Counting-Based Reliability Estimation for Power-Transmission Grids
Authors: Leonardo Duenas-Osorio, Kuldeep Meel, Roger Paredes, Moshe Vardi
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Since the primary objective of this project was to compute connectivity reliability of power transmission grid networks across different cities in U.S., we compared the effectiveness of Rel Net vis-a-vis state of the art techniques. Specifically, we sought to answer the following questions: 1. How does the runtime performance of Rel Net compare to that of the state-of-the art techniques on real world power transmission networks? 2. How do estimates computed by Rel Net compare to the exact estimates of reliability for networks that could be handled by exact techniques? |
| Researcher Affiliation | Academia | Leonardo Duenas-Osorio Department of Civil and Environmental Engineering Rice University Kuldeep S. Meel Department of Computer Science Rice University Roger Paredes Department of Civil and Environmental Engineering Rice University Moshe Y. Vardi Department of Computer Science Rice University |
| Pseudocode | No | The paper describes the steps of Rel Net (Step 1, Step 2, Step 3) in a textual format but does not present them as formally structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | Yes | The raw network data was obtained in GIS format from the Platts repository for maps and geospatial data 1. 1http://www.platts.com/products/gis-data. |
| Dataset Splits | No | The paper mentions using 10 power-transmission networks as benchmarks and sets parameters for Approx MC2 (ε = 0.8 and δ = 0.2). However, it does not specify any explicit train, validation, or test dataset splits in terms of percentages or sample counts for the networks or the data used in their reliability estimation process. |
| Hardware Specification | Yes | Each node of the cluster had a 12-core 2.83 GHz Intel Xeon processor, with 4GB of main memory, and each experiment was run on a single core. |
| Software Dependencies | No | The paper states, 'We implemented a Python prototype of Rel Net, which invokes Approx MC2 to perform counting over Σ1 1 formulas...' and mentions using 'a specialized SAT solver (Crypto Mini SAT (Soos, Nohl, and Castelluccia 2009))'. However, it does not provide specific version numbers for Python, Rel Net, Approx MC2, or Crypto Mini SAT. |
| Experiment Setup | Yes | For all our experiments, we used ε = 0.8 and δ = 0.2 as parameters for Approx MC2, which is consistent with previously reported studies of using hashing-based counting techniques. The timeout for each experiment was set to 1,000 seconds. |