2014
DOI: 10.1080/00207217.2014.966781
|View full text |Cite
|
Sign up to set email alerts
|

Intelligent voltage control strategy for three-phase UPS inverters with output LC filter

Abstract: This paper presents a supervisory fuzzy neural network control (SFNNC) method for a three-phase inverter of uninterruptible power supplies (UPSs). The proposed voltage controller is comprised of a fuzzy neural network control (FNNC) term and a supervisory control term. The FNNC term is deliberately employed to estimate the uncertain terms, and the supervisory control term is designed based on the sliding mode technique to stabilise the system dynamic errors. To improve the learning capability, the FNNC term in… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

0
5
0

Year Published

2018
2018
2023
2023

Publication Types

Select...
7

Relationship

0
7

Authors

Journals

citations
Cited by 11 publications
(5 citation statements)
references
References 21 publications
0
5
0
Order By: Relevance
“…The authors used fuzzy models to obtain quantitative estimates of environmental initiation projects in air transportation [18]. The authors implement supervised fuzzy neural network control (SFNNC) to control the three-phase inverter of a UPS [19]. Zheng et al used FNN to solve control problems for complex robotic systems with uncertainty and disturbances [20].…”
Section: And Online Interactivementioning
confidence: 99%
“…The authors used fuzzy models to obtain quantitative estimates of environmental initiation projects in air transportation [18]. The authors implement supervised fuzzy neural network control (SFNNC) to control the three-phase inverter of a UPS [19]. Zheng et al used FNN to solve control problems for complex robotic systems with uncertainty and disturbances [20].…”
Section: And Online Interactivementioning
confidence: 99%
“…Nevertheless, this complex technique is suitable for VSCs operating with lowswitching frequencies where the possible resonances with the power system must be damped. Other sophisticated control techniques such as predictive control [19] or neural networks [20] have been reported. Despite the good performance of these proposals, it has to be considered that the controller tuning relies on some auxiliary processes, i.e., the selection of adequate weighting factors of the cost function for the predictive controllers or training stages in the case of neural networks.…”
Section: Introductionmentioning
confidence: 99%
“…However, the design of the droop coefficient at each harmonic frequency is more complex and the computational burden of this method is heavy. In [12], a fuzzy neural network based control method is proposed for the standalone inverter. Nevertheless, the learning rate parameters in this method are difficult to tune.…”
Section: Introductionmentioning
confidence: 99%