Wearable electronic sensing devices are deemed to be a crucial technology of smart personal electronics. Strain and pressure sensors, one of the most popular research directions in recent years, are the key components of smart and flexible electronics. Graphene, as an advanced nanomaterial, exerts pre-eminent characteristics including high electrical conductivity, excellent mechanical properties, and flexibility. The above advantages of graphene provide great potential for applications in mechatronics, robotics, automation, human-machine interaction, etc.: graphene with diverse structures and leverages, strain and pressure sensors with new functionalities. Herein, the recent progress in graphene-based strain and pressure sensors is presented. The sensing materials are classified into four structures including 0D fullerene, 1D fiber, 2D film, and 3D porous structures. Different structures of graphene-based strain and pressure sensors provide various properties and multifunctions in crucial parameters such as sensitivity, linearity, and hysteresis. The recent and potential applications for graphene-based sensors are also discussed, especially in the field of human motion detection. Finally, the perspectives of graphene-based strain and pressure sensors used in human motion detection combined with artificial intelligence are surveyed. Challenges such as the biocompatibility, integration, and additivity of the sensors are discussed as well.

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Gas-insulated switchgear (GIS) is widely used across multiple electric stages and different power grid levels. However, the threat from several inevitable faults in the GIS system surrounds us for the safety of electricity use. In order to improve the evaluation ability of GIS system safety, we propose an efficient strategy by using machine learning to conduct SF6 decomposed components analysis (DCA) for further diagnosing discharge fault types in GIS. Note that the empirical probability function of different faults fitted by the Arrhenius chemical reaction model has been investigated into the robust feature engineering for machine learning based GIS diagnosing model. Six machine learning algorithms were used to establish models for the severity of discharge fault and main insulation defects, where identification algorithms were trained by learning the collection dataset composing the concentration of the different gas types (SO2, SOF2, SO2F2, CF4, and CO2, etc.) in the system and their ratios. Notably, multiple discharge fault types coexisting in GIS can be effectively identified based on a probability model. This work would provide a great insight into the development of evaluation and optimization on solving discharge fault in GIS.

The expansion of the power network and integration of wind farms pose challenges in the short-circuit current (SCC) problem. Transmission switching performs better in both flexibility and effectiveness compared with other SCC restriction measures, while the power grid security would be threatened as the number of switched-off-lines increases. This paper proposes a day-ahead scheduling model considering commitment of units and N-1 criterion, to avoid the excessive SCC problem caused by wind farms integration. Specially, the SCC calculation model of the grid-connected wind farms is put forward to aggregate wind farms and calculate SCC. A novel SCC formulation considering transmission switching, commitment of units as well as wind farms integration is deduced and converted to SCC constraints. The SCC constrained mixed-integer linear programming (MILP) based day-ahead scheduling model is proposed, which minimizes system operation cost and transmission switching cost. In addition, the N-1 security requirement is considered in the proposed day-ahead scheduling model to ensure system security by avoiding switching off too many lines. Numerical results of a modified IEEE 30-bus system with two wind farms illustrate effectiveness of the proposed model.INDEX TERMS Short-circuit current, wind farm integration, transmission switching, day-ahead scheduling, mixed-integer linear programming NOMENCLATURE Major symbols and notations used throughout the paper are defined below, while others are defined following their first appearances as needed. Indices: l,g,wIndices of lines, thermal generating units and wind farms. t,n,c,dIndices of hours, buses, contingency statuses and loads. Variables: c g Marginal operation cost of unit g. c lOperation cost of switching line l. I g,t , I w,t