Virtual synchronous generators (VSG) are widely applied in microgrid technology due to their ability to withstand the impact of grid-connected inverters without inertia. The frequency deviation in the droop control of VSG has a reducing effect on the hosting capacity of the microgrid. Therefore, this article proposes a VSG-based frequency deviation-free control strategy. When the microgrid is in the island/grid connected switching operation mode, it will result in violent fluctuations of the active power on the condition that there is a reactive and fast power change. Therefore, the delay characteristic of a synchronous generator is introduced into VSG to achieve a smooth transition of reactive power and steady-state value that lowers the impact on the system. The rotational inertia is a fixed value, which may fail to meet certain control requirements of the microgrid, such as suppressing power oscillation and rapidly recovering frequency. The fixed moment of inertia is later on improved to an adaptive inertia moment that can effectively solve the problem of rapid rise and fall of frequency as well as improve the frequency response of the microgrid. Finally, the simulation model is established by MATLAB to verify the effectiveness and feasibility of the proposed control algorithm.

To operate the power grid safely and reduce the cost of power production, power-load forecasting has become an urgent issue to be addressed. Although many power load forecasting models have been proposed, most still suffer from poor model training, limitations sensitive to outliers, and overfitting of load forecasts. The limitations of current load-forecasting methods may lead to the generation of additional operating costs for the power system, and even damage the distribution and network security of the related systems. To address this issue, a new load prediction model with mixed loss functions was proposed. The model is based on Pinball–Huber’s extreme-learning machine and whale optimization algorithm. In specific, the Pinball–Huber loss, which is insensitive to outliers and largely prevents overfitting, was proposed as the objective function for extreme-learning machine (ELM) training. Based on the Pinball–Huber ELM, the whale optimization algorithm was added to improve it. At last, the effect of the proposed hybrid loss function prediction model was verified using two real power-load datasets (Nanjing and Taixing). Experimental results confirmed that the proposed hybrid loss function load prediction model can achieve satisfactory improvements on both datasets.

During experiments carried out in the fume hood, the fluidity and viscosity of the air in the fume hood cause problems such as turbulence and gas viscosity, which affect the accuracy of wind speed measurement on the fume hood. As a solution to these problems, an adaptive control system of wind speed on the fume hood based on the Kalman filter is designed. The improved Kalman filter is used to reduce the influence of turbulence and gas viscosity on the measurement of cover wind speed, and improve the measurement accuracy. The adaptive PID control algorithm is used to adjust the air valve to improve the control accuracy of cover wind speed. Experiments revealed that our proposed system for hood control outperforms traditional alternatives: it can effectively improve the measurement accuracy and control accuracy of the cover wind speed, reduce the number of control times of the air valve, improve the service life of the fume hood, and ensure the safety of experimenters.