Edge Computing Regulation Optimization Technology Based on Power Internet of Things

Abstract

Abstract In the long-term operation of the power grid, local faults sometimes occur, which are mainly caused by excessive load of users, excessive power consumption, insufficient reactive power compensation and other reasons, resulting in low node voltage and large system loss. To solve this problem, based on the edge computing of power Internet of Things, the regulation optimization technology is studied. In this paper, firstly, the development status of edge computing in power Internet of Things is summarized, the importance of reactive power compensation in power grid under edge computing environment is introduced, and the application of improved particle swarm optimization (GPSO) algorithm in reactive power compensation in power grid is studied. GPSO algorithm is used to compensate the system with local faults, and the compensation effect is compared with that of particle swarm optimization algorithm and genetic algorithm. Then, the power flow calculation and reactive power compensation of the power grid are analyzed theoretically, and the reactive power loss, reactive power compensation device and reactive power compensation mode of the power grid are studied, and some mathematical models and practical working methods are summarized. In addition, according to an actual network example in a certain area, particle swarm optimization, genetic algorithm and GPSO algorithm are used to calculate the reactive power compensation of the power grid, and the reactive power compensation equipment is connected to the power grid reasonably, thus increasing the node voltage and reducing the network loss. Finally, by comparing the grid node voltages of the three algorithms under edge computing conditions, it is proved that GPSO algorithm has improved the power Internet of Things, that is, it is a good regulation and optimization technology to compensate the reactive power of the grid by using GPSO algorithm.