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Hydrogen energy storage has many characteristics such as large energy storage capacity, long storage time, clean and pollution-free, and it realizes the interconnection and complementation of multiple energy networks and collaborative optimization. It is expected to become an important supporting technology to promote the development of distributed energy and improve the efficiency of terminal energy utilization. In order to improve the reliability and renewable energy utilization rate of islanded microgrid, the operation characteristics of typical electric, hydrogen and thermal devices are analyzed, and a multi-objective optimization configuration model of the islanded microgrid is proposed. Then, the target problem is solved based on simulated annealing particle swarm optimization (SAPSO) algorithm to obtain technical and economic indicators under different configuration schemes. Finally, based on the annual natural resource and electric heating load characteristic curve of a certain place in the north, the model built on MATLAB can effectively promote that the load loss rate of the proposed multi-energy complementary configuration scheme decreases by 3.18%, and the utilization rate of renewable energy increases by 8.37% compared with the traditional electric energy storage configuration scheme. Thus, the proposed configuration scheme can effectively promote the consumption of renewable energy and ensure the economy and power supply reliability of the independent micro-grid.

The characteristics of overvoltage are closely related to the system grounding mode, which is of great significance to the selection and design of key equipment. However, the research on grounding mode selection combined with overvoltage analysis in DC distribution network is still imperfect. Firstly, the mechanism of overvoltage generation is analyzed for ±10 kV dual-terminal DC distribution network based on modular multi-level converter (MMC), and the overvoltage peak values at key positions of the system under three typical grounding modes are compared by fault simulation in PSCAD/EMTDC. Then, the fault recovery ability, economy, voltage stability and overvoltage peak value are proposed as the indexes for the comprehensive selection of grounding mode. Grounding by resistance through neutral point of the transformer in AC side and ungrounding in DC side is evaluated as the preferred grounding mode of the studied system. Finally, the arrester parameters and configuration scheme are determined based on the chargeability calculation design method, and the validity is verified by simulation. References for the selection of grounding mode and insulation configuration of DC distribution network are supplied by the research.

With the implementation of “photovoltaic power stations for poverty alleviation”， it becomes a new idea of earning extra income for farmer households with access to photovoltaic power and incorporating solar panels with power grid. In view of the characteristics of rural distribution networks， large-scale integration of photovoltaic power aggravates voltage quality. Firstly， given PV output fluctuations and complex distribution of rural loads， a typical system scenario is established using a clustering method based on AP （affinity propagation）. Secondly， the paper establishes a voltage quality control and planning model of double-layer hybrid distribution networks based on constraints of active power sources and reactive power sources， including bi-directional power flow， voltage constraint， PV， energy storage， and SVG. Finally， the paper presents a comprehensive solution to line loss rate and voltage quality control using distributed robust planning algorithms based on typical system scenarios. The results show that the proposed voltage control scheme can reduce costs and line loss rate without exceeding the voltage limit， and is proved safe and economical.

Non-point source (NPS) pollution has a complex formation mechanism, and identifying its primary controlling factors is crucial for effective pollution treatment. In this study, the Baixi Reservoir Watershed, characterized by low-intensity development, was selected as the study area. A new methodology combining the Soil and Water Assessment Tool (SWAT) with the Random Forest (RF) algorithm was proposed to comprehensively identify the primary controlling factors of NPS pollution and analyze the interaction between factors. The results of the validated SWAT model showed that the annual intensity of total nitrogen (TN) load range was 0.677–11.014 kg ha−1 yr−1, and the total phosphorus (TP) load per unit area range was 0.020–0.110 kg ha−1 yr−1. Loads of sediment, TP, and TN exhibited significant seasonal variations, particularly in the Baixi basin, where sediment yield had the highest absolute change rate, with a value of up to 232.26. Random Forest models for TN and TP displayed high accuracy (R2 > 0.99) and robust generalization ability. Fertilization, sediment yield, and terrain slope were identified through RF models as the primary factors affecting TN and TP. By graphing partial dependency plots (PDPs) based on the results of the RF models to analyze the interaction between factors, the findings suggest a strong synergistic effect of two combined factors: fertilization and sediment yield. When fertilizer application exceeds 15 kg ha−1 yr−1 and sediment yield exceeds 3 kg ha−1 yr−1, there is a sharp increase in nitrogen and phosphorus load. Through the identification and analysis of the primary controlling factors of NPS pollution, this study provides a solid scientific foundation for developing effective watershed management strategies. [ABSTRACT FROM AUTHOR]

Due to the problems such as fuzzy state assessment grading boundaries, the recognition accuracy is low when using traditional fuzzy techniques to grade the switchgear state. To address this problem, this paper proposes a switchgear state assessment and grading method based on deep belief network (DBN) and improved fuzzy C-means clustering (IFCM). Firstly, the switchgear state information data are processed by normalization method; then the feature parameters are extracted from the switchgear state information data by using DBN, and finally the extracted feature parameters are categorised according to the condition of switchgear equipment through clustering using IFCM. The experimental results show that the accuracy of the method in assessing the switchgear state under small sample conditions reaches 94, which exceeds the accuracy of other switchgear state assessment grading methods currently in use. [ABSTRACT FROM AUTHOR]