Routing Algorithm for Power Communication Networks Based on Serivce Differentiated Transmission Requirements

doi:10.11930/j.issn.1004-9649.202405020

Abstract

Abstract:

The electric power communication network, pivotal in ensuring the stable operation of the power grid, is tasked with transmitting control instructions and collecting status data. Addressing the intelligent routing challenge within the electric power communication network under multiple constraints, we propose an innovative routing algorithm that seamlessly integrates Message Passing Neural Network (MPNN) with deep reinforcement learning algorithms. Implemented through the TensorFlow framework, this algorithm has been rigorously validated in a simulation environment constructed using OpenAI Gym. After undergoing over 8,000 training iterations, the algorithm demonstrates remarkable performance enhancements, outperforming traditional shortest path and load balancing algorithms in terms of routing selection capabilities. Furthermore, it has exhibited robust adaptability and resilience in generalization tests on new topology maps and link failure simulation experiments.

Key words: power communication network, routing optimization, message passing neural network, deep reinforcement learning, multiple constraints

Songping XUE, Dequan GAO, Ziyan ZHAO, Yuqian LIN, Zejing GUANG, Dawei ZHANG. Routing Algorithm for Power Communication Networks Based on Serivce Differentiated Transmission Requirements[J]. Electric Power, 2024, 57(11): 183-190.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202405020

https://www.electricpower.com.cn/EN/Y2024/V57/I11/183

Figures/Tables 21

Fig.1 Topology of 500 kV backbone network in Fujian Province

Table 1 Normalized reward for the three services

业务名称		奖励值
Ⅰ类生产控制业务		0.125
Ⅱ类生产控制业务		0.500
Ⅲ类生产控制业务		1.000

Fig.2 Example of service transmission

Table 2 Reliability requirements for different services

业务名称		可靠性要求
Ⅰ类生产控制业务		0.90
Ⅱ类生产控制业务		0.88
Ⅲ类生产控制业务		0.86

Table 3 Transmission distance limitations for different services

业务名称		传输距离限制/km
Ⅰ类生产控制业务		1000
Ⅱ类生产控制业务		950
Ⅲ类生产控制业务		900

Fig.3 Agent interacting with the power communication network

Fig.4 Architecture of message passing neural network

Fig.5 Algorithm 1 message passing neural network

Fig.6 Workflow of deep reinforcement learning algorithm

Fig.8 Loss curve

Table 4 Table of hyperparameters during training

超参数名称		超参数值
链路隐藏状态link_state_dim		32
学习率		0.0006
消息传递次数T		4
读出层层数readout_layers		3
读出层神经元个数readout_units		35
一次读出的数据个数batch_size		32
丢弃层丢弃概率dropout_rate		0.01

Fig.7 Algorithm 2 Deep Reinforcement Learning Algorithm

Fig.9 Score curve

Fig.10 Algorithm evaluation in the initial environment

Fig.11 Nsfnet topology

Fig.12 Algorithms evaluation under Nsfnet

Fig.13 Random topology

Fig.14 Algorithm evaluation under random topology

Fig.15 Algorithm evaluation without failures

Fig.16 Algorithm evaluation under one link failure

Fig.17 Algorithm evaluation under two link failures

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