DocumentCode
2370052
Title
Notice of Violation of IEEE Publication Principles
Application of ANFIS in approximating the security boundary constraint for optimal power flow
Author
Gang Xu ; Zilei Wang ; Jianhong Tang
Author_Institution
Sch. of Electr. & Electron. Eng., North China Electr. Power Univ., Beijing, China
fYear
2012
fDate
18-25 May 2012
Firstpage
282
Lastpage
286
Abstract
Notice of Violation of IEEE Publication Principles
"Application of ANFIS in Approximating the Security Boundary Constraint for Optimal Power Flow"
by Gang Xu, Zilei Wang, and Jianhong Tang
in the Proceedings of the 2012 11th International Conference on Environment and Electrical Engineering (EEEIC), pp. 282 - 286
After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE\´s Publication Principles.
This paper contains portions of text and figures that were copied from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.
"Neural-Network Security-Boundary Constrained Optimal Power Flow"
by Victor J. Gutierrez-Martinez, Claudio A. Ca??izares, Claudio R. Fuerte-Esquivel, Alejandro Pizano-Martinez, and Xueping Gu,
in IEEE Transactions on Power Systems, Vol 26, No 1, February 2011
This paper proposes a new approach to use adaptive neuro-fuzzy inference systems (ANFIS) for approximating the system security boundary (SB) to a differentiable function. The novelty of the proposal is that the representation of SB, which is generated by ANFIS, is used as the security constraint of an optimal power flow (OPF). The combination of the differentiable function and the constraint of the OPF will make the system reach its best economic benefit under the basic require of system security. The effectiveness of the proposed ANFIS method, as well as the feasibility of using the approach to analyze a layering and zoning network, is demonstrated through testing and simulation using the IEEE 118 bus test system. Results show that the proposed method can be used to the dispatch optimization in layering and zoning system, and is a better approach in speed and precision.
"Application of ANFIS in Approximating the Security Boundary Constraint for Optimal Power Flow"
by Gang Xu, Zilei Wang, and Jianhong Tang
in the Proceedings of the 2012 11th International Conference on Environment and Electrical Engineering (EEEIC), pp. 282 - 286
After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE\´s Publication Principles.
This paper contains portions of text and figures that were copied from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.
"Neural-Network Security-Boundary Constrained Optimal Power Flow"
by Victor J. Gutierrez-Martinez, Claudio A. Ca??izares, Claudio R. Fuerte-Esquivel, Alejandro Pizano-Martinez, and Xueping Gu,
in IEEE Transactions on Power Systems, Vol 26, No 1, February 2011
This paper proposes a new approach to use adaptive neuro-fuzzy inference systems (ANFIS) for approximating the system security boundary (SB) to a differentiable function. The novelty of the proposal is that the representation of SB, which is generated by ANFIS, is used as the security constraint of an optimal power flow (OPF). The combination of the differentiable function and the constraint of the OPF will make the system reach its best economic benefit under the basic require of system security. The effectiveness of the proposed ANFIS method, as well as the feasibility of using the approach to analyze a layering and zoning network, is demonstrated through testing and simulation using the IEEE 118 bus test system. Results show that the proposed method can be used to the dispatch optimization in layering and zoning system, and is a better approach in speed and precision.
Keywords
IEEE standards; approximation theory; fuzzy reasoning; load dispatching; load flow; power engineering computing; power system economics; power system security; ANFIS application; IEEE 118 bus test system; OPF; SB constraint approximation; adaptive neurofuzzy inference system application; differentiable function; dispatch optimization; layering network analysis; optimal power flow; security boundary constraint approximation; zoning network analysis; Load flow; Load modeling; Mathematical model; Notice of Violation; Numerical stability; Power system stability; Security; Stability analysis; Adaptive neuro-fuzzy inference system; layering and zoning; optimal power flow; security boundary;
fLanguage
English
Publisher
ieee
Conference_Titel
Environment and Electrical Engineering (EEEIC), 2012 11th International Conference on
Conference_Location
Venice
Print_ISBN
978-1-4577-1830-4
Type
conf
DOI
10.1109/EEEIC.2012.6221589
Filename
6221589
Link To Document