Long-term operation of distribution networks with persistent single-phase grounding faults will result in insulation deterioration and subsequent phase-to-phase short-circuit faults, and grounding arcs also pose potential fire risks. In order to improve the reliability of power supply and ensure the safety of equipment and environment, the active intervention arc suppression device can effectively reduce the current level of the fault point and extinguish the arc light generated by grounding by introducing the short-circuit current into the substation. However, most of the active intervention arc suppression devices use the zero-sequence voltage as the threshold as the starting criterion, and the resistance value of the single-phase grounding transition resistance has a great influence on the zero-sequence voltage. In order to ensure the efficient operation of the device, analyzes the functional relationship between the transition resistance and the zero-sequence voltage firstly, then deduces the calculation formula of the transition resistance by the vector triangle method and the functional relationship method, and explains the setting method of the zero-sequence starting voltage of the device, and finally verifies the validity and rationality of the calculation method of the endurance transition resistance and the parameter setting of the zero-sequence starting voltage through the simulation experiments and the high-impedance artificial single-phase grounding experiment.

To address the complexities in regulating and operating distribution grids with a high proportion of distributed photovoltaic (PV) systems, which challenges conventional methods in vulnerability identification, a novel methodology utilizing an edge-feature graph attention network (EGAT) is proposed for state estimation and further identification of vulnerabilities via over-limit indicators. Initially, busbars within the distribution network are conceptualized as nodes of a graph, with transmission lines serving as edges, thus forming a topological dataset grounded in their connectivity matrix. Subsequently, the power of each transmission line and the voltages at the connected busbars are consolidated into an edge feature matrix. The employment of EGAT layers facilitates targeted feature extraction and learning, where attention weights are attributed to each node and transmission line. In response to various operational scenarios of the PV distribution network, the EGAT model undergoes training with the integration of transfer learning techniques. Ultimately, by establishing indices for node vulnerability and line vulnerability, a thorough evaluation of the network’s vulnerability under different scenarios is achieved. Demonstrative case studies validate the effectiveness of this approach in recognizing vulnerabilities within distribution networks with substantial integration of distributed PV systems, accurately identifying vulnerable nodes and lines induced by fluctuations in PV output and topological modifications.

In order to increase the energy efficiency of fuel-cell hybrid electric vehicle (FCHEV) and improve the operating environment of fuel cells, an energy management strategy is proposed. Firstly, wavelet transform is used to decompose the vehicle bus power, and the decomposed low-frequency demand power and the state of charge of the power battery are used as inputs to the fuzzy controller. Then, the output power of the fuel cell is calculated via the energy management strategy. Furthermore, genetic algorithm is used to improve the fuzzy rules and improve the control performance. Finally, a vehicle energy management strategy model is built, and a vehicle dynamics model using Simulink and Cruise respectively, and conduct joint simulation under CLTC-P operating conditions. Compared with the power following based control strategy, the proposed strategy reduces the equivalent hydrogen consumption of fuel cells by 14%. While the vehicle is more energy-efficient, it effectively reduces the start stop frequency and load change rate of fuel cells, thereby extending their service life.

In order to correctly identify the operating status of photovoltaic arrays and reduce the missed diagnosis rate of faults, an improved bidirectional long short-term memory network (BiLSTM) fault diagnosis method for photovoltaic arrays is proposed. Firstly, by combining Attention mechanisms to optimize and adjust the attention weight allocation of fault labels, the missed diagnosis rate of photovoltaic array fault diagnosis can be reduced; Secondly, the traditional Harris hawks optimization (HHO) algorithm is further optimized by integrating the best point set, segmented adaptive escape energy mechanism, and golden sine strategy, and the optimized Harris hawks optimization (IHHO) algorithm is used to optimize the hyperparameters of the BiLSTM model. Comparative experiments show that the IHHO-Attention-BiLSTM model achieves a fault diagnosis accuracy of 99.26% for photovoltaic arrays, with a missed diagnosis rate as low as 0.13%.

Accurate estimation of the battery state of health (SOH) can effectively improve the safe operation of system. Aiming at the problem of unstable SOH estimation accuracy under different working conditions, a stacking model-based method for lithium-ion battery SOH estimation is proposed. The method firstly carries out incremental capacity (IC) analysis for the charging data, and extracts the health factor as an effective feature, and then constructs an stacking model by stacking the support vector regression (SVR) and back propagation (BP) neural network model to realize the on-line evaluation of the battery SOH. The experimental results show that the maximum error of the stacking model proposed decreases by 38.1% and 23.7%, and the root mean square error (RMSE) decreases by 47% and 20% compared with the two sub-models. The proposed method can more accurately estimate the SOH under various operating conditions, mitigating the instability factor of the single model estimation.

