Placement and Coordinated Tuning of Control Devices for Capacity and Security Enhancement Using...

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 JaneiroCOPPE Brazil

* Also with CEPEL/Eletrobrás CEPEL

2NSF Workshop on Applied Mathematics for Deregulated Electric Power Systems - Washington - November 2003

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


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)


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


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


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


Decomposed Approach

Placement Problem• Location • Type• Control Structure• Parameters

Coordinated TuningProblem

• Parameter ..Adjustment

PlacementDecisions

Performance of Tuned Controllers

Integer Programming Problem• Branch-and-bound• Metaheuristics • Etc.

Continuous Optimization Problem• Non-linear programming• Metaheuristics • Etc.

Bender’s Decomposition


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.


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

Location Type Control Structure Parameters


GA Aided Control System Design

Genetic Algorith

m

Software

Performance Index Evaluation

(Fitness Function)Software for

Control System Simulation

Linear AnalysysEtc.


GA Aided Control System Design

S

C

S

CS

C

S

C

Population of Potential Solutions

Time Simulation

Eigenanalysis

Other Methods

.

.

Performance Index

Evaluation(Fitness Function)

Genetic Operators• Selection• Crossover• Mutation


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


Flow Chart of the Optimization Strategy

Genetic Algorithm

Load Factor Increase

Program Initialization and Ending

Load Flow


Results: System Loadability

SaturationRelatively Small Improvement

SaturationRelatively Small Improvement


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


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:


Problem Formulation

m: number of operating conditions

n: system order

: closed-loop system eigenvalue damping ratio

K, , T : controllers parameters

Fitness Function:


Results

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

Damping ratio for closed-loop eigenvalues


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


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


Results

Scenario 2

Scenario 1

Scenario 3Damping enhancement

constrained by low-

damped multivariable

zero

Closed-loop eigenvalues


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


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


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

Placement and Coordinated Tuning of Control Devices for Capacity and Security Enhancement Using...

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