Accurately identifying the key links in active distribution network (ADN) that affect the system's safety and stability is of great significance to improve the efficiency of operation and maintenance and risk early warning for distribution network (DN). In this study, the node resilience (NR) is first proposed as an indicator for structural stability identification of DN. Then, a critical node identification method considering the security, economy and structure stability in ADN is proposed, which uses the NR, the utility risks of branch load change, node voltage deviation and line loss variation as the identification indicators. Also, combining with subjective experience and objective data, the comprehensive evaluation method of critical nodes is improved, and the multilevel and multidimensional evaluation model of critical nodes is constructed. Furthermore, the time sequence of load change and photovoltaic output is considered. Finally, the rationality and effectiveness of the proposed method are proved by taking IEEE 33‐bus system and an actual ADN as examples.

This study proposes a novel protection scheme for residual current devices (RCDs) based on the theory of skewness and the least squares‐support vector machine (LS‐SVM). The skewness of the residual current is applied to detect the electric shock fault time, and the LS‐SVM regression technique is used to perform touch current identification. The experimental test results indicate that the protection scheme can not only detect the electric shock fault time correctly and rapidly but also effectively recognise the circulation through the animal's current. Moreover, the proposed protection technique can provide valuable information for developing new generations of RCDs.

Gases emissions estimation and analysis by using carbon dioxide balance method in natural-ventilated dairy cow barns

With the widespread use of new energy sources and Internet of things, the power market landscape has become complex. In particular, new energy is more stochastic and volatile; it is prone to the problems of inaccurate forecasting on longer time scales, affecting electricity trading. This study proposes a new method for predicting medium-term load series data based on the transformer-lightGBM. The method first preprocesses electricity market data, including missing value processing, outlier processing, overall analysis, and correlation analysis, to extract features with a strong correlation to medium-term electricity consumption forecasts. Then, a transformer neural network is used to learn the complex patterns and dynamic time scales of the load series data to predict the day-ahead market series. Finally, lightGBM is used to combine power characteristics and time characteristics to forecast power consumption. The effectiveness of the proposed method is proved using the ISO-NE dataset. Experimental results indicate that the present method verified more accurate prediction than LSTM-based methods.

The mechanical and electrical development in dairy farming in China increases energy-related carbon emission (CE). To support the sustainable planning strategy of the department, this study calculated the CE and the carbon emission intensity (CI) of the direct energy consumed in dairy farms from 21 provinces in China. Through four dimensions analysis including the national level, farm scale, inter-provincial distribution, and main producing area, this study illustrates the impact of the environment, production, and management on CE. The total CE of nationwide dairy farming was about 2.4 Tg CO₂ eq. in 2019, and the CIs of the 21 provinces varied from 0.009 to 0.216 kg CO₂ eq. per kg of milk. The results indicate that the management mode applied in large-scale dairy farms (500 heads and above) varies considerably due to inadequate adaptation to climate. In general, semi-arid and semi-humid regions are more suitable for dairy farming than arid and humid regions. In the main milk-producing area, the spatial aggregation effect is visible in the carbon reduction potential. The present study suggests that further steps to promote sustainability and milk productivity are embodied when the replacement of fossil fuels and the management standardization are adapted to regional characteristics.