINA990008286320403321 |
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Autore |
Goggiamani, Francesca |
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Titolo |
La doverosità della pubblica amministrazione / Francesca Goggiamani |
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Pubbl/distr/stampa |
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Torino : Giappichelli, c2005 |
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ISBN |
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Descrizione fisica |
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Disciplina |
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Locazione |
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Collocazione |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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2. |
Record Nr. |
UNINA9910544855903321 |
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Autore |
Sun Yao |
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Titolo |
Series-parallel converter-based microgrids : system-level control and stability / / Yao Sun [and five others] |
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Pubbl/distr/stampa |
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Cham, Switzerland : , : Springer, , [2022] |
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©2022 |
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ISBN |
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Descrizione fisica |
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1 online resource (384 pages) |
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Collana |
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Disciplina |
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Soggetti |
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Microgrids (Smart power grids) |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Intro -- Preface -- Acknowledgments -- Contents -- About the Authors -- List of Symbols -- 1 Overview of Microgrid -- 1.1 Microgrid Concept and Challenges -- 1.1.1 Microgrid Concept -- 1.1.2 |
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Challenges for Microgrid -- 1.2 Converters Classification in Microgrid -- 1.2.1 Grid-Following Converter -- 1.2.2 Grid-Forming Converter -- 1.3 Architecture of Microgrid -- 1.3.1 Parallel-Type Microgrid -- 1.3.2 Series-Type Microgrid -- 1.3.3 Hybrid Series-Parallel Microgrid -- 1.4 Hierarchical Control Theory-General Introduction and Motivation -- 1.4.1 Primary Control -- 1.4.1.1 Conventional Droop Control -- 1.4.1.2 Virtual Impedance Control -- 1.4.2 Secondary Control -- 1.4.2.1 Centralized Control -- 1.4.2.2 Distributed Control and the Consensus Algorithm -- 1.4.3 Tertiary Control -- 1.5 Microgrid System Stability -- 1.5.1 Classification of Microgrid System Stability -- 1.5.1.1 Power Supply and Balance Stability -- 1.5.1.2 Control System Stability -- 1.5.2 Stability Analysis and Performance Assessment -- 1.5.2.1 Time-Scale Separation and Model Reduction -- 1.5.2.2 Stability of a Single Converter Connected to an Infinite Bus -- 1.5.2.3 Stability of Multi-Converter Systems -- 1.5.2.4 Stability of Multi-Converter Multi-Machine Systems -- 1.6 Organization of the Book -- References -- Part I Parallel-Type Microgrid System -- 2 Unified Droop Control Under Different Impedance Types -- 2.1 Different Droop Control Under Different Impedance Types -- 2.2 Basic Droop Control -- 2.2.1 Fundamental Concept of Frequency Droop -- 2.2.2 Equivalence of Virtual Impedance and Angle Droop -- 2.2.3 Analogy Between Angle Droop and Frequency Droop -- 2.3 Unified Droop Control Under Different Impedance Types -- 2.3.1 Unified Droop Control -- 2.3.2 Small-Signal Analysis -- 2.4 Simulation Results -- 2.5 Experimental Results -- 2.6 Conclusion -- References. |
