The Transient Stability Preventive Control of Power System Based on RBF Neural Network

Abstract:

The transient stability preventive estimation and control is one of the most important tasks of the power system. The traditional time domain simulation method cannot meet the standard of on-line estimation because of its heavy computation burden. In this paper, we realize the on-line estimation of transient stability limits on critical lines of a power system using the favorable approximation ability of RBF network. We choose right samples by off-line count to train the RBF network in order to make the error satisfy demand. Preventive control direction and amount are determined based on first-order sensitivities of transient stability limits to generator outputs. The sensitivities are derived from partial derivatives of RBF network outputs to inputs. In the end, we take a practical power system for an example to demonstrate the efficiency of RBF network in estimation of the transient stability limit on critical lines and ability to provide preventive control strategy.

• Periodical: Advanced Materials Research (Volumes 121-122)
• Pages: 887-892
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.121-122.887
• Online since: June 2010
• Authors: Lan Lan Yu, Bo Xue Tan, Tian Xing Meng
• Keywords: Preventive Control, RBF Network, Transient Stability Limit

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