ON-LINE CONTROL OF POWER SYSTEMS

ECE 723

Course Description

State estimation based on line-flow measurements. Detection and correction of incorrect on-line measurements. Reduction techniques. Network security evaluation. On-line contingency studies and contingency remedial action. Calculation of penalty factors and optimal power dispatch strategies. On-line stability determination. Parallel processors for on-line studies. Knowledge of basic probability analysis [such asE C E 331,STAT/MATH 431, orSTAT 311] strongly encouraged.

Prerequisties

Graduate/professional standing

Satisfies

This course does not satisfy any prerequisites.

Credits

Not Reported

Offered

Not Reported

Grade Point Average

2.88

-23.33% from Historical

Completion Rate

87.5%

-10.31% from Historical

A Rate

50%

-40.58% from Historical

Class Size

36.59% from Historical

Instructors (2025 Fall)

Sorted by ratings from Rate My Professors

Similar Courses

FEEDBACK CONTROL SYSTEMS

ECE 332

Modeling of continuous systems; computer-aided solutions to systems problems; feedback control systems; stability, frequency response and transient response using root locus, frequency domain and state variable methods.

ELECTRIC POWER SYSTEMS

ECE 427

The electric power industry, operation of power systems, load flow, fault calculations, economic dispatch, general technical problems of electric power networks.

STATE SPACE SYSTEMS ANALYSIS

ECE 334

Analysis of systems using matrix methods to write and solve state-variable differential equations. Additional topics include stability, controllability, observability, state feedback, observers, and dynamic output feedback.

ADVANCED POWER SYSTEM ANALYSIS

ECE 731

Electrical transients due to faults and switching. Effect on power system design and operation. Traveling waves and surge protection. Computerized analysis of power transients.

PROBABILITY AND RANDOM PROCESSES

ECE 730

Review of basic probability. Advanced probability concepts. Random vectors; linear filtering of random processes; stationarity; power spectral densities; estimation; convergence; Markov chains; Poisson process; Wiener process. Knowledge of basic probability analysis [such asE C E 331,STAT/MATH 431, orSTAT 311] strongly encouraged.