The effective identifications of important nodes in power grid are foundations of differentiated management and stable operation of power grid. However, the current studies on this field are not thorough enough. This paper applies the model based on co-citation (MBCC)-hypertext induced topic selection (HITS) algorithm used in web page ranking to power grid and proposes an index to determine node importance of power grid from the perspectives of inflow and outflow power. MBCC-HITS algorithm is modified considering load, power source, and line flow comprehensively for the differences between Internet and power grid in this paper. Then a method for evaluating node importance is proposed using the modified algorithm. Lastly, the rationality and validity of the proposed method are verified by comparisons with other methods in case studies of IEEE 14-bus and 118-bus systems.

Unmanned aerial vehicle (UAV) inspection has become the mainstream of transmission line inspection, and the detection of insulator defects is an important part of UAV inspection. On the premise of ensuring high accuracy and detection speed, an improved YOLOv5 model is proposed for defect detection of insulators. The algorithm uses the weights trained on conventional large-scale datasets to improve accuracy through the transfer learning method of feature mapping. The algorithm employs the Focal loss function and proposes a dynamic weight assignment method. Compared with the traditional empirical value method, it is more in line with the distribution law of samples in the data set, improves the accuracy of difficult-to-classify samples, and saves a lot of time. The experimental results show that the average accuracy of the insulator and its defect is 98.3%, 5.7% higher than the original model, while the accuracy and recall rate of insulator defects are improved by 5.7% and 7.9%, respectively. The algorithm improves the accuracy and recall of the model and enables faster detection of insulator defects.

To enhance equalization efficiency and address the issue of traditional equalization methods overlooking temperature factors, this paper proposes a multilayer equalization circuit for both intra-group and inter-group balancing. The traditional Buck-Boost equalization topology between groups is improved by incorporating a two-way interleaved inductor structure, which helps reduce equalization idle time. An adaptive fuzzy control equalization strategy for multiple objectives is applied to the topology. The state of charge (SOC) and temperature of the battery are used as key variables for equalization, with the equalization current dynamically adjusted based on changes in the SOC and temperature. This approach improves the balance between equalization speed and temperature control, reducing equalization time while limiting battery temperature rise. A simulation model is developed using MATLAB/Simulink. The simulation results demonstrate that, compared to the traditional Buck-Boost equalization topology, the proposed topology reduces equalization time by 15.1%. Additionally, under three different operating conditions, the equalization cotnrol strategy designed in this paper improves time efficiency by over 14% compared to traditional methods, while also reducing both the maximum temperature and temperature difference.