التفاصيل البيبلوغرافية
| العنوان: |
A Machine Learning Approach to Low System Strength Grid Identification for Large Scale Power Systems. |
| المؤلفون: |
Clark, Angel, Yang Zhang, Huang, Shun Hsien (Fred), Le Xie |
| المصدر: |
International Workshop on Large-Scale Integration of Wind Power into Power Systems as well as on Transmission Networks for Offshore Wind Plants; 2018, p1-5, 5p |
| مصطلحات موضوعية: |
ELECTRIC power distribution grids, MACHINE learning, ELECTRIC inverters, RANDOM forest algorithms, SHORT circuits |
| مستخلص: |
This research proposes a screening method to identify low system strength, i.e. weak grid with low short circuit current level, portions of power systems where potential voltage instabilities could occur in a system with high penetration of inverter-based resources (IBRs), like wind and solar. The proposed method uses a random forest algorithm by clustering the transmission buses with interconnected IBRs based on features extracted from the short circuit current and electrical distance. This proposed screening method can be used as a screening tool to identify the system strength based on short circuit current and connected IBRs' capacity without computationally intensive dynamic simulations. Therefore, the impact of system outages and different system scenarios can quickly be analyzed, with the resulting areas identified and quantified in each scenario. The developed method was applied to an ERCOT case under a developed long-term system condition and a Synthetic Texas Network, demonstrating the robustness of the screening tool and ability to identify the areas with low system strength challenges. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
