To address the problem of lacking a global perspective in traditional methods for network resource adjustment and optimization, a resource allocation method for a power communication network is studied based on the operation situation in this article. Firstly, a power communication network situational awareness model is constructed. Secondly, a network resource model, a network business model, and a situational evaluation model are built. Finally, a power communication network resource allocation method based on a genetic algorithm is proposed, which adjusts the weights of network links to change data forwarding strategies and thus adjusts the situational values of each node, achieving resource allocation and dynamic optimization of the power communication network. Finally, through simulation experiments, it is demonstrated that the proposed method improves network situational awareness by 17%.
The rapid development of the energy internet, the deep integration of the power system and information system, and the emergence of various new services in the distribution network have led to an explosion of service data. Traditional routing optimization methods are inadequate in satisfying the efficiency and reliability requirements of the power communication network for data transmission. This paper presents a collaborative optimization method for enhancing the traffic in the power communication network based on segmentation learning. Firstly, we propose a power service data transmission routing architecture in EPCN, where multiple routes between the source and destination nodes are available. Secondly, a segmentation learning-based traffic collaboration optimization algorithm for EPCN is proposed, which divides the traffic to explore the transmission performance of multiple routes within one optimization, thereby reducing data congestion at critical nodes. Finally, simulation results demonstrate that the proposed algorithm outperforms in terms of data transmission delay and routing optimization speed.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.