Research on Power Grid Information Model Based on Artificial Intelligence

Pei, Yinghui; Zhang, Haohai; Gu, Xinqiao; Wang, Hao

doi:10.1109/iccnea.2019.00067

Cited by 5 publications

(1 citation statement)

References 6 publications

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“…Por otro lado, se ha comprobado que la resistencia a estos fallos y eventos se relaciona fuertemente con la topología (estructura) de la red de potencia [4]. Lo que ha motivado el uso de la teoría de grafos en el estudio de este sistema [5]. La estructura topológica de una red de potencia, junto con sus características de comportamiento, como el cumplimiento de los límites operativos de voltaje y el monitoreo de sobrecargas de la red, puede definir la robustez frente a determinadas perturbaciones.…”

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Critical scenarios identification in power system simulations using graph measures and machine learning

Reyes

Salgueiro

Rivera

et al. 2021

2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON)

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It is essential that electrical power systems are constructed with a reliable and resilient infrastructure. The evaluation of convergence scenarios of the load flow is a technique widely used to study the reliability of energy systems. This paper considers the classification of convergence scenarios under different loading and power generation conditions. Scenarios where the solution is not converging are evaluated using machine learning algorithms. A data set is built from power system topological representation and the simulation of load flows. Algorithms including Support Vector Machine, K-Nearest-Neighbor, and Decision Trees are evaluated and compared. The trained models can be used as a step in the contingency analysis process to be able reduce the computational time and effort in the execution of load flow calculations.

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