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Placement and Coordinated Tuning of Control Devices for Capacity and Security Enhancement Using Genetic Algorithms and O

CEPEL. Placement and Coordinated Tuning of Control Devices for Capacity and Security Enhancement Using Genetic Algorithms and Other Metaheuristics. Djalma M. Falcão* Glauco N. Taranto Federal University of Rio de Janeiro COPPE Brazil

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Placement and Coordinated Tuning of Control Devices for Capacity and Security Enhancement Using Genetic Algorithms and O

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  1. CEPEL Placement and Coordinated Tuning of Control Devices for Capacity and Security Enhancement Using Genetic Algorithms and Other Metaheuristics Djalma M. Falcão* Glauco N. Taranto Federal University of Rio de Janeiro COPPE Brazil * Also with CEPEL/Eletrobrás

  2. Summary • Motivation • Power System Controls • Placement and Coordinated Tuning • GAs and Other Metaheuristics Approach • Examples • FACTS placement for loadability improvement • FACTS tuning for damping control • PSS tuning for damping control in a large scale power system • Ongoing Work • Future Work • Conclusions

  3. Motivation • New Scenario • Regulatory uncertainty • Difficulties in line and plant construction • Power systems must operate reliably and efficiently under a variety of operating conditions • Robust control • Coordinated tuning • Wide-area control • Available Technology / Challenges • Computer, Communication, and Control • Wide-Area Monitoring Systems (WAMS) • New design and optimization technologies (metaheuristics)

  4. Power System Controls • Available Controllers • Generators: AVRs, Governors, PSSs, etc. • OLTC transformers • FACTS • HVDC links • Automatic Generation Control and Coordinated Voltage Control • Control Strategies • Mostly local or task oriented • Placed and designed on an ad hoc basis • Present situation requires a better use of available control • System-wide performance • Robustness in the presence of component losses

  5. Placement & Coordinated Tuning • Placement Problem • Location (branch, bus, generator, etc.) • Type: FACTS (TCSC, SVC, UPFC, etc.), PSS, etc. • Control Structure • Parameters (range) • Coordinated Tuning (given a set of controllers) • Parameter adjustment • Combined Placement & Tuning • More complex and larger problem • “Global” optimization

  6. Combined Placement and Tuning • Mixed-Integer Nonlinear Programming Problem • “Unfriendly” Characteristics • Large scale: thousands of variables • Non-convex functions • Some functions may not be available explicitly • Design bounds not easily determined • Possible Approaches • Two stage solution approach • Propose a potential solution for the placement problem • Coordinated tuning of controller for that potential solution • Simultaneous solution approach using Metaheuristics

  7. Placement Problem • Location • Type • Control Structure • Parameters • Integer Programming Problem • Branch-and-bound • Metaheuristics • Etc. Performance of Tuned Controllers Placement Decisions • Coordinated Tuning • Problem • Parameter ..Adjustment • Continuous Optimization Problem • Non-linear programming • Metaheuristics • Etc. Decomposed Approach Bender’s Decomposition

  8. Metaheuristic(Free On-Line Dictionary of Computing) • A top-level general strategy which guides other heuristics to search for feasible solutions in domains where the task is hard • Metaheuristics have been most generally applied to problems classified as NP-Hard or NP-Complete by the theory of Computational Complexity • Metaheuristics would also be applied to other combinatorial optimization problems for which it is known that a polynomial-time solution exists but is not practical • Examples of Metaheuristics are Tabu Search, Simulated Annealing, Genetic Algorithms, Particle Sworm Optimization, etc.

