Research on line overload identification of power system based on improved neural network algorithm

Abstract

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. K E Y W O R D S BP neural network, genetic algorithm, overload, power system 1 INTRODUCTION The power system is a combination of network space, physical space, and social space. Large-scale grid fault accidents will cause severe safety loopholes. Therefore, the operation safety of the power grid is an essential issue in the development of modern society. In practice, most power grid accidents originate from line overload of the power system, component failure, and similar problems. 1 To reduce such events, power grid enterprises should not only enhance daily maintenance but also need to improve the identification and survival ability of the power grid in emergencies. 2 With the development of information and communication technology, the Internet of Things has been extensively applied to the power grid, and the differential privacy protection policy can effectively protect the data security of power grid transfer. 3 Meanwhile, it would be better to detect corresponding important nodes and vulnerable lines and to reinforce the maintenance in advance before safety accidents happen, which can reduce economic losses and social disruption. 4 Many power safety management problems in power grid system can be converted into optimization problems through mathematical modeling. Some are multi-objective optimization problems, and high-performance computing at a faster speed is needed to solve these problems. 5 Line overload identification is still a difficulty for the safety of the power system. Therefore, we proposed an improved back propagation (BP) neural network based on the genetic algorithm (GA) to tackle the line overload problem in the power grid system. 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 optimizing this model, we take the advantages of combining the genetic algorithm and BP neural network. It is worth noting that the original BP neural network usually has the problem of s...