Power System Loading Margin Estimation Using a Neuro-Fuzzy Approach

Torres, Santiago P.; Peralta, W. H.; Castro, Carlos A.

doi:10.1109/tpwrs.2007.907380

Cited by 28 publications

(13 citation statements)

References 10 publications

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“…[24][25][26][27][28][29][30] Torre et al proposed a new methodology for the loading margin estimation based on a subtractive clustering and adaptive neuro-fuzzy inference system. 31 Also, various voltage stability indices are selected as the inputs of intelligent system to be used in real-time environments with an uncertain load distribution. In addition, for static voltage stability limits by means of different training criteria and algorithms, a multilayer feed-forward neural network has been designed to estimate the power margin.…”

Section: Discussionmentioning

confidence: 99%

“…Also, these applications can be linked to an intelligent system that can utilize the accumulated knowledge from the previous calculations, or approximate the security region boundary using rules set by experts for online security assessment . Torre et al proposed a new methodology for the loading margin estimation based on a subtractive clustering and adaptive neuro‐fuzzy inference system . Also, various voltage stability indices are selected as the inputs of intelligent system to be used in real‐time environments with an uncertain load distribution.…”

Section: Introductionmentioning

confidence: 99%

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Reconstruction of security region with high information content using effective attributes extraction

Javan

Mashhadi

2016

Int. Trans. Electr. Energ. Syst.

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Summary Security region‐based power system operation guides operators in a fast and relevant decision‐making process as to what conditions require operators' actions. The key to this approach is the manner in which effective attributes are extracted among the network's operating variables. This paper reconstructs the security region of power system with high information content. The security region of power system is constructed by means of the static security boundaries such as static voltage stability limit, generator output power limit, and transformer and transmission line thermal limits by increasing loading factor along various directions until these limits are violated. Then, the security region is described by means of effective features set that contain maximum information content. In this paper, nonlinear feature extraction techniques have been proposed to dimensionality reduction and relevant extraction of power system operating variables. Convex and nonconvex techniques of feature extraction are analyzed based on a mutual information index and generalization error. The simulation results on the IEEE 9‐bus and the IEEE 118‐bus test systems demonstrate the effectiveness of the proposed method for describing the security region by considering high information content.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reconstruction of security region with high information content using effective attributes extraction

Javan

Mashhadi

2016

Int. Trans. Electr. Energ. Syst.

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“…Therefore, simpler fuzzy inference system (FIS) models with few fuzzy rules can be obtained by using this technique even with the problems having a considerable number of inputs. This model is composed of significant features expedient for fast computation time [25]. An in-depth discussion regarding this algorithm can be procured from [48].…”

Section: Anfis Modelmentioning

confidence: 99%

The Amalgamation of SVR and ANFIS Models with Synchronized Phasor Measurements for On-Line Voltage Stability Assessment

et al. 2017

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This paper presents the application of support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS) models that are amalgamated with synchronized phasor measurements for on-line voltage stability assessment. As the performance of SVR model extremely depends on the good selection of its parameters, the recently developed ant lion optimizer (ALO) is adapted to seek for the SVR's optimal parameters. In particular, the input vector of ALO-SVR and ANFIS soft computing models is provided in the form of voltage magnitudes provided by the phasor measurement units (PMUs). In order to investigate the effectiveness of ALO-SVR and ANFIS models towards performing the on-line voltage stability assessment, in-depth analyses on the results have been carried out on the IEEE 30-bus and IEEE 118-bus test systems considering different topologies and operating conditions. Two statistical performance criteria of root mean square error (RMSE) and correlation coefficient (R) were considered as metrics to further assess both of the modeling performances in contrast with the power flow equations. The results have demonstrated that the ALO-SVR model is able to predict the voltage stability margin with greater accuracy compared to the ANFIS model.

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“…In [10] and [11], the authors propose estimating load margin by using voltage stability indices with the application of ANFIS. Finally, an enhancement of [10] and [11] is proposed in [12], which takes into account a quasirandom load direction, and considers base case and contingency situations based on subtractive clustering and ANFIS.…”

Section: Introductionmentioning

confidence: 99%

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

Ma¹,

Wang²,

Niu³

et al. 2012

2012 Asia-Pacific Power and Energy Engineering Conference

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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.

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Power System Loading Margin Estimation Using a Neuro-Fuzzy Approach

Cited by 28 publications

References 10 publications

Reconstruction of security region with high information content using effective attributes extraction

Reconstruction of security region with high information content using effective attributes extraction

The Amalgamation of SVR and ANFIS Models with Synchronized Phasor Measurements for On-Line Voltage Stability Assessment

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

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