| Title | Static Security Analysis of Source-Side High Uncertainty Power Grid Based on Deep Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Qian, Tiantian, Yang, Shengchun, Wang, Shenghe, Pan, Dong, Geng, Jian, Wang, Ke |
| Conference Name | 2021 China International Conference on Electricity Distribution (CICED) |
| Keywords | Analytical models, composability, Deep Learning, high uncertainty, Human Behavior, Neural networks, power grids, pubcrawl, renewable energy, renewable energy sources, resilience, Resiliency, Safety, static analysis, static security, Uncertainty |
| Abstract | As a large amount of renewable energy is injected into the power grid, the source side of the power grid becomes extremely uncertain. Traditional static safety analysis methods based on pure physical models can no longer quickly and reliably give analysis results. Therefore, this paper proposes a deep learning-based static security analytical method. First, the static security assessment index of the power grid under the N-1 principle is proposed. Secondly, a neural network model and its input and output data for static safety analysis problems are designed. Finally, the validity of the proposed method was verified by IEEE grid data. Experiments show that the proposed method can quickly and accurately give the static security analysis results of the source-side high uncertainty grid. |
| DOI | 10.1109/CICED50259.2021.9556780 |
| Citation Key | qian_static_2021 |
Static Security Analysis of Source-Side High Uncertainty Power Grid Based on Deep Learning