چكيده به لاتين
Current power systems can be considered one of the major contributors to environmental impacts, including greenhouse gas emissions and global warming, as they rely on fossil fuels, particularly coal. Therefore, the introduction of smart power grids has become a necessity, as these networks facilitate bidirectional communication between network components, reducing the need for centralized energy production and large power plants while contributing to more efficient energy management. Additionally, the bidirectional communication systems in smart grids create an advanced, automated, and energy-efficient power delivery network. Due to their vulnerability to extreme events, smart grids require effective communication systems to maintain electricity services. Network failures can cause significant damage and disrupt communications, leading to a considerable reduction in services. Various factors can cause network failures, with link failures between network compo- nents being the most common. To operate autonomously and independently of human intervention, a smart grid must have a flexible communication infrastructure capable of detecting and repairing any link failure as quickly as possible. A software-defined networking (SDN)-based approach enables this capability. SDN facilitates dynamic management and rapid response to failures by providing centralized control and separating the data and control layers. This technology accelerates rerouting and path recovery in case of link failures by offering centralized and optimized decision-making. Central controllers in SDN can select a new route for data flow in the event of a failure, which helps reduce recovery time and packet loss rate. In this study, we have designed a module for the SDN controller, equipped with link failure detection capabilities and automatic recovery using the Q-learning algorithm. In smart power grids, the packet loss rate is critically important, as any packet loss can lead to reduced service quality and a lack of essential information required for system control in critical situations, thereby diminishing the network’s ability to respond to instabilities. The main focus of this research is on reducing the packet loss rate, as date loss has a detrimental impact on the stability and efficiency of the network. focus of this research is on reducing packet loss rate, as data loss has a damaging effect on the stability and efficiency of the network. When comparing the proposed method with the reference paper, the packet loss rate, recovery time, and algorithm execution time in the German topology were reduced by 62.66%, 99.85%, and 99.91%, respectively. Similarly, in the U.S. topology, these values decreased by 50.90%, 99.76%, and 99.98%, respectively. Additionally, compared to the normal mode, the packet loss rate in the German topology was reduced by an average of 95.67%, and in the U.S. topology by 15.13%.
