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Transient Stability Assessment of Power Systems Based on CLV-GAN and I-ECOC

Author

Listed:
  • Nan Li

    (Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin 132012, China)

  • Jiafei Wu

    (Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin 132012, China)

  • Lili Shan

    (School of Science and Engineering, Tonghua Open University, Tonghua 134001, China)

  • Luan Yi

    (State Grid Corporation Siping Power Supply Company, Siping 136000, China)

Abstract

In order to improve the multi-class assessment performance of transient stability in power systems, a multi-class assessment model that combines the CLV-GAN algorithm with an improved error-correcting output coding technique is proposed in the paper. To address the issue of the small number of unstable samples in power systems, a sample generation model is constructed by combining a dual-encoder VAE with a GAN network. The model generates effective artificial samples to balance the sample ratio between categories by learning the latent distribution of aperiodic and oscillatory unstable samples from the distribution. The decomposition method based on an improved error-correcting output coding algorithm is applied to convert the multi-class problem into a decision fusion issue for binary models. This method improves the overall performance of the multi-class model, particularly significantly increasing the recognition accuracy of discrimination against oscillatory unstable samples and reducing the safety hazards in the operation of power systems. The simulation validation was conducted on the IEEE 39-bus and IEEE 140-bus systems to confirm the effectiveness of the proposed model.

Suggested Citation

  • Nan Li & Jiafei Wu & Lili Shan & Luan Yi, 2024. "Transient Stability Assessment of Power Systems Based on CLV-GAN and I-ECOC," Energies, MDPI, vol. 17(10), pages 1-18, May.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:10:p:2278-:d:1391035
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    References listed on IDEAS

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    1. Petar Sarajcev & Antonijo Kunac & Goran Petrovic & Marin Despalatovic, 2022. "Artificial Intelligence Techniques for Power System Transient Stability Assessment," Energies, MDPI, vol. 15(2), pages 1-21, January.
    2. Shi, Zhongtuo & Yao, Wei & Zeng, Lingkang & Wen, Jianfeng & Fang, Jiakun & Ai, Xiaomeng & Wen, Jinyu, 2020. "Convolutional neural network-based power system transient stability assessment and instability mode prediction," Applied Energy, Elsevier, vol. 263(C).
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