2019
DOI: 10.1016/j.ijepes.2018.09.009
|View full text |Cite
|
Sign up to set email alerts
|

Synthetic inertia control based on fuzzy adaptive differential evolution

Abstract: The transformation of the traditional transmission power systems due to the current rise of non-synchronous generation on it presents new engineering challenges. One of the challenges is the degradation of the inertial response due to the large penetration of high power converters used for the interconnection of renewables energy sources. The addition of a supplementary synthetic inertia control loop can contribute to the improvement of the inertial response. This paper proposes the application of a novel Fuzz… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
35
0
1

Year Published

2019
2019
2022
2022

Publication Types

Select...
4
3

Relationship

0
7

Authors

Journals

citations
Cited by 60 publications
(36 citation statements)
references
References 63 publications
(66 reference statements)
0
35
0
1
Order By: Relevance
“…The emulation of VI can be integrated with AI to improve transient response and steady-state error. A fuzzy adaptable logic controller based on a differential evolution algorithm is developed specifically for VI-based frequency regulation [83]. The inertial response of a PV system can be improved by using battery ESS.…”
Section: Future Researchmentioning
confidence: 99%
“…The emulation of VI can be integrated with AI to improve transient response and steady-state error. A fuzzy adaptable logic controller based on a differential evolution algorithm is developed specifically for VI-based frequency regulation [83]. The inertial response of a PV system can be improved by using battery ESS.…”
Section: Future Researchmentioning
confidence: 99%
“…FL is based on set theory to interpret linguistic knowledge in control rules, therefore a fuzzy logic controller (FLC) works without a mathematical model so it does not consider uncertainties and unknown parameters that make a non-linear system [27]. The development of an FL follows three steps [28][29][30]:…”
Section: Fuzzy Logicmentioning
confidence: 99%
“…Neurons can be represented as in Figure 4. unknown parameters that make a non-linear system [27]. The development of an FL follows three steps [28][29][30]:…”
Section: Artificial Neural Networkmentioning
confidence: 99%
See 1 more Smart Citation
“…The expansion of wind generation imposes significant challenges to the planning, operation, and control of power systems . Wind turbine generators (WTGs) usually operate using the maximum power point tracking (MPPT) control approach and, consequently, they do not have a power margin (or, equivalently, a power reserve) to supply load variations .…”
Section: Introductionmentioning
confidence: 99%