3 Dynamic Frequency Regulation Via Adaptive Virtual Inertia -- 3.1 Analogy Between Droop Control and Virtual Synchronous Generator -- 3.2 Algorithm of Adaptive Virtual Inertia -- 3.2.1 Comparison Between SG and Droop-Based DG -- 3.2.2 Adaptive Virtual Inertia -- 3.2.3 Practical Control Scheme Without Derivative Action -- 3.3 Stability Proof -- 3.3.1 Single Inverter-Based DG in Grid-Connected Mode -- 3.3.2 Synchronization of Multiple DGs in Islanded Mode -- 3.4 Design Guidelines for Key Control Parameters -- 3.4.1 Design Guideline for Droop Damping Coefficient Dm -- 3.4.2 Design Guideline for Inertia Coefficient J0 -- 3.4.3 Design Guideline for Inertia Compensation Coefficient k -- 3.4.4 Parameter Design to Limit Excessive RoCoF -- 3.4.5 Adaptive Inertia Bound [Jmin, Jmax] to Avoid Long-Term Overcapacity of Converters -- 3.5 Hardware-In-Loop (HIL) Results -- 3.5.1 Case 1: Under Resistive Time-Varying Load -- 3.5.2 Case 2: Under Frequent-Variation Load -- 3.5.3 Case 3: Under Induction Motor (IM) -- 3.5.4 Case 4: Comparisons with Alternating Inertia Method -- 3.5.5 Case 5: Adaptive Inertia Control with Three DGs -- 3.5.6 Case 6: Adaptive Inertia Control with RoCoFLimitation -- 3.6 Conclusion -- References -- 4 Accurate Reactive Power Sharing -- 4.1 Analysis of Conventional Droop Control Method -- 4.1.1 Conventional Droop Control -- 4.1.2 Reactive Power Sharing Errors Analysis -- 4.2 Reactive Power Sharing Error Compensation Method -- 4.2.1 Droop Controller -- 4.2.2 Communication Setup -- 4.2.3 Convergence Analysis -- 4.3 Simulation Results -- 4.3.1 Case 1: Power Sharing Accuracy Improvement -- 4.3.2 Case 2: Effect of Communication Delay -- 4.3.3 Case 3: Effect of Load Change -- 4.4 Experimental Results -- 4.5 Conclusion -- References -- 5 Droop-Based Economical Dispatch -- 5.1 Economical Dispatch Problems Formulation. |
5.2 GOD Criterion and Decentralized Control Schemes -- 5.2.1 GOD Criterion Via Decentralized Manner -- 5.2.2 Decentralized Suboptimal Scheme -- 5.3 Simulation and Experimental Results -- 5.3.1 Case 1: Global Optimal Case -- 5.3.2 Case 2: Suboptimal Case -- 5.3.3 Case 3: Suboptimal Case -- 5.4 Conclusion -- References -- 6 Dynamic |
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Distributed Consensus Control Strategy -- 6.1 Analysis of Modular UPS System -- 6.1.1 Configuration of Modular UPS System -- 6.1.2 Operation Principle of Modular UPS System -- 6.2 Dynamic Consensus-Based Adaptive Virtual ResistanceControl -- 6.3 Simulation Results -- 6.3.1 Case 1: Dynamic Performance Test with Linear Load and Mismatched Line Resistance -- 6.3.2 Case 2: Dynamic Performance Test with Both Linear and Nonlinear Loads -- 6.4 Experimental Results -- 6.4.1 Case 1: Under Linear Load -- 6.4.2 Case 2: Under Generalized Load -- 6.5 Conclusion -- References -- 7 Distributed Event-Triggered Control with Less Communication -- 7.1 Islanded Microgrid Analysis -- 7.1.1 Reactive, Unbalanced, and Harmonic Power Sharing Analysis in Islanded AC Microgrids -- 7.1.2 Communication Network -- 7.2 Distributed Event-Triggered Control -- 7.2.1 Power Calculation -- 7.2.2 Controller Design -- 7.3 Stability Analysis -- 7.3.1 Proof of Theorem -- 7.3.2 Inter-Event Interval Analysis -- 7.4 Experimental Results -- 7.4.1 Case 1: Unbalanced Load -- 7.4.2 Case 2: Nonlinear Load -- 7.4.3 Case 3: Comparison with Periodic Communication -- 7.5 Conclusion -- References -- Part II Series-Type Microgrid Systems -- 8 Decentralized Method for Islanded Operation Mode -- 8.1 Series-Type Microgrid Configuration -- 8.2 Traditional Operation Mode -- 8.3 Decentralized Control Method Design -- 8.3.1 An f-P/Q Droop Control Scheme -- 8.3.2 A New Decentralized Control with Unique Equilibrium Point -- 8.3.3 Power Factor Angle Droop Control -- 8.4 Stability Analysis. |
