.png){kind=icon}
Return
A novel chaotic teaching-learning-based optimization algorithm for multi-machine power system stabilizers design problem
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ijepes.2015.11.050.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper proposes an efficient optimization algorithm named chaotic teaching-learning algorithm (CTLA), to solve multimachine power system stabilizers design problem. The original teaching learning algorithm as competitive to other optimization algorithms, used two phases to proceed to the global optimal solution: 'teacher phase' and 'learner phase'. However, during the second phase an adequate interaction between the teacher and the learners in entire search space are not guaranteed and the algorithm may be trapped in local optima. Thus, in the proposed CTLA a new phase named chaotic phase is added in order to overcome this drawback. The performance of the CTLA is investigated by using a set of benchmark functions. To demonstrate the effectiveness of the proposed algorithm in power systems, the conventional lead-lag power system stabilizers (PSSs) are tuned for: three machines nine bus system (WSCC) and the ten machine thirty-nine bus New England power systems. The performance of the proposed CTLA-based PSS (CTLAPSSs) under different loading conditions and disturbances is investigated through eigen-value analysis, non-linear time domain-simulations and some performance indices. (C) 2015 Elsevier Ltd. All rights reserved.
Keywords:
Chaotic teaching learning
Dynamic stability
Low-frequency oscillation
Power system stabilizer
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
5
Papers:
1.1W
Citations:
3.1W
Organization
Cited Papers
.png){kind=icon}
Robust design of multimachine power system stabilizers using fuzzy gravitational search algorithm
PREAI
ShareSaveShareSaveShareSaveShareSaveShareSaveShareSave
View more