A Data-driven Methodology for Transient Stability Assessment Based on Broad Learning System
| Autor: | Mehrdad Ghandhari, Keyou Wang, Yuan Tian, Marina Oluic |
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| Rok vydání: | 2020 |
| Předmět: |
Generalization
Computer science 020209 energy Stability (learning theory) Boundary (topology) 02 engineering and technology Expression (mathematics) Data-driven 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Transient (computer programming) Differentiable function Transfer of learning Algorithm |
| Zdroj: | 2020 IEEE Power & Energy Society General Meeting (PESGM). |
| DOI: | 10.1109/pesgm41954.2020.9281501 |
| Popis: | This paper proposes a data-driven methodology for transient stability assessment (TSA) based on constructing a transient stability boundary (TSB). Without stacking the network layers, the TSB construction algorithm makes a broad expansion in the neural nodes thereby forming a clear structure that can be theoretically analysed. While preserving a high accuracy and generalization ability, the TSB expression is clear, differentiable and therefore applicable to dynamic security constrained problems. Furthermore, a transfer learning strategy (TLS) is employed to build TSBs from a limited number of samples in a time-saving way. The possibilities of the developed method are tested via case study that uses the IEEE 39-bus test system. The case study confirmed that the introduced algorithm is highly precise and insensitive to the number of available samples/parameters. This indicates that the proposed method is effective, robust and that as such it may serve as a valuable tool of online TSA. |
| Databáze: | OpenAIRE |
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