A Decision Method for LDC Parameters of a SVR and Voltage Control Algorithm using Measurement Data of a Distribution System

2012 ◽  
Vol 132 (8) ◽  
pp. 701-708 ◽  
Author(s):  
Katsuhiro Matsuda ◽  
Takafumi Futakami ◽  
Kazuhiro Horikoshi ◽  
Toshiyuki Seto ◽  
Masahiro Watanabe ◽  
...  
Keyword(s):  
Distribution System ◽  
Control Algorithm ◽  
Measurement Data ◽  
Voltage Control ◽  
Decision Method

Voltage Control using Small Batteries in Distribution System with Large Amount of PVs

2013 ◽  
Vol 133 (5) ◽  
pp. 439-448 ◽  
Author(s):  
Shinya Sekizaki ◽  
Mutsumi Aoki ◽  
Hiroyuki Ukai ◽  
Takaya Shigetou ◽  
Shunsuke Sasaki
Keyword(s):  
Distribution System ◽  
Voltage Control

scholarly journals Proposal of an Adaptive Neurofuzzy System to Control Flow Power in Distributed Generation Systems

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Helbert Eduardo Espitia ◽  
Iván Machón-González ◽  
Hilario López-García ◽  
Guzmán Díaz
Keyword(s):  
Statistical Analysis ◽  
Distributed Generation ◽  
Distribution System ◽  
Voltage Control ◽  
Control Flow ◽  
The Other ◽  
Charge Variation ◽  
Flow Power ◽  
Dynamics Identification ◽  
Neurofuzzy Systems

Systems of distributed generation have shown to be a remarkable alternative to a rational use of energy. Nevertheless, the proper functioning of them still manifests a range of challenges, including both the adequate energy dispatch depending on the variability of consumption and the interaction between generators. This paper describes the implementation of an adaptive neurofuzzy system for voltage control, regarding the changes observed in the consumption within the distribution system. The proposed design employs two neurofuzzy systems, one for the plant dynamics identification and the other for control purposes. This focus optimizes the controller using the model achieved through the identification of the plant, whose changes are produced by charge variation; consequently, this process is adaptively performed. The results show the performance of the adaptive neurofuzzy system via statistical analysis.

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