Notice of Violation of IEEE Publication Principles Security-Boundary Constrained Optimal Power Flow Based on Adaptive Neuro-Fuzzy Inference Systems

Notice of Violation of IEEE Publication Principles

"Security-Boundary Constrained Optimal Power Flow Based on Adaptive Neuro-Fuzzy Inference Systems"
by Changhui Ma, Zilei Wang, Xinsheng Niu, and Lei Zhang
in the Proceedings of the 2012 Asia-Pacific Power and Energy Engineering Conference (APPEEC)

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 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

In optimal power flow problems, the determination of constraints plays a vital role to ensure the stability and security of a power system. This paper proposes a new approach to construct security- boundary constrained optimal power flow (SBC-OPF) model based on adaptive neuro-fuzzy inference systems (ANFIS) representation of the system security boundary (SB). In the stage of determining the OPF constraint, a closed form, differentiable function derived from the system\´s SB by ANFIS is used to represent security constraints in a OPF model, thereby solving the OPF model. The effectiveness and feasibility of the proposed ANFIS method is demonstrated through testing and simulation using the IEEE two-area benchmark system. Results show that the ANFIS method provides brand- new thought for SBC-OPF analysis, and is feasible and efficient.