  9. Location Type Control Structure Parameters Metaheuristics Approach • Placement and tuning problem can be solved simultaneously • Potential solutions are coded in a “computational structure” • Population of potential solutions are evolved according to metaheuristic rules • Global optimization is not assured but usually finds good “engineering solutions” • Deals nicely with multiobjective problems • Very large computation requirements: high performance computing may be required

  10. GA Aided Control System Design Performance Index Evaluation (Fitness Function) Software for Control System Simulation Linear Analysys Etc. Genetic Algorithm Software

  11. Population of Potential Solutions Time Simulation Performance Index Evaluation (Fitness Function) S S S S C C C C Eigenanalysis . . . Other Methods • Genetic Operators • Selection • Crossover • Mutation GA Aided Control System Design

  12. Example 1: Optimal Location of Multi-Type FACTS Devices by Means of GAs • Gerbex, Chekaoui & Germond, IEEE PWRS, August 2001 • Steady-state modeling: Load Flow model • Performance index (fitness function): System Loadability • Constraints: Thermal and Voltage Limits • FACTS Devices considered: TCSC, TCPST, TCVR, SVC • Test System: IEEE 118 bus

  13. Load Flow Genetic Algorithm Load Factor Increase Program Initialization and Ending Flow Chart of the Optimization Strategy

  14. Saturation Saturation Relatively Small Improvement Relatively Small Improvement Results: System Loadability

  15. Example 2: Robust Decentralized Control Design using GAs in Power System Damping Control • Taranto & Falcão, Proceedings of IEE, Part C, Jan. 1998 • Linearized dynamic model: Small-Signal Stability model • Performance index (fitness function): Sum of the Spectrum Damping Ratio for all operating conditions • Constraints: Bounds on controllers parameters and minimum damping ratio • Test System: Hypothetical 12 bus, 6 generators system

  16. Test System • Hypothetical 12 bus and 6 generators power system • SVC and TCSC • All generators modeled with six variables with identical parameters • Five operating conditions • Two low-frequency electromechanical inter-area oscillatory modes: • Mode 1: B  A + C • Mode 2: A  C • Controllers structure:

  17. Problem Formulation m: number of operating conditions n: system order  : closed-loop system eigenvalue damping ratio K,  , T : controllers parameters Fitness Function:

  18. Results Damping ratio for closed-loop eigenvalues Nominal: controllers designed using classical control techniques GA: controllers designed using GA NDFS: direct flow (generators 3, 4, 5, 6 are exporting; main load L3) NRFS: reverse flow (decrease L3 , increase L10 , reverse flow in TCSC) Weak 1: NDFS with a weaker tie in the SVC transmission path Weak 2: NDFS with a weaker tie in the TCSC transmission path Weak 3: NRFS with a weaker tie in the TCSC transmission path

  19. Example 3: Simultaneous Tuning of Power System Damping Controllers Using GA • Bomfim, Taranto & Falcão, IEEE PWRS, February 2000 • Linearized dynamic model: Small-Signal Stability model • Performance index (fitness function): Sum of the Spectrum Damping Ratio for all operating conditions • Constraints: Bounds on controllers parameters and minimum damping ratio • Problem Formulation: similar to example 2 • Test System: Brazilian interconnected power system

  20. Test System • Equivalent of the Brazilian South-Southeastern System • Model: • 1762 AC buses • 2515 AC branches • 57 synchronous generators • 22 PSSs • DC link not modeled dynamically • 450 state variables • Three operating scenarios considered • Controllers structure: identical to example 2

  21. Scenario 1 Scenario 2 Scenario 3 Results Closed-loop eigenvalues Damping enhancement constrained by low- damped multivariable zero

  22. Comments • The GA based tuning process has shown robustness in achieving controllers satisfying the design criteria in a large-scale realistic power system • Large computation time • Approximately 8h in a Pentium 4 processor • Most of time spent in the eigenvalue calculations (QR) • Parallel implementation on a Cluster of PCs: considerable reduction in computing time • Combination of GAs approaches with other design methods: • Pole placement • LMI

  23. Ongoing Work • Experiments with other objective functions • Frequency domain based • Time domain based • Simultaneous tuning of PSS and AVRs • Objective: higher performance of the excitation system • Performance Index: combination of frequency and time domain features • Difficulties: higher computation time requirements

  24. Future Work • Improvements in the GA-based methodology aimed to solve the combined placement and tuning problem • Tests with other metaheuristics and hybrid formulations • New challenges: • Ability of the control system to respond properly to catastrophic events • Integrated analysis of control and protection systems

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