With the rapid development of photovoltaic (PV) power generation technology, especially the widespread application of inverter-interfaced distributed generation (IIDG), its impact on distribution networks has become increasingly significant. To fully utilize the positive effects brought by IIDG integration into distribution networks, this paper proposes a method of harmonic differential protection for PV grid-connected loads based on characteristic signal injection. This method achieves effective protection of system load components by adding a characteristic signal injection strategy to the current inner-loop controller of inverters. Through theoretical analysis and simulation verification, the effectiveness of this method under different fault conditions is proven.

To address challenges in railway curve section track replacement operations- including difficulties in longitudinal movement of old rails, limited space for new rail placement, and poor stability of welded rails-this study designs a longitudinal rail relocation system for curved track sections. Through 3D modeling and static simulation, beam element models of 60kg/m rails were established with varying lengths (50m, 75m, 100m, 150m), material properties were configured, and constraints and loads were applied. The analysis revealed support arrangement schemes for each rail length that ensure maximum deformation remains below 32mm. The system incorporates longitudinal rail movement mechanisms, positioning devices, and rapid disassembly components, enabling safe fixation of relocated rails while reducing cutting and secondary welding processes. This innovation enhances operational efficiency, cost-effectiveness, and safety in rail replacement for curved track sections, providing technical support for modern track maintenance practices.

Nowadays, existing fault diagnosis technologies have problems such as slow response speed, low accuracy, and weak adaptive ability. To prevent overfitting, this article can use a strictly separated set of training and testing samples to train the model. In order to ensure the generalization performance of the model, mutual confirmation technology was adopted. The computing power of GPUs can be utilized to effectively process massive amounts of data and improve training efficiency. In the field of fault diagnosis, the proposed method can achieve real-time collection of the operating status of the power grid, and use the established artificial intelligence model to analyze it, thereby achieving rapid identification and localization of system fault types and locations. This method has self-learning function, which can continuously improve the accuracy of fault diagnosis while accumulating data. At the same time, the algorithm also has an alarm function, which can predict and warn the system before it malfunctions, thereby taking corresponding preventive measures. At a transmission speed of 10 kbps, the error detection accuracy of the system reached 98.5%. This article can promote the development of power grid fault diagnosis and protection technology, which is conducive to providing new ideas and methods for power system fault diagnosis and relay protection.

Electricity is the basis for everyday life, and defect detection of insulators of high-voltage transmission lines is the key to ensuring power transmission. In order to overcome the problems of low accuracy of traditional target detection algorithms for small targets, weak representation ability of feature maps and little key information extraction, an improved CBAM attention-based insulator defect detection method CBAM-YOLOv8 based on YOLOv8 was proposed. The core is to apply the combination of Channel Attention Module and Spatial Attention Module to process the channel attention and spatial attention modules respectively for the input feature layers. Experiments show that the AP in the CPLID dataset is improved by 4.7% and the FPS is reduced by 2.7% compared with the original YOLOv8, which proves that the proposed method can maintain a high detection speed while greatly improving the detection accuracy, providing a more effective and safer solution for the detection of high-voltage transmission lines, and greatly reducing the labor cost and operation risk.

Mulch planting and cultivation technology, known as the "second revolution" of agricultural production, is widely used in the development of agricultural production and promotes the increase of farmers' income. However, due to the difficult degradation of agricultural film materials and improper treatment methods, a large number of agricultural film fragments remain in the deep soil, causing great pollution to the soil environment. In this paper, through the analysis and research of the pollution status of agricultural broken film and the application status of agricultural film recycling device, it is proposed that the principle of electrostatic adsorption is used to adsorb and remove the small particle broken film in the deep soil, and the microwave drying technology is used to reduce the adhesion between the broken film and soil, so as to achieve the optimization of the existing device. The experimental results show that the optimized cleaning device can effectively remove the residual broken film in the deep soil, effectively reduce the white pollution in the soil, and provide a reference for the treatment of broken film pollution.

