TY  - BOOK
AU  - Bevrani,Hassan
AU  - Hiyama,Takashi
TI  - Intelligent automatic generation control
SN  - 9781439849538
AV  - TK1005 .B48 2011
PY  - 2011///]
CY  - Boca Raton
PB  - CRC Press
KW  - Electric power systems
KW  - Automation
KW  - Intelligent control systems
N1  - Includes bibliographical references and index; Table Of Contents; Preface; Acknowledgments; 1 Intelligent Power System Operation and Control: Japan Case Study; 1.1 Application of Intelligent Methods to Power Systems; 1.2 Application to Power System Planning; 1.2.1 Expansion Planning of Distribution Systems; 1.2.2 Load Forecasting; 1.2.3 Unit Commitment; 1.2.4 Maintenance Scheduling; 1.3 Application to Power System Control and Restoration; 1.3.1 Fault Diagnosis; 1.3.2 Restoration; 1.3.3 Stabilization Control; 1.4 Future Implementations; 1.5 Summary; References; 2 Automatic Generation Control (AGC): Fundamentals and Concepts; 2.1 AGC in a Modern Power System; 2.2 Power System Frequency Control; 2.2.1 Primary Control; 2.2.2 Supplementary Control; 2.2.3 Emergency Control; 2.3 Frequency Response Model and AGC Characteristics; 2.3.1 Droop Characteristic; 2.3.2 Generation-Load Model; 2.3.3 Area Interface; 2.3.4 Spinning Reserve; 2.3.5 Participation Factor; 2.3.6 Generation Rate Constraint; 2.3.7 Speed Governor Dead-Band; 2.3.8 Time Delays; 2.4 A Three-Control Area Power System Example; 2.5 Summary; References; 3 Intelligent AGC: Past Achievements and New Perspectives; 3.1 Fuzzy Logic AGC; 3.1.1 Fuzzy Logic Controller; 3.1.2 Fuzzy-Based PI (PID) Controller; 3.2 Neuro-Fuzzy and Neural-Networks-Based AGC; 3.3 Genetic-Algorithm-Based AGC; 3.4 Multiagent-Based AGC; 3.5 Combined and Other Intelligent Techniques in AGC; 3.6 AGC in a Deregulated Environment; 3.7 AGC and Renewable Energy Options; 3.7.1 Present Status and Future Prediction; 3.7.2 New Technical Challenges; 3.7.3 Recent Achievements; 3.8 AGC and Microgrids; 3.9 Scope for Future Work; 3.9.1 Improvement of Modeling and Analysis Tools; 3.9.2 Develop Effective Intelligent Control Schemes for Contribution of DGs/RESs in the AGC Issue; 3.9.3 Coordination between Regulation Powers of DGs/RESs and Conventional Generators; 3.9.4 Improvement of Computing Techniques and Measurement Technologies; 3.9.5 Use of Advanced Communication and Information Technology; 3.9.6 Update/Define New Grid Codes; 3.9.7 Revising of Existing Standards; 3.9.8 Updating Deregulation Policies; 3.10 Summary; References; 4 AGC in Restructured Power Systems; 4.1 Control Area in New Environment; 4.2 AGC Configurations and Frameworks; 4.2.1 AGC Configurations; 4.2.2 AGC Frameworks; 4.3 AGC Markets; 4.4 AGC Response and an Updated Model; 4.4.1 AGC System and Market Operator; 4.4.2 AGC Model and Bilateral Contracts; 4.4.3 Need for Intelligent AGC Markets; 4.5 Summary; References; 5 Neural-Network-Based AGC Design; 5.1 An Overview; 5.2 ANN-Based Control Systems; 5.2.1 Fundamental Element of ANNs; 5.2.2 Learning and Adaptation; 5.2.3 ANNs in Control Systems; 5.3 Flexible Neural Network; 5.3.1 Flexible Neurons; 5.3.2 Learning Algorithms in an FNN; 5.4 Bilateral AGC Scheme and Modeling; 5.4.1 Bilateral AGC Scheme; 5.4.2 Dynamical Modeling; 5.5 FNN-Based AGC System; 5.6 Application Examples; 5.6.1 Single-Control Area; 5.6.2 Three-Control Area; 5.7 Summary; References; 6 AGC Systems Concerning Renewable Energy Sources; 