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Optimizing FACTS Device Placement Using Evolutionary Algorithms for Enhanced Power System Performance

This project explores optimizing the placement of Flexible AC Transmission System (FACTS) devices utilizing an Evolutionary Algorithm (EA) combined with Sequential Quadratic Programming (SQP). The motivation arises from the need to prevent cascading failures in power systems due to blackouts or attacks. The proposed methodology enhances the economic use of FACTS devices and improves overall system performance. Experimental results show that the Evolutionary Algorithm outperforms traditional heuristic placement methods, providing insight into effective FACTS device coordination and settings.

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Optimizing FACTS Device Placement Using Evolutionary Algorithms for Enhanced Power System Performance

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  1. OPTIMIZING THE PLACEMENT OF FACTS DEVICES USING EVOLUTIONARY ALGORITHM CS 448 Project By Radha Kalyani Professor Dr. Daniel R Tauritz

  2. OUTLINE • Motivation • Problem Statement • EASQP Motivation • Specifications of EA • Experimental Setup • Experimental Results • Conclusion & Future Work

  3. CASCADED FAILURE

  4. Motivation • Blackout in Power System – • Cascading Failure • Terrorist Attack • What can we do? • Install a fast reacting device to control the power flow • UPFC - Third generation FACTS device - Versatile - Controls various parameters of Transmission Line ( Real and Reactive Power ) • How can we use FACTS effectively? • Placement + Settings - Maximize System Performance - Economic use of device - Better coordination of devices

  5. Problem Statement • EA Placement + SQP Settings (EASQP) EA Placement Black Box SQP Black Box Max Flow Algorithm • Max – Flow Control settings + Heuristic Placement (MFH) • Vs • EA Placement + SQP Settings

  6. EA Motivation • Brute Force • 1 to 2 • 3 • 4 or More Fair • Evolutionary Algorithm • Power Flow Equations • Non smooth surfaces • Discontinuous functions • Non differentiable equations • Simple to Implement • Robust

  7. Sequential Quadratic Programming Optimization

  8. Specifications of EA • Fitness Function for the Settings (SQP) Maximize PI = -  [SlineL/ Smax]2 SCLs lineL • Proposed Specifications of EA

  9. Line 20 Line 28 Line 28 Line 22 Line 22 Line 20 FACTS Line 23 Line 23 Line 24 Line 24 Line 21 Line 21 FACTS Customized Specifications of EA • Initialization - Random Initialization ( Popsize – 1) + Best Solution of MFS • Mutation - Move FACTS device from the present line to its neighboring lines

  10. Experimental Setup IEEE 118 BUS DATA 118 buses, 186 lines, 20 generators

  11. Experimental Results • Number of FACTS devices Installed – • 2 FACTS • Parameter Sets

  12. Experimental Results • For 2 FACTS devices Final Best Parameter Set - Parameter Set 3 Placement – ( 69, 158 )

  13. EASQP Placement IEEE 118 BUS DATA

  14. MFH Placement IEEE 118 BUS DATA

  15. Experimental Results

  16. Conclusion & Future Work • EASQP performed better than MFH • Time performance of EASQP has to be improved • Increase FACTS devices • Compare Brute Force with SQP Vs EASQP

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