2021
DOI: 10.1108/compel-04-2020-0159
|View full text |Cite
|
Sign up to set email alerts
|

Parameter identification for load frequency control using fuzzy FOPID in power system

Abstract: Purpose This paper aims to suggest the parameter identification of load frequency controller in power system. Design/methodology/approach The suggested control approach is established using fuzzy logic to design a fractional order load frequency controller. A new suitable control law is developed using fuzzy logic, and based on this developed control law, the unknown parameters of the fractional order proportional integral derivative (FOPID) controller are derived using an optimization technique, which is be… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
1
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
7

Relationship

0
7

Authors

Journals

citations
Cited by 9 publications
(2 citation statements)
references
References 57 publications
0
1
0
Order By: Relevance
“…In order to solve this problem, the authors propose to use deep learning techniques in the power of intelligence and a new method of deep learning taking into account the uncertainty of prediction. To analyze the efectiveness of the method, the historical data of the energy load of the power plant over a period of time was analyzed [16]. Te power grid continuously records the complete power load data in 2021; due to the large amount of data, Figure 3 only shows the complete power load historical data for two days.…”
Section: Resultsmentioning
confidence: 99%
“…In order to solve this problem, the authors propose to use deep learning techniques in the power of intelligence and a new method of deep learning taking into account the uncertainty of prediction. To analyze the efectiveness of the method, the historical data of the energy load of the power plant over a period of time was analyzed [16]. Te power grid continuously records the complete power load data in 2021; due to the large amount of data, Figure 3 only shows the complete power load historical data for two days.…”
Section: Resultsmentioning
confidence: 99%
“…There is also room for application in electrical engineering-for example, identifying faults in distribution grids and hydroelectric units in the power market. In control science and engineering, least squares support vector machines and local regression neural networks are used for parameter identification in PID control (Kumar & Sikander, 2021). In mathematics, Drosophila optimization algorithms enable global convergence of mathematical models, parameter estimation optimization problems and the optimization of mathematical models.…”
Section: Research Backgroundmentioning
confidence: 99%