Multi-objective Particle Swarm Optimization Algorithm for Feeder Capacity Planning of Distribution Network Considering Dynamic Load

The optimization proportion of distribution network feeder capacity cannot reach the expected standard, so this paper proposes a multi-objective Particle swarm optimization algorithm for distribution network feeder capacity considering dynamic load. The multi-objective method is used to expand the planning scope, the multi-objective Particle swarm optimization feeder capacity planning matrix is set, and the feeder capacity optimization planning calculation model under dynamic load is designed. The experimental results show that the final optimization rate of the feeder capacity in the distribution network can reach over 5, indicating that the designed directional planning algorithm has high measurement accuracy, strong pertinence, and controllable error under dynamic load conditions and has practical application value.

I. Introduction

The setting and planning of feeder capacity is an important factor affecting the operation of the distribution network and daily stable power supply [1–2]. The so-called dynamic load mainly refers to a kind of power characteristics taken into account [3] when the active power and reactive power of the power load in the system fluctuate with the voltage and frequency and when the system voltage and frequency change rapidly. In this context, the current optimal planning and calculation structure of distribution network feeder capacity can be further expanded, and a more flexible and changeable calculation form can be gradually formed. A specific multiobjective particle swarm optimization matrix algorithm is designed using feeder remote monitoring. According to the network topology and the actual feeder capacity optimization standard, the recognition coverage of the modulation matrix can achieve the corresponding normalization processing [4] under specific constraints. In addition, because the actual computation of multi-objective particle swarm optimization planning for feeder capacity is large and the corresponding processing time is also long when designing algorithms, it is necessary to access a feeder terminal unit (FTU) in a controllable program, strengthen the current planning form, and assist in anomaly location and to plan through the matrix and normalized processing calculation in this way. It can better solve the capacity planning of multi-power supply and multi-fault feeders, realize the balance adjustment and optimization of the distribution network, enhance the actual power supply performance, and lay the foundation for the innovation and development of subsequent related technologies.