Semimonthly

ISSN 1000-1026

CN 32-1180/TP

+Advanced Search 中文版
Optimization Method for Artificial Phase Sequence Based on Load Forecasting and Non-dominated Sorting Genetic Algorithm
Author:
Affiliation:

1.Anhui Provincial Laboratory of Renewable Energy Utilization and Energy Saving (Hefei University of Technology), Hefei 230009, China;2.Electric Power Research Institute of State Grid Anhui Electric Power Co., Ltd., Hefei 230601, China;3.State Grid Anhui Electric Power Co., Ltd., Hefei 230061, China

Abstract:

It is found that adjusting the access phase sequence of the load can effectively reduce the line loss and three-phase load unbalance during the process of saving energy and reducing loss in 0.4 kV distribution network. This paper proposes an artificial phase sequence optimization method based on load forecasting and non-dominated sorting genetic algorithm (NSGA2). Firstly, the user load model is established by using the outlet current curve substitution method for distribution network. Secondly, based on the historical data, Elman neural network is used to predict the daily electricity consumption of each user and three-phase outlet current in distribution network on the day of phase modulation. Then, a multi-objective phase sequence optimization mathematical model of distribution network is established based on prediction data, taking the minimum line loss and the least number of phase modulation as the objective function. The NSGA2 is applied to solve the model to obtain the optimized phase sequence of each load. Finally, the effectiveness of the proposed method is verified by comparing the theoretical line loss before and after phase sequence adjustment in one distribution network of Anhui power grid of China.

Keywords:

Foundation:

This work is supported by Regional Innovation and Development Joint Fund of National Natural Science Foundation of China (No. U19A20106).

Get Citation
[1]HAN Pingping, PAN Wei, ZHANG Nan, et al. Optimization Method for Artificial Phase Sequence Based on Load Forecasting and Non-dominated Sorting Genetic Algorithm[J]. Automation of Electric Power Systems,2020,44(20):71-78. DOI:10.7500/AEPS20200313003
Copy
Share
History
  • Received:March 13,2020
  • Revised:July 26,2020
  • Adopted:
  • Online: October 16,2020
  • Published: