Back to Search
Start Over
Explicit Data-Driven Small-Signal Stability Constrained Optimal Power Flow.
- Source :
-
IEEE Transactions on Power Systems . Sep2022, Vol. 37 Issue 5, p3726-3737. 12p. - Publication Year :
- 2022
-
Abstract
- This paper proposes a data-driven small-signal stability constrained optimal power flow (SSSC-OPF) method with high computational efficiency. Instead of repeating the computational expense small-signal stability analysis via differential and algebraic equations during the iterative OPF process, a computationally cheap surrogate constraint for small-signal stability is developed. To reduce the learning difficulty for small-signal stability boundaries, an efficient sample generation strategy is proposed with sampling space compression. This allows us to use the support vector machine (SVM) with a kernel function to derive the explicit data-driven surrogate constraint for small-signal stability. Penalty factor optimization is proposed to compensate for the error caused by SVM. The learned small-signal stability constraint is embedded into the OPF model for generator control. An examination strategy is also developed to avoid the small-signal instability of re-dispatch caused by the error of the data-driven surrogate model. Comparison results with other model-based and data-driven methods on the IEEE 39-bus and 118-bus systems demonstrate the high computational efficiency and economic benefits of the proposed method. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 08858950
- Volume :
- 37
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Power Systems
- Publication Type :
- Academic Journal
- Accession number :
- 158649790
- Full Text :
- https://doi.org/10.1109/TPWRS.2021.3135657