6.1 An Updated AGC Frequency Response Model; 6.2 Frequency Response Analysis; 6.3 Simulation Study; 6.3.1 Nine-Bus Test System; 6.3.2 Thirty-Nine-Bus Test System; 6.4 Emergency Frequency Control and RESs; 6.5 Key Issues and New Perspectives; 6.5.1 Need for Revision of Performance Standards; 6.5.2 Further Research Needs; 6.6 Summary; References; 7 AGC Design Using Multiagent Systems; 7.1 Multiagent System (MAS): An Introduction; 7.2 Multiagent Reinforcement-Learning-Based AGC; 7.2.1 Multiagent Reinforcement Learning; 7.2.2 Area Control Agent; 7.2.3 RL Algorithm; 7.2.4 Application to a Thirty-Nine-Bus Test System; 7.3 Using GA to Determine Actions and States; 7.3.1 Finding Individual's Fitness and Variation Ranges; 7.3.2 Application to a Three-Control Area Power System; 7.4 An Agent for β Estimation; 7.5 Summary; References; 8 Bayesian-Network-Based AGC Approach; 8.1 Bayesian Networks: An Overview; 8.1.1 BNs at a Glance; 8.1.2 Graphical Models and Representation; 8.1.3 A Graphical Model Example; 8.1.4 Inference; 8.1.5 Learning; 8.2 AGC with Wind Farms; 8.2.1 Frequency Control and Wind Turbines; 8.2.2 Generalized ACE Signal; 8.3 Proposed Intelligent Control Scheme; 8.3.1 Control Framework; 8.3.2 BN Structure; 8.3.3 Estimation of Amount of Load Change; 8.4 Implementation Methodology; 8.4.1 BN Construction; 8.4.2 Parameter Learning; 8.5 Application Results; 8.5.1 Thirty-Nine-Bus Test System; 8.5.2 A Real-Time Laboratory Experiment; 8.6 Summary; References; 9 Fuzzy Logic and AGC Systems; 9.1 Study Systems; 7.1.1 Two Control Areas with Subareas; 9.1.2 Thirty-Nine-Bus Power System; 9.2 Polar-Information-Based Fuzzy Logic AGC; 9.2.1 Polar-Information-Based Fuzzy Logic Control; 9.2.2 Simulation Results; 9.2.2.1 Trunk Line Power Control; 9.2.2.2 Control of Regulation Margin; 9.3 PSO-Based Fuzzy Logic AGC; 9.3.1 Particle Swarm Optimization; 9.3.2 AGC Design Methodology; 9.3.3 PSO Algorithm for Setting of Membership Functions; 9.3.4 Application Results; 9.4 Summary; References; 10 Frequency Regulation Using Energy Capacitor System; 10.1 Fundamentals of the Proposed Control Scheme; 10.1.1 Restriction of Control Action (Type I); 10.1.2 Restriction of Control Action (Type II); 10.1.3 Prevention of Excessive Control Action (Type III); 10.2 Study System; 10.3 Simulation Results; 10.4 Evaluation of Frequency Regulation Performance; 10.5 Summary; References; 11 Application of Genetic Algorithm in AGC Synthesis; 11.1 Genetic Algorithm: An Overview; 11.1.1 GA Mechanism; 11.1.2 GA in Control Systems; 11.2 Optimal Tuning of Conventional Controllers; 11.3 Multiobjective GA; 11.3.1 Multiobjective Optimization; 11.3.2 Application to AGC Design; 11.4 GA for Tracking Robust Performance Index; 11.4.1 Mixed H2/H∞; 11.4.2 Mixed H2/H∞ SOF Design; 11.4.3 AGC Synthesis Using GA-Based Robust Performance Tracking; 11.5 GA in Learning Process; 11.5.1 GA for Finding Training Data in a BN-Based AGC Design; 11.5.2 Application Example; 11.6 Summary; References; 12 Frequency Regulation in Isolated Systems with Dispersed Power Sources; 12.1 Configuration of Multiagent-Based AGC System; 12.1.1 Conventional AGC on Diesel Unit; 12.1.2 Coordinated AGC on the ECS and Diesel Unit; 12.2 Configuration of Laboratory System; 12.3 Experimental Results; 12.4 Summary; References; Index
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