The state of health (SOH) of Li-ion batteries gradually decreases with the number of charges and discharges, whose features are hidden in the physical information such as battery current and voltage. A data-driven method based on wavelet packet transform (WPT) and sparrow search algorithm (SSA) is proposed for online estimation of SOH, and the feature indices of discharge voltage plateau time (DVPT) and constant current charging time (CCCT) are proposed. The algorithm is verified on the University of Maryland battery public dataset. The results show that the proposed DVPT characteristic metric has a strong correlation with SOH, with an average Pearson correlation coefficient of 98.38% across the four batteries; the model predicted the optimal results with RMSE of 0.019 1, MAE of 0.012 5, and R² of 97.78%.

With the development of wireless power transfer (WPT) technology, the parameter identification technology for WPT systems has attracted extensive attention. Efficient and accurate parameter identification methods can simplify system design and control strategy, and achieve online monitoring of key parameters. The parameter identification and control methods of WPT system are first summarized. Then, the current parameter identification methods of WPT system are divided into three categories: mutual inductance identification, simultaneous identification of mutual inductance and load, and multi-parameter joint identification. Meanwhile, the implementation methods and key problems of each type of identification method are analyzed. Finally, the important problems and technical challenges of parameter identification technology are discussed, and the future application prospects of parameter identification technology are forecasted.

The unmanned platforms have the advantages of small size, low cost and no man-carrying operation, and are widely used in various military missions. At present, the unmanned platforms mainly use electric energy as power source, and their demand for reliable power transmission methods is increasing. In the cases where personnel cannot participate, the charging interfaces are not suitable, and the charging process is low in efficiency, wired charging or battery replacement has drawbacks, which cannot meet the demand for electric power transmission of the unmanned platforms. The system structure is designed to meet the requirements of unmanned platforms wireless power transfer, and based on this structure, a parameter optimization method based on particle swarm optimization (PSO) algorithm is proposed. By analyzing the structure and characteristics of the compensation circuit, coupling coil and energy storage device of the unmanned platform wireless power transfer system, the optimization objectives of the mathematical model of the system parameter optimization and the constraints of power, efficiency and current are proposed, and the algorithm flow of the parameter optimization method of the unmanned platform wireless power transfer system based on PSO algorithm is designed. The results show that the proposed optimization method based on PSO algorithm can reduce the fluctuation amplitude of the output voltage, power and efficiency with the load resistance and coupling coefficient change, and improve the robustness and environmental adaptability of the unmanned platform wireless power transfer system.

Aiming at the problem that the relative position deviation of the transceiver coil in the magnetic coupling resonance wireless power transfer system leads to the rapid reduction of transmission power and efficiency, A power transfer mechanism with wide effective magnetic coupling region is constructed from the perspective of coil physical parameter optimization. The aim is to increase the anti-offset ability of the system under the allowable range of energy efficiency fluctuation. Firstly, the mapping relationship between system parameters and energy efficiency is derived, and the coil parameters which affect the high efficiency transfer of the system are analyzed. Then, the effective magnetic coupling region of power transmission is defined with the goal of high power and high efficiency transfer of the system. Based on this, the coupling coil parameters are analyzed and optimized, and an optimal strategy for selecting universal transceiver coil parameters is formed. Finally, in order to verify the correctness of the quantitative analysis of the effective magnetic coupling region and the effectiveness of the coil parameter optimization, a wireless power transfer system prototype is built. The results show that when the coupling distance is 5 cm and the efficiency is higher than 85% and the power fluctuation range is less than 25%, when the coupling coil is a regular quadrilateral structure and the area ratio of the transmitting and receiving coils is 1:2.89, the power transfer mechanism has the largest effective magnetic coupling area.

The total weight and flight resistance added to the UAV by the wireless charging device during wireless charging of the UAV can reduce its flight efficiency as well as its flight time. A lightweight design method based on the receiver side of UAV wireless charging system is proposed to address this problem. Firstly, the core material and size of the coupling mechanism are optimized. Then the feasibility of the optimization method to realize the lightweighting of the receiving coil by increasing the system frequency is studied and verified, and the impact of increasing the system frequency on the main circuit is analyzed from the perspective of magnetic core loss. Then combined with the actual structure of the UAV, the effect of the carbon fiber fuselage on the performance of the magnetic coupling mechanism of the UAV is analyzed by using the overall modeling and analysis method. Finally, the physical model of wireless charging system for UAVs is built, and the performance tests of the original system and the up-frequency lightweight system are carried out, and the feasibility is verified through comparative experiments.