Composite insulator is an important insulation equipment of overhead transmission lines. Different pollution levels will cause different degrees of influence on the hydrophopathy of composite insulator. The decrease of hydrophobicity level and the increase of pollution level will reduce the pollution flashover voltage of composite insulator and affect the reliable operation of composite insulator. In order to accurately study the influence of different pollution grades on the hydrophobicity of composite insulators, the hydrophobicity test, equivalent salt density test and non souble deposit density test were conducted on the composite insulators of voltage grade of 5-18 years, 35kV and above in Ningxia power grid. The results show that under the same operating years, composite insulators with higher pollution levels have lower hydrophobicity and are more prone to flashover. The evaluation results can provide important reference value for the subsequent operation and maintenance of composite insulators.

The course of mechanical testing technology occupies a very important position in the mechanical major, and signal processing technology is its main teaching content. This paper introduces a fault diagnosis case based on vibration sensor to deepen students' understanding of signal processing process. Taking a certain mechanical equipment as the research object, a vibration sensor is arranged to monitor the vibration of the equipment during operation. Then, the vibration data of the equipment under different working conditions are collected and analyzed. Based on the feature extraction of vibration data in time and frequency domain, principal component analysis is used as the feature selection method. Finally, the fault analysis is carried out with support vector machine. Through this case, the students could better understand how to use vibration data for fault diagnosis analysis.

The electrical equipment of offshore oil support ships operates in harsh marine environments with high failure rates, posing challenges to the development of preventive maintenance strategies. To address this issue, this article proposes an intelligent platform based on Long Short Term Memory (LSTM) algorithm to optimize preventive maintenance strategies for electrical equipment. Firstly, operational data of ship electrical equipment is collected, and data preprocessing and key feature extraction are carried out; subsequently, a prediction model based on LSTM is constructed to make real-time predictions on the health status of the equipment; then, the predictive model is integrated into the intelligent platform to achieve real-time monitoring of device status and dynamic optimization of maintenance strategies. The experimental results show that the LSTM based prediction model outperforms support vector regression (SVR) and random forest methods in terms of prediction accuracy and robustness. The average monthly failure rate of the equipment is 0.67 times, and the maintenance cost for 12 months is only $4750. In the above data conclusions, the intelligent platform based on LSTM algorithm can significantly improve the effectiveness of preventive maintenance strategies for marine oil support ship electrical equipment, highlighting the advantages of the proposed method.

With the deepening of energy structure transformation, new energy sources such as photovoltaics and wind power are important means to achieve carbon neutrality in the future. In order to solve the instability problem of their use, research on electrochemical energy storage systems has become a key aspect. In addition, lithium-ion batteries play an important role in electronic devices, but their lower theoretical specific capacity makes it difficult to meet the needs of large electronic devices. Lithium-sulfur batteries have a theoretical specific energy density (2600 Wh•kg^-1) and theoretical specific capacity (1675 mAh•g^-1) several times higher than lithium-ion batteries, and they have abundant elemental sulfur reserves and low prices. Therefore, lithium-sulfur batteries are very promising energy storage devices. The positive electrode material has an important impact on the performance of lithium-sulfur batteries and has been widely studied. This article investigates the effect of different sulfur cathode thicknesses on the performance of lithium-sulfur batteries.

Distributed generation (DG) with unreasonable location and capacity will adversely affect the network loss, voltage stability and reliability of the distribution network, resulting in the underutilization of distributed energy such as wind energy and solar energy. In this paper, based on the measured wind turbine output data, the distributed flexible AC transmission system (D-FACTS) is used to optimize the power flow of the distribution network to reduce the limitation of DG position and capacity. The double changes of wind turbine output and load at different times are simulated, and the network loss, voltage level and reliability of the system are analyzed. The verification of IEEE36 distribution system shows that the installation of D-FACTS equipment on the distribution network with a large number of DGs plays a role in reducing network loss, stabilizing system voltage and improving reliability.