Genetic algorithms based on an intelligent controller

Dangprasert, P.; Avatchanakorn, Vichit

doi:10.1016/0957-4174(96)00026-7

Cited by 8 publications

(3 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The controller requires no prior knowledge or training data for learning and has an online parameter identification function. Another application of GA to optimize the parameters of conventional AGC systems is presented in [263]. The optimization process of the genetic algorithm is carried out in conjunction with a numerical simulation.…”

Section: Genetic Algorithms (Gas)mentioning

confidence: 99%

“…The analysis also includes more elaborate feedback control strategies, such as the PI type in the decentralized frame. The authors of [263] demonstrated the effectiveness of genetic algorithms in tuning AGC parameters. The application of fuzzy gain planning on PI LFC for a multi-area interconnected power system is described in [264].…”

Section: Genetic Algorithms (Gas)mentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Review of Load Frequency Control Technologies

Rasolomampionona,

Połecki,

Zagrajek

et al. 2024

Energies

View full text Add to dashboard Cite

Load frequency control (LFC) is one of the most important tools in power system control. LFC is an auxiliary service related to the short-term balance of energy and frequency of power systems. As such, it allows the acquisition of a central role in enabling electricity exchanges and providing better conditions. The classification of LFC can be carried out from different angles: we can enumerate, among others, the type of control used. The following types of control are presented in this review: classical, optimal, and robust control. More advanced controls can also be used for classification: fuzzy logic control, ANN control, genetic algorithms, PSO control, etc. The influence of renewables and power control tools like FACTS is also considered as a category to be analyzed. The last classifications are related to two important subjects—the influence of DC links on LFC efficiency and the dangers of cyberattacks on the LFC.

show abstract

Section: Genetic Algorithms (Gas)mentioning

confidence: 99%

Section: Genetic Algorithms (Gas)mentioning

confidence: 99%

A Comprehensive Review of Load Frequency Control Technologies

Rasolomampionona,

Połecki,

Zagrajek

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

“…However, the nonlinear, strong and complex constraints, as shown in [13], make difficult the synthesis of optimal predictive control because the linear programming might be unsuitable for this kind of optimization problems [7,11]. To solve these problems, various studies [4,12,13] focused on the SISO systems have used the combination of T-S fuzzy model and PSO algorithm to improve nonlinear generalized predictive control [10]. In this work, we combine the T-S fuzzy modeling approach and the intelligent optimization algorithm PSO to solve the constrained nonlinear predictive control of MIMO system.…”

Section: Introductionmentioning

confidence: 99%

Constrained multivariable predictive controller based on PSO algorithm and T-S fuzzy modeling approach

Thamallah

Sakly

M'sahli

2014

2014 15th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)

View full text Add to dashboard Cite

This paper describes the development of a strategy to optimally tune constrained predictive controller of Multi Inputs Multi Outputs Nonlinear system with T-S fuzzy modeling approach. The proposed method consists of using T-S fuzzy modeling to determine process model. The intelligent algorithms Particle Swam Optimization is applied to provide the controls action by solving nonlinear optimization problems which is function of the future prediction outputs and the future inputs. The performance of this strategy is evaluated through its application to Quadruple-Tank Process.

show abstract