The high penetration of distributed energy resources (DERs) into distribution grids and increasing extreme events bring security challenges into grids, while accurate grid model is difficult to be obtained due to the widespread of DERs and unknown extreme events. This paper proposes a combined data driven and model based identification method to develop power system digital twin (DT), which achieves the identification of the topology structure and line parameters of distribution grids even under unbalanced conditions. Moreover, a DT based multi-step critical load restoration scheme is established to improve distribution system resilience after extreme events. At last, the proposed method is validated on the three phase unbalanced IEEE-123 node system to verify its effectiveness.

The oily particles produced by aerosolization of the Metalworking fluids (MWFs) pose a threat to human health. To quantify the transmission of oily particles for developing mitigation strategy, the possible particle volatilization, adhesion and coagulation during air transmission should be determined. Therefore, this study firstly by measured the particle size distributions at and away from the emission source in the laboratory. Then, to further verify the results obtained in the laboratory, the particle size distributions of oily particles in a machining workshop were measured. Meanwhile, to better understand the source characteristic of oily particles, this investigation measured the water content of the oily particles in the machining workshop because such a parameter could affect the removal performance of oily particles by filtration. The results revealed that the particle size distributions of oily particles at different locations were similar regardless of the laboratory measurement or on-site measurement. Thus, the evolution of particle size distribution of oily particles during air transmission could be ignored. Besides, the oily particles in the air had a water content of 22.6 % when the MWFs with a water content of 95 % was used during turning process. Although the oily particles in the air contained a certain amount of water, they were difficult to volatilize. The oily particles in the air might mainly consist of pure oily particles and water-in-oil particles. The results in this study could provide guidance for developing better control strategies of oily particles in machining workshops.

The oily particles pose a threat to human health. To quantify the transmission of oily particles for developing mitigation strategy, the possible particle volatilization, adhesion and coagulation during air transmission and the water content of oily particles should be determined. Therefore, the particle size distributions at and away from the emission source were measured in the laboratory. Then, to further verify the results obtained in the laboratory, the particle size distributions of oily particles in a machining workshop were measured. Meanwhile, this investigation measured the water content of the oily particles in the machining workshop. The results revealed the evolution of particle size distribution of oily particles during air transmission could be ignored. The oily particles in the air had a water content of 22.6% and they were difficult to volatilize. The oily particles in the air might mainly consist of pure oily particles and water-in-oil particles.

The integration of inverter-based renewable energy sources (RESs) like PVs into distribution grids brings severe voltage violation issues due to high stochastic uncertainties. Existing approaches give less consideration to unbalanced scenarios and utilize model-based Volt-Var Control (VVC) with higher complexity and computational problems. Deep reinforcement learning (DRL) methods are effective in dealing with uncertainties, but it is difficult to guarantee secure constraints in existing works. To address the above challenges, a safe DRL-based VVC strategy with a modified Twin Delayed DDPG (TD3) algorithm is applied to achieve voltage control in unbalanced systems utilizing inverter-based PVs. The proposed method is tested in a modified IEEE 13-bus unbalanced system, ensuring 100% voltage safety and minimizing power losses simultaneously.

Local exhaust system is an effective but energy-consuming method for capturing oil mist particles in machining workshops. To reduce the flow resistance of an exhaust system for minimal fan energy consumption, the method of applying individually shape-optimized exhaust hoods, namely mass-production design, is feasible. However, the combined effect of multiple exhaust hoods in an exhaust system may not be optimal in reducing the flow resistance. This investigation thus firstly validated the shape optimization of an individual exhaust hood by a discrete adjoint method. The discrete adjoint method could adjust the shape of an exhaust hood automatically in the direction of reducing the flow resistance. The design variables were the coordinates of wall boundaries of the exhaust hood. The validation used measured data from a small-scale experiment. This study then applied the validated discrete adjoint method to conduct customized design through the shape optimization of multiple exhaust hoods simultaneously in the exhaust system. The flow resistance under customized design was compared with the method of mass-production design. The results revealed that the customized design led to different shapes of individual exhaust hoods and they were different from the shape of the individually optimized exhaust hood. The flow resistance of the exhaust system under customized design was reduced by 57%. However, only 36.5% reduction in flow resistance was achieved when the mass-production design method was employed. The customized design method was more effective in reducing flow resistance of the exhaust system.