Reasonable repair crew dispatch can accelerate the service restoration in distribution systems after some components are damaged. It is worthy noting that as traditional distribution systems are involving into cyber-physical distribution systems composed of physical distribution networks and cyber networks, damaged components in these two networks should be coordinately repaired to achieve maximal restoration performance. However, repair crews in physical distribution networks are scheduled without considering the impact of the corresponding cyber networks in existing repair crew dispatch strategies. In this paper, a coordinated model is proposed to co-optimize the repair crew schedule of the two networks. Besides the cyber-physical interdependence between the two networks, backup power systems, topology reconfiguration and intentional distribution generator (DG) island are also modelled to improve the repair performance. The proposed model is formulated as a mixed-integer linear programming model, and its effectiveness is verified on the modified IEEE 33-node and 123-node test systems. Numerical results highlight the necessity of co-optimizing repair crew dispatch in both physical distribution networks and cyber networks.

This article provides a framework for modeling and analysis of spatial cyber-physical power systems (CPPSs). Previous studies on structural vulnerability analysis of CPPS mainly focused on network topology without any spatial information. In this article, we first propose a hierarchical information system evolution model for spatial power grids. Then, a spatial CPPS model is established by combining power and information systems that accounts for overlapped power and information lines. Finally, the structural vulnerability under global and localized failures is investigated based on direct current (dc) power flow. Under global failures, different types of components have distinguishing effects, and the power-node failure is the most serious case. For localized failures, key zones can be identified by the cascading failure scale, and similarly, different failure modes affect different areas. Furthermore, considering power system scenarios with only defenders and those with both attackers and defenders, we investigate two resource allocation situations. For defenders, increased resources and appropriately dispersed allocation can work effectively. However, in the attack–defense scenario, the perfect subgame equilibrium suggests that both attackers and defenders must concentrate all their resources on the most vulnerable zone. These findings may establish a foundation for future efforts to model and protect spatial CPPSs.

Hydrophobicity is an important parameter to characterize electrical properties of insulated materials. Therefore, it is an urgent task to develop on-line instruments to identify the hydrophobicity of insulated material's surface conveniently, quickly and accurately. For this purpose, a novel evaluation system with image processing and decision tree is proposed which is based on embedded platform. For obtaining satisfactory results, we first propose a mixed image segmentation method to overcome the complex conditions outside, concerning non-controlled illumination, nonstandard surfaces and unfixed shooting angle. Then we adopt four new characteristic parameters to describe the image of each sample. Finally, a classification method based on MultiBoost decision tree is conducted which synthesizes the merits of both AdaBoost and Wagging algorithm. Results indicate the procedures can be applied in the DSP (Digital Signal Processor) platform perfectly and better results can be obtained than those did in our previous study or that of some other research.