Name
Abstract | ||
|---|---|---|
This paper presents a tensor-recovery method to solve probabilistic power flow problems. Our approach generates a high-dimensional and sparse generalized polynomial-chaos expansion that provides useful statistical information. The result can also speed up other essential routines in power systems (e.g., stochastic planning, operations and controls). Instead of simulating a power flow equation at all quadrature points, our approach only simulates an extremely small subset of samples. We suggest a model to exploit the underlying low-rank and sparse structure of high-dimensional simulation data arrays, making our technique applicable to power systems with many random parameters. We also present a numerical method to solve the resulting nonlinear optimization problem. Our algorithm is implemented in MATLAB and is verified by several benchmarks in MATPOWER $5.1$. Accurate results are obtained for power systems with up to $50$ independent random parameters, with a speedup factor up to $9\times 10^{20}$. |
Year | Venue | Field |
|---|---|---|
2015 | CoRR | MATLAB,Tensor,Computer science,Artificial intelligence,Probabilistic logic,Speedup,Computation,Mathematical optimization,Algorithm,Electric power system,Quadrature (mathematics),Numerical analysis,Machine learning |
DocType | Volume | Citations |
Journal | abs/1508.02489 | 1 |
PageRank | References | Authors |
0.36 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zheng Zhang | 1 | 125 | 12.54 |
| Hung D. Nguyen | 2 | 10 | 3.01 |
| Konstantin S. Turitsyn | 3 | 62 | 14.72 |
| Luca Daniel | 4 | 497 | 50.96 |