The smart grid (SG) is an integration of a traditional power grid with advanced information and communication infrastructure for a large number of electrical applications. Despite all these advantages that the SG will bring, certain issues arise when designing a high-quality SG communication network. One of the critical challenges is that the existing routing strategies in smart power grids are incapable of guaranteeing differentiated QoS requirements considering the network dynamics. To address this limitation, we propose an SDN routing algorithm called the QoS-guaranteed and congestion-controlled OpenFlow routing strategy (QCORS) to satisfy the various communication demands by utilizing the flexibility of SDN. Gaining from its open and programmable idea in SDN, the proposed strategy is expected to divide the link into different congestion levels based on predicting the future congestion status from transmission links. Then packets are expected to be transmitted to routers through links under lower load conditions. The simulation results have demonstrated that the proposed method can reduce the average peer-to-peer delay of all the vocational flow and guarantee the reliability of the network.

Damage detection is important for the maintenance of automated machines. General non-destructive testing techniques require static equipment and complex analysis processes, which restricts the maintenance of automated machines. Therefore, this paper proposes an acoustic emission (AE) tomography method for detecting cavity damage in automated machines, combining the fast sweeping method (FSM) and the limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method. This approach overcomes the limitations of real-time AE detection for cavity damage in continuous and homogeneous materials. The proposed method has been applied in numerical and laboratory experiments to validate its feasibility. The results show that the inversed low-velocity regions correspond to the actual cavity regions, and the sources of cavity damage can be effectively detected. This paper provides a new perspective for AE testing technologies, and also lays the foundation for other non-destructive testing techniques, in terms of cavity damage detection.