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Probabilistic Power Flow Computation via Low-Rank and Sparse Tensor Recovery
- Publication Year :
- 2015
- Publisher :
- arXiv, 2015.
-
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 $9\times 10^{20}$.<br />Comment: 8 pages, 10 figures, submitted to IEEE Trans. Power Systems
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....7420f18a46e18b01fe295264fc464a14
- Full Text :
- https://doi.org/10.48550/arxiv.1508.02489