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| Data-driven frequency security assessment based on generative adversarial networks and metric learning |
| DOI：10.19783/j.cnki.pspc.231542 |
| Key Words:frequency security machine learning data-driven generative adversarial network metric learning |
| Author Name | Affiliation | | LI Huarui1 | 1. State Grid Jiangsu Electric Power Co., Ltd. Electric Power Research Institute, Nanjing 211100, China
2. State Key Laboratory
of Power Grid Safety and Energy Conservation (China Electric Power Research Institute), Beijing 100192, China | | LI Wenbo1 | 1. State Grid Jiangsu Electric Power Co., Ltd. Electric Power Research Institute, Nanjing 211100, China
2. State Key Laboratory
of Power Grid Safety and Energy Conservation (China Electric Power Research Institute), Beijing 100192, China | | LI Zheng1 | 1. State Grid Jiangsu Electric Power Co., Ltd. Electric Power Research Institute, Nanjing 211100, China
2. State Key Laboratory
of Power Grid Safety and Energy Conservation (China Electric Power Research Institute), Beijing 100192, China | | JIA Yuqiao1 | 1. State Grid Jiangsu Electric Power Co., Ltd. Electric Power Research Institute, Nanjing 211100, China
2. State Key Laboratory
of Power Grid Safety and Energy Conservation (China Electric Power Research Institute), Beijing 100192, China | | LIU Quan1 | 1. State Grid Jiangsu Electric Power Co., Ltd. Electric Power Research Institute, Nanjing 211100, China
2. State Key Laboratory
of Power Grid Safety and Energy Conservation (China Electric Power Research Institute), Beijing 100192, China | | MIAO Deyang1 | 1. State Grid Jiangsu Electric Power Co., Ltd. Electric Power Research Institute, Nanjing 211100, China
2. State Key Laboratory
of Power Grid Safety and Energy Conservation (China Electric Power Research Institute), Beijing 100192, China | | LI Yaran1 | 1. State Grid Jiangsu Electric Power Co., Ltd. Electric Power Research Institute, Nanjing 211100, China
2. State Key Laboratory
of Power Grid Safety and Energy Conservation (China Electric Power Research Institute), Beijing 100192, China | | WANG Baocai2 | 1. State Grid Jiangsu Electric Power Co., Ltd. Electric Power Research Institute, Nanjing 211100, China
2. State Key Laboratory
of Power Grid Safety and Energy Conservation (China Electric Power Research Institute), Beijing 100192, China |
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| Abstract:With the construction of large-capacity long-distance high-voltage direct current transmission projects and the large-scale integration of renewable energy, frequency security of the power system is facing severe challenges. For fast and accurate online assessment of frequency security, a data-driven model based on a metric learning (ML) and generative adversarial network (GAN) is proposed. First, the key frequency security indicators are selected as the outputs of the model, and an input feature set is constructed. Then, the improved Wasserstein generative adversarial network (WGAN) based on the Wasserstein distance metric is used to learn the distribution information of historical operation scenarios of power systems. This generates operational scenarios covering typical modes to build the training sample set. Considering the inapplicability of a single machine learning model to frequency security assessment with complicated operational modes of power systems, a combined assessment model for assessment composed of multiple sub-models is constructed based on metric learning for a kernel regression (MLKR) method. Finally, a simplified Shandong power system example is used to verify the effectiveness of the proposed method. |
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