2014 Sixth International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) (2014)
July 13, 2014 to July 15, 2014
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/PAAP.2014.28
With the increasingly complex power system, wide area protection, using global data obtained from different substations through communications, has been a hot research topic for some time. However, the overall transmission of large amounts of data will cause communication network congestion, which will lead to delay and loss of data. Therefore building an algorithm which can make use of a reduced number of global data to identify the fault area is very useful. This paper proposes a down-sampling matrix to reduce the original data. For example, a protection system requiring 240 feature points of voltage data, if using the down-sampling matrix, will need only a minimum of 24 points, and still has a high probability to identify the fault zone. Simulation results show that when the data size M > 0.3, the result of classifying adjacent bus fault point is credible (greater than 60%), and when the data size M > 0.05, the result of classifying the non-adjacent bus fault point is credible (greater than 72%).
Training, Simulation, Fault diagnosis, Substations, Sparse matrices, Mathematical model, Equations
B. Li, J. He, T. Yip and J. Li, "Wide Area Power System Fault Detection Using Compressed Sensing to Reduce the WAN Data Traffic," 2014 Sixth International Symposium on Parallel Architectures, Algorithms and Programming (PAAP), Beijing, China, 2014, pp. 40-45.