Smart Energy Laboratory (SEL)
Computational Intelligence Applications (CIA) Laboratory

Department of Electrical Engineering
University of Washington

Power System Security

The purpose of vulnerability assessment is to determine the ability of the power system to continue providing service in case of an unforeseen, but probable, catastrophic contingency. A power system can become vulnerable for various reasons, including major component failures, communication interruptions, human errors, unfavorable weather conditions, and even sabotage. A power system is invulnerable if it withstands all postulated credible contingencies without violating any of the system constraints. If there is at least one contingency (or one sequence of events) for which the system constraints are violated, the system is said to be vulnerable or insecure. Vulnerable system could experience a catastrophic failure of system components leading to blackouts that often affect large portions of the power network, and typically millions of customers.

Target Audience

Electric utility employees who need greater understanding of system security.
System operators
Public agency and regulatory staff with responsibility for electric power issues.
Engineers with and without a background in power systems

Course Topics

Concept of Power System Security

Power System Models

Static Security Assessment

Power System Model for Static Security Assessment
Contingency selection
Contingency Evaluation

Dynamic Security Assessment (DSA)

Power System Model for Dynamic Security Assessment
Features of Dynamic Security Assessment
DAS based on Systems Eigenvalues
DAS based on Lyapunov function
DSA based on Critical Clearing Time
DSA based on Energy Margin
DSA based on Second Kick Method

Challenges of On-line Security Assessment

Computational time
Contingency list
Cascaded events
Operating conditions
Topology change

Security Assessment Border

Concept of security border
Security Index
Border identification
Border tracking

Security Control and Event Response

System Vulnerability
System control to enhance system invulnerability

Advanced Computational Techniques for DSA

Advantages of using Intelligent Techniques
DSA model based on Neural Networks
DSA Border generation based on inverted neural Networks
Evolutionary Computation for border identification
Particle Swarm Optimization for border tracking