Research on line overload identification of power system based on improved neural network algorithm. (20th July 2020)

• Record Type: Journal Article
• Authors: Yang, Lin; Luo, Zhiming; Lin, Wangqing; Li, Shaozi
• Is Part Of: Concurrency and computation. Volume 32:Number 24(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-07-20
• Subjects: BP neural network -- genetic algorithm -- overload -- power system; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5933
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Due to the continuous appearance of safety fault accidents in the practice process, operation safety has become the central task of various operation and management tasks of the power grid. Therefore, to establish a line overload identification and data control model for the power system, we first defined the vulnerability of complex power systems based on the analysis of each line and node. For finding the optimal parameters of this model, we proposed an improved optimization strategy by combining the genetic algorithm and BP neural network. To verified the effectiveness of our proposed method, we conducted experiments on a simulation on the IEEE 30‐node power system environment. Experimental results demonstrate that the proposed algorithms can establish an optimized overload identification model with better performance. This study can help to conduct reasonable adjustment when overload happens to the power system, and then reduce similar failure as well as enhance the operation safety.