8.5 Case Study -- 8.5.1 Case 1: Suited for All Types of Loads -- 8.5.2 Case 2: Unique Equilibrium Point -- 8.6 Conclusion -- References -- 9 Decentralized Optimal Economical Dispatch Scheme -- 9.1 Economical Optimization of Series-Type Microgrids -- 9.1.1 Economical Optimization Problem Formulation -- 9.2 Communication-Free Economical Operation Control Scheme -- 9.2.1 Control Scheme -- 9.2.2 Steady-State Analysis -- 9.3 Stability Analysis -- 9.4 Simulation Results -- 9.4.1 Case 1: Switch Between the RL and RC Load -- 9.4.2 Case 2: Optimal Economical Operation Under RL Load -- 9.4.3 Case 3: Optimal Economical Operation Under RC Load -- 9.4.4 Case 4: Capacity Constraints -- 9.4.5 Case 5: Comparisons Between the Scheme and Existing Method -- 9.4.6 Case 6: Performance of the Scheme Under the Feeder Impedance Variation -- 9.5 Experimental Results -- 9.6 Conclusion -- References -- 10 Decentralized SOC Balancing Control for Series-Type Storages -- 10.1 Decentralized SOC Balancing Control -- 10.1.1 Equivalent Model of Series Energy Storage System -- 10.1.2 Approximate Relationship Between SOC and Output Power -- 10.1.3 SOC Balancing Control Method -- 10.1.4 Design of Double Control Loop -- 10.2 Stability Analysis of the Decentralized SOC BalancingControl -- 10.2.1 Singular Perturbation Theory -- 10.2.2 System Model -- 10.2.3 Analysis on the Outer System -- 10.2.4 Analysis on the Boundary Layer System -- 10.3 Simulation Results -- 10.3.1 Case 1: SOC Balancing in Four Quadrant Operations -- 10.3.2 Case 2: Mode Switching Between Discharging and Charging -- 10.3.3 Case 3: Simulation Tests Under Discharging Mode with Load Characteristics Changing -- 10.3.4 Case 4: Different Capacities of ESU -- 10.3.5 Case 5: Comparison of ESS with and Without SOC Balancing Control -- 10.4 Experimental Results -- 10.5 Conclusion -- References. |
11 Decentralized Control Strategies in Grid-Connected Mode -- 11.1 Decentralized Control for Grid-Connected Series-Connected Inverters -- 11.1.1 Equivalent Models of Grid-Connected Series-Connected Inverters -- 11.1.2 Decentralized P-ω Droop Control -- 11.1.3 Steady State and Stability Analysis -- 11.1.4 Simulation Results -- 11.2 |
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Decentralized Control for Series-Connected H-BridgeRectifiers -- 11.2.1 Models of Series-Connected Rectifiers -- 11.2.2 Decentralized Control for Series-Connected H-Bridge Rectifiers -- 11.2.3 Steady State and Synchronization Mechanism Analysis -- 11.2.4 Discussion and Comparisons Between the Introduced Control and Existing Methods -- 11.2.5 Experimental Results -- 11.3 Decentralized Control Scheme for Medium/High Voltage Series-Connected STATCOM -- 11.3.1 Models of Series-Connected STATCOM -- 11.3.2 Decentralized Control for Series-ConnectedSTATCOM -- 11.3.3 Steady State and Stability Analysis -- 11.3.4 Improved Decentralized Control for Abnormal-Grid Condition -- 11.3.5 Simulation Results -- 11.4 Conclusion -- References -- 12 A Master-Slave Control in Grid-Connected Applications -- 12.1 Hybrid Voltage/Current Control -- 12.1.1 Control Configuration of Series Inverters -- 12.1.2 Hybrid Voltage/Current Control in DecentralizedManner -- 12.2 Performance Discussion and Comparison -- 12.2.1 Steady-State Analysis and Comparison with Existing Methods -- 12.2.2 Synchronization Mechanism -- 12.2.3 Discussion of One-to-All-Failure Redundancy -- 12.3 Experimental Results -- 12.3.1 Case 1: Source Power Change Under Unity PF -- 12.3.2 Case 2: Grid Voltage Sag Under No-Unity PF -- 12.3.3 Case 3: Large Source Power Gap Among SomeInverters -- 12.3.4 Case 4: Grid Frequency Deviation -- 12.3.5 Case 5: Grid Harmonics Condition -- 12.3.6 Case 6: Grid Impedance Variation -- 12.3.7 Case 7: One CCI Unit Fault Redundancy. |
12.3.8 Case 8: One VCI Unit Fault Redundancy. |
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3. |
Record Nr. |
UNINA9910973988803321 |
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Autore |
Foreman John W. |
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Titolo |
Data smart : using data science to transform information into insight / / John W. Foreman |
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Pubbl/distr/stampa |
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Indianapolis : , : Wiley, , [2014] |
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©2014 |
