DNN-based Contingency Screening Module for Voltage Stability analysis

Ibrahim, Tamer; Mohamed, Ahmed

doi:10.1109/naps46351.2019.9000276

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…However, there are two shortcomings in this method [7,8]: firstly, the CPF calculation is too complex to fulfill online operation; secondly, the method, as a global index, cannot provide the specific information of nodes or regions. The development of machine learning has solved the problems of traditional methods [9][10][11][12][13][14]. In the Literature [9], deep neural network (DNN) is applied to the online predictive contingency screening of power systems.…”

Section: Introductionmentioning

confidence: 99%

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PSO-SVR-based voltage stability online monitoring method for large-scale power grids

Hou

et al. 2022

J. Phys.: Conf. Ser.

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With the power load increasing, the issue of voltage stability and voltage collapse is getting greater attention. Online monitoring of grid voltage stability is therefore a necessary requirement. In this paper, a PSO-SVR-based voltage stability online monitoring method for large-scale power grid is proposed. Initially, the load impedance modulus margin (LIMM) method is used to identify weak nodes in the system and thereby determine the best installation locations of the phase measurement units (PMUs). Afterwards, a particle swarm optimization modified support vector regression (PSO-SVR) algorithm is trained by using the state variables of the weak nodes where the PMUs are installed as input features and the calculated LIMM values as output features. In this way, the PSO-SVR model learns the non-linear relationship between the operating state of the power system and the corresponding LIMM values. The LIMM values of the nodes are obtained from the respective state-variables. The method proposed in this paper not only accurately assesses the stable voltage level of the load node, but also avoids the problems of parameter drift and misjudgement that can occur in the original LIMM method. Therefore, it has significantly advantages for online applications. Lastly, it is tested on an IEEE 118 system to verify the effectiveness and accuracy of the proposed method in this paper.

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Section: Introductionmentioning

confidence: 99%

“…The development of machine learning has solved the problems of traditional methods [9][10][11][12][13][14]. In the Literature [9], deep neural network (DNN) is applied to the online predictive contingency screening of power systems. DNN is able to scan all contingencies quickly and analyse unstable operational nodes in depth.…”

Section: Introductionmentioning

confidence: 99%