Resilience Of Interdependent Networks Research Articles

An influential node identification method considering multi-attribute decision fusion and dependency

It is essential to study the robustness and centrality of interdependent networks for building reliable interdependent systems. Here, we consider a nonlinear load-capacity cascading failure model on interdependent networks, where the initial load distribution is not random, as usually assumed, but determined by the influence of each node in the interdependent network. The node influence is measured by an automated entropy-weighted multi-attribute algorithm that takes into account both different centrality measures of nodes and the interdependence of node pairs, then averaging for not only the node itself but also its nearest neighbors and next-nearest neighbors. The resilience of interdependent networks under such a more practical and accurate setting is thoroughly investigated for various network parameters, as well as how nodes from different layers are coupled and the corresponding coupling strength. The results thereby can help better monitoring interdependent systems.

Enhancing resilience of interdependent networks against cascading failures under preferential recovery strategies

Interdependent networks are extremely fragile because a very small node failure in one network would trigger a cascade of failures in the entire system. Therefore, the resilience of interdependent networks is always a critical issue studied by researchers in different fields. Existing studies mainly focused on protecting several influential nodes for enhancing robustness of interdependent networks before the networks suffer random failures. In reality, it is necessary to repair a failing interdependent network in time to prevent it from suffering total breakdown. Recent investigations introduce a failure-recovery model for studying the concurrent failure process and recovery process of interdependent networks based on a random recovery strategy. This stochastic strategy covers repairing a small fraction of mutual boundary nodes which are the failed neighbors of the giant connected component of each network, with a random probability of recovery . Obviously, the random recovery is simple and straightforward. Here, we analyze the recovery process of interdependent networks with two types of connectivity links, i.e., the first-type connectivity links and the second-type connectivity links, which represent the mutual boundary nodes(being also failed nodes) linked to survival nodes in current giant connected component, and linked to failed nodes out of current giant connected component in networks, respectively. We find that when mutual boundary nodes have more first-type connectivity links, the current giant connected component has higher average degree and immediately makes better interdependent network resilience, on the other hand, more second-type connectivity links generate more candidates during the recovery procedure, and indirectly make better system resilience. In short, two types of connectivity links of mutual boundary nodes both have great effects on the resilience of interdependent networks during the recovery. In this paper, we propose a new recovery strategy (preferential recovery based on connectivity link, or PRCL) to identify the mutual boundary node recovery influence in interdependent networks, based on the failure-recovery model. By defining two indexes that represent the numbers of first-type and links second-type connectivity links, respectively, we calculate the boundary influence with one parameter f by combining together with two indexes. After calculating all boundary nodes in the current process, we obtain a boundary importance index which is more accurate to indicate recovery influence of boundary node for each boundary node in interdependent networks. Our strategy is applied to interdependent networks constructed by ER random network or/and scale-free network with the same average degree. And a dynamical model of random failure based on percolation theory is used to make a comparison of performance between PRCL and other recovery strategies(including random recovery, preferential recovery based on degree, preferential recovery based on local centrality) in terms of four quantitative indices, i.e., probability of existence of the giant connected component, number of iteration steps, recovery robustness and average degree of the steady state of the giant connected component. Experiments on different interdependent networks (ER-ER/SF-SF/ER-SF/SF-ER) demonstrate that with a very small number of mutual boundary node recoveries by PRCL strategy, the resilience and robustness of entire system under the recovery process can be greatly enhanced. Finally, the only parameter f in PRCL strategy is also discussed. Our strategy is meaningful in practice as it can largely enhance interdependent network resilience and contribute to the decrease of system breakdown risk.

Effective restoration strategies of interdependent power system and communication network

The power system is continuously evolving towards a smarter grid by adding intelligent electrical devices that leverage communication technologies to provide visualisation, control and automation. The integration of power system and communication network has brought challenges into the resilience of interdependent networks. Following an outage or blackout, system operators cannot only restore one network while simply ignoring the interconnection with the other network. Effective restoration strategies that involve co-ordination between power system and communication network have become critical in enhancing the resilience. An optimisation-based restoration strategy is developed to coordinate the restoration actions between two networks. The problem is modelled as a mixed integer linear programming problem, with the objective of activating every node in both networks with the minimum number of activation/energisation of branches. The developed strategies are tested on the modified IEEE 14-bus system incorporating communication network. Simulation results demonstrate that the coordinated restoration strategies are effective and critical in the successful recovery of interdependent networks. The developed model could also provide insights in network design and planning to enhance the resilience of interdependent power system and communication network.