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