Open AccessDistribution Network Reconfiguration Based on NoisyNet Deep Q-Learning Network
Author(s) -
Beibei Wang,
Hong Zhu,
Honghua Xu,
Yuqing Bao,
Huifang Di
Publication year - 2021
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2021.3089625
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
The distribution network reconfiguration (DNR) aims at minimizing the power losses and improving the voltage profile. Traditional model-based methods exactly need the network parameters to derive the optimal configuration of the distribution network. This paper proposes a DNR method based on model-free reinforcement learning (RL) approach. The proposed method adopts NoisyNet deep Q-learning network (DQN), by which the exploration can be automatically realized without need of tuning the exploration parameters, in order to accelerate the training process and improve the optimization performance. The proposed method is validated by the simulation results.