Fuzzy logic in voltage and reactive power control in power systems

Ekel, Petr; Terra, L.D.B.; Junges, M.F.D.; Oliveira, F.J.A. de; Kowaltschuk, R.; Taguti, T.Y.

doi:10.1109/cca.1999.806724

Cited by 9 publications

(7 citation statements)

References 6 publications

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“…In particular, it is possible to solve problem (23) with satisfying (26) or problem (25) with satisfying (27). For instance, the algorithms [3,4] utilized to solve the 381 problems of discrete optimization (23), (26) and (25), (27) are based on modifying the generalized methods of discrete optimization [28].…”

Section: Optimization Problems With Fuzzy Coefficientsmentioning

confidence: 99%

“…(b) The model of minimization (25) with the constraints (24) approximated by (27). When using this approach, which is of a universal character, solutions dominated by the initial objective function are cut off from above as well as from below to the greatest degree [3,4].…”

Section: Modelling Of Fuzzy Preference Relations and Its Applicationsmentioning

confidence: 99%

“…It is rational to construct the sensitivity models [25] on the basis of experimental design techniques [26]. In particular, varying the above indicated generation magnitudes on two levels…”

Section: Multicriteria Power System Operationmentioning

confidence: 99%

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Models and methods of decision making in fuzzy environment and their applications to power engineering problems

Canha

Ekel

Queiroz

et al. 2007

Numerical Linear Algebra App

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SUMMARYResults of research into the use of fuzzy sets for handling various forms of uncertainty in optimization problems are presented. Two types of situations which need the application of a multicriteria approach are classified. According to this, two classes of models ( X, M and X, R models) are considered with utilizing the Bellman-Zadeh approach to decision making in a fuzzy environment and techniques for modelling of fuzzy preference relations for their analysis. The application of the Bellman-Zadeh approach for solving X, M problems conforms to the principle of guaranteed result and provides constructive lines in obtaining harmonious solutions on the basis of analysing associated maxmin problems. Analysis of X, R models is considered as part of a general approach to solving problems with fuzzy coefficients. Three techniques for processing of fuzzy preferences relations are considered. The first technique is associated with building and analysing a membership function of a subset of non-dominated alternatives considering all criteria simultaneously. The second technique is of a lexicographic character and consists in step-by-step consideration of criteria. The third technique is based on aggregating membership functions of subsets of non-dominated alternatives for each criterion. The results of the paper are universally applicable and are already being used to solve power engineering problems. It is illustrated by considering problems of multicriteria power and energy shortage allocation, multicriteria power system operation, and substation planning with considering criteria of quantitative as well as of qualitative character.

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Section: Optimization Problems With Fuzzy Coefficientsmentioning

confidence: 99%

Section: Modelling Of Fuzzy Preference Relations and Its Applicationsmentioning

confidence: 99%

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Models and methods of decision making in fuzzy environment and their applications to power engineering problems

Canha

Ekel

Queiroz

et al. 2007

Numerical Linear Algebra App

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“…It is rational to construct the sensitivity models [9] on the basis of experimental design techniques [14]. In particular, varying the above indicated generation magnitudes on two levels x i , i = 1, .…”

Section: Multicriteria Power System Operationmentioning

confidence: 99%

Decision making in a fuzzy environment and its application to multicriteria power engineering problems

Ekel

Menezes

Neto

2007

Nonlinear Analysis: Hybrid Systems

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“…..,J (reflecting the power flow change on line k ) and [S S kj ], k =1,...,K, j =1,...,J (reflecting the reactive power flow change on line k allowing the estimation of loss increments [5]), their building is possible on the basis of the same computing experiments, which are necessary to obtain voltage sensitivities.…”

Section: Construction Of Sensitivity Indicesmentioning

confidence: 99%

Fuzzy Logic Based Control of Voltage and Reactive Power in Subtransmission System

Braga

Carnevalli

Ekel

et al. 2005

Lecture Notes in Computer Science

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This paper presents results of research related to the second stage of the project dedicated to developing and employing the xOMNI system (software of the SCADA type). This stage is associated with the elaboration of the Energy Management System (EMS) for the subtransmission system of the Minas Gerais State Energy Company. One of the most important EMS functions is voltage and reactive power control. Its implementation is based on the use of fuzzy logic technology. Taking this into account, the paper covers the consideration of questions of building diverse types of sensitivity indices and forming production rules providing comprehensive and flexible solutions in conditions of regulated and deregulated environments. Some aspects of rational carrying out fuzzy logic procedures are considered as well.

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Fuzzy logic in voltage and reactive power control in power systems

Cited by 9 publications

References 6 publications

Models and methods of decision making in fuzzy environment and their applications to power engineering problems

Models and methods of decision making in fuzzy environment and their applications to power engineering problems

Decision making in a fuzzy environment and its application to multicriteria power engineering problems

Fuzzy Logic Based Control of Voltage and Reactive Power in Subtransmission System

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