000			07231nam a2200301 i 4500
008			120620s2011 flu b a001 0 eng d
020			_a9781439849538
020			_a1439849536
040			_aBAUN _beng _cBAUN _erda
049			_aBAUN_MERKEZ
050	0	4	_aTK1005 _b.B48 2011
100	1		_aBevrani, Hassan
245	1	0	_aIntelligent automatic generation control / _cHassan Bevrani, Takashi Hiyama
264		1	_aBoca Raton : _bCRC Press, _c[2011]
264		4	_c©2011
300			_axvii, 290 pages : _billustrations ; _c24 cm
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
504			_aIncludes bibliographical references and index
505	0	0	_tTable Of Contents: _tPreface _tAcknowledgments _t1 Intelligent Power System Operation and Control: Japan Case Study _t1.1 Application of Intelligent Methods to Power Systems _t1.2 Application to Power System Planning _t1.2.1 Expansion Planning of Distribution Systems _t1.2.2 Load Forecasting _t1.2.3 Unit Commitment _t1.2.4 Maintenance Scheduling _t1.3 Application to Power System Control and Restoration _t1.3.1 Fault Diagnosis _t1.3.2 Restoration _t1.3.3 Stabilization Control _t1.4 Future Implementations _t1.5 Summary _tReferences _t2 Automatic Generation Control (AGC): Fundamentals and Concepts _t2.1 AGC in a Modern Power System _t2.2 Power System Frequency Control _t2.2.1 Primary Control _t2.2.2 Supplementary Control _t2.2.3 Emergency Control _t2.3 Frequency Response Model and AGC Characteristics _t2.3.1 Droop Characteristic _t2.3.2 Generation-Load Model _t2.3.3 Area Interface _t2.3.4 Spinning Reserve _t2.3.5 Participation Factor _t2.3.6 Generation Rate Constraint _t2.3.7 Speed Governor Dead-Band _t2.3.8 Time Delays _t2.4 A Three-Control Area Power System Example _t2.5 Summary _tReferences _t3 Intelligent AGC: Past Achievements and New Perspectives _t3.1 Fuzzy Logic AGC _t3.1.1 Fuzzy Logic Controller _t3.1.2 Fuzzy-Based PI (PID) Controller _t3.2 Neuro-Fuzzy and Neural-Networks-Based AGC _t3.3 Genetic-Algorithm-Based AGC _t3.4 Multiagent-Based AGC _t3.5 Combined and Other Intelligent Techniques in AGC _t3.6 AGC in a Deregulated Environment _t3.7 AGC and Renewable Energy Options _t3.7.1 Present Status and Future Prediction _t3.7.2 New Technical Challenges _t3.7.3 Recent Achievements _t3.8 AGC and Microgrids _t3.9 Scope for Future Work _t3.9.1 Improvement of Modeling and Analysis Tools _t3.9.2 Develop Effective Intelligent Control Schemes for Contribution of DGs/RESs in the AGC Issue _t3.9.3 Coordination between Regulation Powers of DGs/RESs and Conventional Generators _t3.9.4 Improvement of Computing Techniques and Measurement Technologies _t3.9.5 Use of Advanced Communication and Information Technology _t3.9.6 Update/Define New Grid Codes _t3.9.7 Revising of Existing Standards _t3.9.8 Updating Deregulation Policies _t3.10 Summary _tReferences _t4 AGC in Restructured Power Systems _t4.1 Control Area in New Environment _t4.2 AGC Configurations and Frameworks _t4.2.1 AGC Configurations _t4.2.2 AGC Frameworks _t4.3 AGC Markets _t4.4 AGC Response and an Updated Model _t4.4.1 AGC System and Market Operator _t4.4.2 AGC Model and Bilateral Contracts _t4.4.3 Need for Intelligent AGC Markets _t4.5 Summary _tReferences _t5 Neural-Network-Based AGC Design _t5.1 An Overview _t5.2 ANN-Based Control Systems _t5.2.1 Fundamental Element of ANNs _t5.2.2 Learning and Adaptation _t5.2.3 ANNs in Control Systems _t5.3 Flexible Neural Network _t5.3.1 Flexible Neurons _t5.3.2 Learning Algorithms in an FNN _t5.4 Bilateral AGC Scheme and Modeling _t5.4.1 Bilateral AGC Scheme _t5.4.2 Dynamical Modeling _t5.5 FNN-Based AGC System _t5.6 Application Examples _t5.6.1 Single-Control Area _t5.6.2 Three-Control Area _t5.7 Summary _tReferences _t6 AGC