Transient Stability Assessment of Power Systems Based on Graph Convolutional Neural Networks

Dongfeng XUE; Fang YAO; Fushuan WEN; Xinyu ZHANG; Wenquan YUE

doi:10.16513/j.2096-2185.DE.2308505

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Distributed Energy ›› 2023, Vol. 8 ›› Issue (5) : 36-43. DOI: 10.16513/j.2096-2185.DE.2308505

Basic Research

Transient Stability Assessment of Power Systems Based on Graph Convolutional Neural Networks

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Abstract

With the rapid development of emerging technologies such as high penetration of new energy, ultra-high voltage transmission interconnection and power grid digital intelligence, the structure of power system has become more complex and diverse, which puts forward various requirements for the safe operation and stability of the power system. Based on this, a transient stability analysis based on graph convolutional network model is given, which combines short-time simulation and neural network prediction to reduce the cycle time required for transient stability analysis. The method is applicable to a wide range of simulation analysis scenarios. Meanwhile, the agent model of graph convolutional network is combined with genetic algorithm to complete the solution of preventive control optimization strategy quickly and stably in transient stability for the contingency set. Compared with the traditional transient stability analysis, the method proposed in this paper requires shorter running time and is highly efficient. The test results based on the IEEE-30 and IEEE-39 node systems verify the applicability, efficiency and superiority of the proposed method.

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new energy / graph convolutional network / contingency set / transient stability

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Dongfeng XUE , Fang YAO , Fushuan WEN , et al . Transient Stability Assessment of Power Systems Based on Graph Convolutional Neural Networks[J]. Distributed Energy Resources. 2023, 8(5): 36-43 https://doi.org/10.16513/j.2096-2185.DE.2308505

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