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ISBN |
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1-118-83986-2 |
1-118-66148-6 |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (434 p.) |
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Disciplina |
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Soggetti |
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Data mining |
Web sites - Design |
Web usage mining |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Cover; Title Page; Copyright; Contents; Chapter 1 Everything You Ever Needed to Know about Spreadsheets but Were Too Afraid to Ask; Some Sample Data; Moving Quickly with the Control Button; Copying Formulas and Data Quickly; Formatting Cells; Paste Special Values; Inserting Charts; Locating the Find and Replace Menus; Formulas for Locating and Pulling Values; Using VLOOKUP to Merge Data; Filtering and Sorting; Using PivotTables; Using Array Formulas; Solving Stuff with Solver; OpenSolver: I Wish We Didn't Need This, but We Do; Wrapping Up |
Chapter 2 Cluster Analysis Part I: Using K-Means to Segment Your Customer Base Girls Dance with Girls, Boys Scratch Their Elbows; Getting Real: K-Means Clustering Subscribers in E-mail Marketing; Joey Bag O' Donuts Wholesale Wine Emporium; The Initial Dataset; Determining What to Measure; Start with Four Clusters; Euclidean Distance: Measuring Distances as the Crow Flies; Distances and Cluster Assignments for Everybody!; Solving for the Cluster Centers; Making Sense of the Results; Getting the Top Deals by Cluster; The Silhouette: A Good Way to Let Different K Values Duke It Out |
How about Five Clusters? Solving for Five Clusters; Getting the Top |
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Deals for All Five Clusters; Computing the Silhouette for 5-Means Clustering; K-Medians Clustering and Asymmetric Distance Measurements; Using K-Medians Clustering; Getting a More Appropriate Distance Metric; Putting It All in Excel; The Top Deals for the 5-Medians Clusters; Wrapping Up; Chapter 3 Naive Bayes and the Incredible Lightness of Being an Idiot; When You Name a Product Mandrill, You're Going to Get Some Signal and Some Noise; The World's Fastest Intro to Probability Theory; Totaling Conditional Probabilities |
Joint Probability, the Chain Rule, and Independence What Happens in a Dependent Situation?; Bayes Rule; Using Bayes Rule to Create an AI Model; High-Level Class Probabilities Are Often Assumed to Be Equal; A Couple More Odds and Ends; Let's Get This Excel Party Started; Removing Extraneous Punctuation; Splitting on Spaces; Counting Tokens and Calculating Probabilities; And We Have a Model! Let's Use It; Wrapping Up; Chapter 4 Optimization Modeling: Because That "Fresh Squeezed" Orange Juice Ain't Gonna Blend Itself; Why Should Data Scientists Know Optimization? |
Starting with a Simple Trade-Off Representing the Problem as a Polytope; Solving by Sliding the Level Set; The Simplex Method: Rooting around the Corners; Working in Excel; There's a Monster at the End of This Chapter; Fresh from the Grove to Your Glass...with a Pit Stop Through a Blending Model; You Use a Blending Model; Let's Start with Some Specs; Coming Back to Consistency; Putting the Data into Excel; Setting Up the Problem in Solver; Lowering Your Standards; Dead Squirrel Removal: The Minimax Formulation; If-Then and the "Big M" Constraint |
Multiplying Variables: Cranking Up the Volume to 11 |
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Sommario/riassunto |
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Data Science gets thrown around in the press like it's magic. Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions. But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the ""data scientist,"" to extract this gold from your data? Nope. Data science is little more than using straight-forward steps to process raw data into actionable insight. |
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