A Novel Online Estimation Scheme for Static Voltage Stability Margin Based on Relationships Exploration in a Large Data Set

Fan, Youping; Liu, Songkai; Qin, Libin; Li, Huimin; Qiu, Huimin

doi:10.1109/tpwrs.2014.2349531

Cited by 52 publications

(35 citation statements)

References 19 publications

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“…Except for ANN, other learning techniques such as decision tree (DT) [93,94,95] , random forest [96], support vector machine (SVM) [97], bagging [98] and linear regression [99,100] However, most of these implicity or explicit formulations of VSM are computationally intricate and therefore rarely used in the context of online VSM control. The practical methods in this category are usually based on the linear formulation (approximation) of VSM, i.e., using VSM sensitivity [110,111].…”

Section: Methods Based On Voltage Stability Regionmentioning

confidence: 99%

“…The recent work [98] admits the locality of underlying model in the topological sense, but the looking-up scheme it uses for topology matching actually gives up learning the topology from data, and consequently cannot directly deal with the unseen ones. Although its model updating procedure empowers the method with adaptability for network topology, but the seamless real-time prediction hardly can be achieved due to the simulation and retraining process for unseen topology.…”

Section: Limitations Of Mlrm and Other Methodsmentioning

confidence: 99%

“…Although its model updating procedure empowers the method with adaptability for network topology, but the seamless real-time prediction hardly can be achieved due to the simulation and retraining process for unseen topology. Besides, [98] did not report its nonlinear basis function family, for which we can only find the examples with cubic polynomial and sinusoidal function restricted within one half period. In this case, the method is essentially an additive polynomial (roughly) model trained by bagging-like approach using heuristic weights.…”

Section: Limitations Of Mlrm and Other Methodsmentioning

confidence: 99%

“…the MLRM approach we published previously in [100]. Some qualitative comparisons to other approaches can be found in [100,98].…”

Section: Examplesmentioning

confidence: 99%

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Online monitoring and control of voltage stability margin via machine learning-based adaptive approaches

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Section: Methods Based On Voltage Stability Regionmentioning

confidence: 99%

Section: Limitations Of Mlrm and Other Methodsmentioning

confidence: 99%

Section: Limitations Of Mlrm and Other Methodsmentioning

confidence: 99%

“…the MLRM approach we published previously in [100]. Some qualitative comparisons to other approaches can be found in [100,98].…”

Section: Examplesmentioning

confidence: 99%

See 2 more Smart Citations

Online monitoring and control of voltage stability margin via machine learning-based adaptive approaches

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“…The computational simplicity of these methods makes them suitable for real time applications, but the accuracy of these methods is restricted by the information obtained from a single location [61][62][63][64][65][66][67][68][69][70]. New strategies are proposed to monitor and control the voltage stability of smart grid system [71][72]. In these works, the physical constraints of the grid were not included.…”

Section: Voltage Stability Monitoring and Control Using Pmu Technmentioning

confidence: 99%

On-line voltage stability monitoring and control in smart grid — A survey

Sahu

Verma

2015

2015 IEEE UP Section Conference on Electrical Computer and Electronics (UPCON)

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Most of the work on voltage stability assessment of power systems has concentrated on offline studies. With advancement in Phasor Measurement Unit (PMU) technology, it is quite possible to assess voltage stability margin of real time systems in smart grid. This paper has addressed on different works going on in the area of voltage stability monitoring and control using PMU technology. The report in this paper has elaborated on different methodologies which have been proposed in literature for optimal placement of PMUs for control of voltage stability margin in smart grid. The paper sets new scope for promising researchers for the best utilization of PMUs in online voltage stability monitoring and control in smart grid architecture.

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Online estimation of voltage stability margin via deep neural network with consideration of the local structures in power grid

Bai

Tan

et al. 2020

Int Trans Electr Energ Syst

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Summary Objective Online estimation of voltage stability margin (VSM) is critical to the long‐term safety and stable operation of power system. In this paper, a deep neural network (DNN)‐based model that incorporates the intrinsic grid topology information is proposed to achieve accurate VSM estimation. Methods The node embedding information together with the electrical parameter measurements are combined together into an array of local structures as the input feature vector. A local‐global deep neural network (LGDNN) model is proposed to extract and integrate the local information into high‐level global representation for VSM estimation. The Node2vec algorithm is employed to obtain embedded node vectors and a NodeRank algorithm is designed to form local structures, which are composed of the measurements of a fixed number of similar nodes according to the similarity among their embedded vectors. A sequential DNN with cascade‐connected depthwise separable 1D convolutional layer and fully connected layer is trained to estimate the current VSM. Results The model performance is validated on the IEEE‐39 and IEEE‐118 benchmark systems under normal and post‐contingency situations. Experiment results indicate that the proposed model can achieve VSM estimation with high precision that surpasses seven mainstream approaches in multiple estimation error indices. Conclusion Through the integration of local structures, the proposed LGDNN increases the estimation accuracy with relatively few parameters than various DNN‐based models. Thus, it is an practical model for online power grid voltage stability monitoring.

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A Novel Online Estimation Scheme for Static Voltage Stability Margin Based on Relationships Exploration in a Large Data Set

Cited by 52 publications

References 19 publications

Online monitoring and control of voltage stability margin via machine learning-based adaptive approaches

Online monitoring and control of voltage stability margin via machine learning-based adaptive approaches

On-line voltage stability monitoring and control in smart grid — A survey

Online estimation of voltage stability margin via deep neural network with consideration of the local structures in power grid

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