AI approach to optimal VAr control with fuzzy reactive loads

Abdul-Rahman, K.H.; Shahidehpour, Mohammad; Daneshdoost, M.

doi:10.1109/59.373931

Cited by 54 publications

(11 citation statements)

References 27 publications

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“…; J. As it was indicated above, the construction of sensitivities in [2][3][4][5][6][7][8][9][10][11][12][13][14] is based on the use of system Jacobian matrices and encounters limitations. At the same time, the application of experimental design [16][17][18] provides a means for building adequate sensitivity models.…”

Section: Construction Of Sensitivity Indices and Their Use In Power Ementioning

confidence: 99%

“…As an example, it is possible to indicate the problem of voltage and reactive power control in power systems. The techniques for its solution related to applying optimization as well as knowledge based approaches (for instance, [2][3][4][5][6][7][8][9][10][11][12][13][14]) utilize diverse algorithmic modifications of the method [15] allowing the use of the system Jacobian matrices to build sensitivities (by the way, the solution of other power engineering problems with the use of sensitivity models is also based on applying techniques associated with utilizing Jacobian matrices [2]). However, deficient linearization accuracy permits one to use these techniques when perturbations are small (for example, discussion to [3]).…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity and functionally oriented models for power system planning, operation, and control

Ekel

Junges

Kokshenev

et al. 2013

International Journal of Electrical Power & Energy Systems

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Section: Construction Of Sensitivity Indices and Their Use In Power Ementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sensitivity and functionally oriented models for power system planning, operation, and control

Ekel

Junges

Kokshenev

et al. 2013

International Journal of Electrical Power & Energy Systems

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“…Recently, Arti cial Intelligence (AI) based methods for real and reactive power optimization using fuzzy logic and Arti cial Neural Networks (ANN) have been reported in (Abdul-Rahman and Shahidehpour, 1993), (Miranda and Saraiva, 1992) and (Abdul-Rahman et al, 1995). Fuzzy logic has been used to model the voltage constraints in (Abdul-Rahman and Shahidehpour, 1993) for RPO.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic Based Successive LP Method for Reactive Power Optimization

Venkatesh¹

1999

Electric Machines & Power Systems

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This paper presents a new Successive Fuzzy Linear Programming (SFLP) method for Reactive Power Optimization (RPO) to minimize the transmission loss and improve the voltage pro le. The SFLP method uses Fuzzy LP (FLP) model in the Successive Linear Programming (SLP) framework to solve the RPO problem. In the FLP model, the objective and each of the constraints are assigned a satisfaction parameter. The satisfaction parameter corresponding to the objective quanti es the degree of closeness of the objective in the current state to the optimum. The satisfaction parameter corresponding to a constraint describes the degree of enforcement of that constraint. By maximizing the minimum of these satisfaction parameters, the objective as well as the constraint enforcements are maximized. The SFLP method of RPO is e cient and reliable in scheduling systems having severe under-voltages and insucient reactive power, as the method is capable of maximizing the enforcement of the violated load bus voltage constraints, minimizing the transmission loss while simultaneously enforcing all the other constraints strictly. This method uses compactly stored, factorized constant matrices in all the LP steps, both for the construction of the FLP model as well as for the power ow solution. The control variables used in this method are generator bus voltage magnitudes, reactive powers of switchable VAR sources, and on-load tap changer (OLTC) settings of transformers. The method was tested on IEEE test systems and on a practical electric utility system. The merits of the proposed method compared to the SLP method using non-fuzzy approach are brought out.

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“…In this paper, the efficiency of the hybrid LMNN (HNFS) has been demonstrated for the ED problem on 6 and 13-generating unit systems taken from reference [20].…”

Section: Introductionmentioning

confidence: 99%