Systems Concerning Renewable Energy Sources _t6.1 An Updated AGC Frequency Response Model _t6.2 Frequency Response Analysis _t6.3 Simulation Study _t6.3.1 Nine-Bus Test System _t6.3.2 Thirty-Nine-Bus Test System _t6.4 Emergency Frequency Control and RESs _t6.5 Key Issues and New Perspectives _t6.5.1 Need for Revision of Performance Standards _t6.5.2 Further Research Needs _t6.6 Summary _tReferences _t7 AGC Design Using Multiagent Systems _t7.1 Multiagent System (MAS): An Introduction _t7.2 Multiagent Reinforcement-Learning-Based AGC _t7.2.1 Multiagent Reinforcement Learning _t7.2.2 Area Control Agent _t7.2.3 RL Algorithm _t7.2.4 Application to a Thirty-Nine-Bus Test System _t7.3 Using GA to Determine Actions and States _t7.3.1 Finding Individual's Fitness and Variation Ranges _t7.3.2 Application to a Three-Control Area Power System _t7.4 An Agent for β Estimation _t7.5 Summary _tReferences _t8 Bayesian-Network-Based AGC Approach _t8.1 Bayesian Networks: An Overview _t8.1.1 BNs at a Glance _t8.1.2 Graphical Models and Representation _t8.1.3 A Graphical Model Example _t8.1.4 Inference _t8.1.5 Learning _t8.2 AGC with Wind Farms _t8.2.1 Frequency Control and Wind Turbines _t8.2.2 Generalized ACE Signal _t8.3 Proposed Intelligent Control Scheme _t8.3.1 Control Framework _t8.3.2 BN Structure _t8.3.3 Estimation of Amount of Load Change _t8.4 Implementation Methodology _t8.4.1 BN Construction _t8.4.2 Parameter Learning _t8.5 Application Results _t8.5.1 Thirty-Nine-Bus Test System _t8.5.2 A Real-Time Laboratory Experiment _t8.6 Summary _tReferences _t9 Fuzzy Logic and AGC Systems _t9.1 Study Systems _t7.1.1 Two Control Areas with Subareas _t9.1.2 Thirty-Nine-Bus Power System _t9.2 Polar-Information-Based Fuzzy Logic AGC _t9.2.1 Polar-Information-Based Fuzzy Logic Control _t9.2.2 Simulation Results _t9.2.2.1 Trunk Line Power Control _t9.2.2.2 Control of Regulation Margin _t9.3 PSO-Based Fuzzy Logic AGC _t9.3.1 Particle Swarm Optimization _t9.3.2 AGC Design Methodology _t9.3.3 PSO Algorithm for Setting of Membership Functions _t9.3.4 Application Results _t9.4 Summary _tReferences _t10 Frequency Regulation Using Energy Capacitor System _t10.1 Fundamentals of the Proposed Control Scheme _t10.1.1 Restriction of Control Action (Type I) _t10.1.2 Restriction of Control Action (Type II) _t10.1.3 Prevention of Excessive Control Action (Type III) _t10.2 Study System _t10.3 Simulation Results _t10.4 Evaluation of Frequency Regulation Performance _t10.5 Summary _tReferences _t11 Application of Genetic Algorithm in AGC Synthesis _t11.1 Genetic Algorithm: An Overview _t11.1.1 GA Mechanism _t11.1.2 GA in Control Systems _t11.2 Optimal Tuning of Conventional Controllers _t11.3 Multiobjective GA _t11.3.1 Multiobjective Optimization _t11.3.2 Application to AGC Design _t11.4 GA for Tracking Robust Performance Index _t11.4.1 Mixed H2/H∞ _t11.4.2 Mixed H2/H∞ SOF Design _t11.4.3 AGC Synthesis Using GA-Based Robust Performance Tracking _t11.5 GA in Learning Process _t11.5.1 GA for Finding Training Data in a BN-Based AGC Design _t11.5.2 Application Example _t11.6 Summary _tReferences _t12 Frequency Regulation in Isolated Systems with Dispersed Power Sources _t12.1 Configuration of Multiagent-Based AGC System _t12.1.1 Conventional AGC on Diesel Unit _t12.1.2 Coordinated AGC on the ECS and Diesel Unit _t12.2 Configuration of Laboratory System _t12.3 Experimental Results _t12.4 Summary _tReferences _tIndex
650		0	_aElectric power systems _xAutomation
650		0	_aIntelligent control systems
700	1		_aHiyama, Takashi
900			_a33119
900			_bsatın
942			_2lcc _cKT
999			_c30199 _d30199