An Affine Arithmetic-Based Methodology for Reliable Power Flow Analysis in the Presence of Data Uncertainty

By Alfredo Vaccaro, Claudio A. Cañizares, D. Villacci. IEEE Transactions on Power Systems, 2010

174 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/622da6cbd1788bdbb990686763630d920a218660

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Rui Bo
2. Kankar Bhattacharya
3. Fangxing Li
4. Alfredo Vaccaro
5. Claudio A. Cañizares
6. Mehrdad Pirnia
7. Hongbin Sun
8. Qinglai Guo

In Statistics and Probability

1. Kankar Bhattacharya
2. Mehrdad Pirnia
3. Alfredo Vaccaro
4. Claudio A. Cañizares
5. Tao Ding

Citations in the corpus

• A Novel Affine Arithmetic Method to Solve Optimal Power Flow Problems With Uncertainties (2014, methodology): “Therefore, the noise magnitudes can be obtained as follows [15]:”
• Interval Power Flow Analysis Using Linear Relaxation and Optimality-Based Bounds Tightening (OBBT) Methods (2015, background): “Therefore, affine arithmetic (AA), which is introduced in [16] and [17] as an improvement of IA, has been proposed to take into account the correlation between uncertain operands and sub-formulas involved in the interval calculation.”
• A Range Arithmetic-Based Optimization Model for Power Flow Analysis Under Interval Uncertainty (2013, methodology): “Based on the MCP formulation of the power flow (2)–(10), this problem is basically transformed into solving the following interval optimization problem assuming interval uncertainty in the input powers (generators and/or loads) as in [15]:”