In this paper, the high-voltage and high-power electrical connection methods of more- electric engines are reviewed. Firstly, the technical challenges of electrical connection faced by more-electric engines due to high-power motor components, high-voltage systems, and new control modes are analyzed. Secondly, the key technology analysis is carried out from the aspects of high-current connectors, transmission cables, laminated busbars, partial discharge, and electromagnetic compatibility. The design measures such as contact clearance, creepage distance, interface sealing, contact sealing and interlocking function of joining and separating are proposed for high-voltage and high-current connectors. The electrical transmission capacity of three transmission cables, such as copper power cables, lightweight power cables and superconducting cables, is analyzed. The busbar connection technology with high voltage and high working temperature is studied. The mechanism and solution measures of high-altitude partial discharge are analyzed. A combination of simulation and test solution is proposed for electromagnetic interference. Finally, it is pointed out that multi-dimensional optimization design should be carried out in the design process of more-electric engine to achieve the optimal electrical connection. The work of this paper can provide reference for the current design of lightweight electrical system of more-electric engine.

With regard to the measured Raman spectral data of transformer insulating oil, the relevant research is carried out based on Raman spectral data processing, feature extraction and aging diagnosis with Raman spectral technology as a means to accurately identify the aging state of transformer insulation. First of all, combined with the law of change of Raman spectral noise, based on the wavelet transform theory, the global threshold wavelet transform filtering method is proposed, which effectively removes the noise signals in Raman spectra. Further, an improved baseline correction method is proposed based on the adaptive iterative reweighting penalized least squares (airPLS) method, which accurately removes the fluorescence background of Raman spectra. Secondly, the aging feature information in Raman spectra is extracted using successive projections algorithm (SPA), and its relationship with the aging degree of transformer insulating oil is analyzed. Finally, the light gradient boosting machine (LightGBM) classification model is used to realize accurate discrimination of transformer insulation aging state, and the extreme gradient boosting (XGBoost) model is used as a control group to compare the diagnostic accuracy of the two. The experimental results show that the LightGBM model possesses obvious advantages in diagnostic accuracy, and also further verifies the validity of the extracted aging feature information.

Considering that the network utilization cost in peer-to-peer (P2P) integrated energy transactions often accounts for more than 25% of the transaction costs, which significantly impacts the transaction benefits for prosumers, a P2P transaction strategy for the integrated electricity-heat energy system, which accounts for the network utilization cost, is proposed. Electrical distances in transaction path for electrical energy, and thermal resistances and lengths of pipelines in transaction path for thermal energy, are modeled to calculate the network utilization cost for both electricity and heat networks. By using the reputation index to assess the willingness of both parties participating in transactions, the optimization problem of P2P energy transaction strategy is constructed. Furthermore, a distributed solution method based on alternating direction method of multipliers (ADMM) is proposed. A P2P integrated energy simulation system comprising a 15-node electrical grid and an 8-node thermal network is established and further extended to a 33-node electrical grid and a 23-node thermal network system to verify the effectiveness of the proposed method and its scalability in P2P energy trading. Simulation results show that electricity-heat integrated energy P2P transactions with considering the network utilization cost are helpful to reducing energy transaction costs, promoting local energy consumptions, and increasing social welfares.

This paper proposes a multi-target recgnition network for birds on power transmission lines, called RVFNet, based on the fusion of radar and camera data. The network achieves high-precision recgnition of bird targets within the monitoring range by integrating radar radio frequency (RF) data with visual images. To address the semantic differences between multimodal data, the correspondence between radar RF signals and image positional information is calculated to ensure consistency in feature representation. Structurally, the network incorporates a bird posture convolutional network (BPC) to effectively fuse multimodal information, enhancing the extraction of small-target features and preserving fine details. Additionally, a feature fusion module (FFM) is introduced to integrate multimodal features, significantly improving feature interaction while maintaining low computational costs. Experimental results demonstrate that RVFNet achieves an average bird recognition accuracy of 80.18% under various weather conditions, highlighting its robustness.

In response to issues such as the diverse waveforms of arc high impedance faults in distribution networks, significant discrepancies between simulated and actual waveforms, which resulting difficulty in generalizing existing fault recognition models, the voltage-current characteristics of existing simulation models are analyzed firstly in this paper. Then experimental data are obtained to examine the actual voltage-current characteristics, and the differences between simulation and measured waveforms are analyzed. Finally, the characteristics influencing waveforms, including nonlinearity, stochasticity, intermittency, thermal inertia, and “shoulder” offset, are summarized, and improvement suggestions are proposed based on the consideration of reactive components.

The microgrid is an important part of the new power system. The energy coordination and interaction between microgrids is conducive to the consumption of new energy and improving the economy of the group. An energy collaborative optimization method considering demand response and carbon trading is proposed for multi-microgrid systems. Firstly, the framework of multi-microgrid energy collaborative optimization is constructed. Then, the internal resources of the microgrid are modeled, and demand response and carbon trading mechanisms are added on the original basis. Then, the objective function of energy coordination between single microgrid and multi-microgrid is constructed, and the improved beluga whale optimization is used to solve it. Finally, based on the simulation, it is verified that the algorithm can effectively improve the economy of multi-microgrid system energy cooperative optimization, and provide certain reference for the actual coordinated optimization operation of multi-microgrid.

Due to the input capacitive load characteristics of unmanned aerial vehicle (UAV) onboard electronic equipment, a high inrush current is easily induced during DC power-on transients, leading to reduced stability of the power system and potential system-level electromagnetic compatibility (EMC) issues. To address the demand for suppressing transient inrush currents, this study proposes an innovative pre-charge circuit suppression method based on a systematic analysis of traditional surge suppression schemes. By constructing a controllable pre-charge circuit and optimizing the RC time constant design, precise matching between the charging current and time constant is achieved, significantly reducing implementation costs while ensuring system reliability. Engineering validation demonstrates that this method effectively suppresses the peak inrush current within the rated operational current range and maintains stable power bus voltage. The research provides an engineering-practical solution for ensuring power quality in onboard electronic systems, particularly suitable for UAV platforms with stringent requirements for power supply integrity and electromagnetic compatibility.

In actual production and daily life, non-linear loads such as LED lights and LED screens are widely used. Such devices generate 3n harmonics, resulting in an excessively large current in the N line. The N line curent can be managed by configuring neural line safety protector (SNP) at the terminal. When there are a large number of SNP devices, the inspection personnel need to check them one by one, resulting in relatively low operation and maintenance efficiency. When there is an overcurrent in the N-line or other faults, timely feedback cannot be obtained, and ultimately, the SNP is damaged, causing economic losses. This paper proposes a method for N-line overcurrent protection and detection based on neutral line safety protector, which is realized through the internal hardware circuit. When the current value of the N-line exceeds the set value, the internal relay sends a dry contact signal to control the shutdown and alarm of the SNP. At the same time, users can monitor data and control the operation through the WiFi module and the network communication circuit. Finally, the practicality and safety of this method are verified through experiments. It has the advantages of providing timely feedback of information, protecting the safety of equipment, and facilitating operation and maintenance.

This study aims to effectively identify abnormal or non-compliant electricity consumption behaviors in electric vehicle charging stations, thereby enhancing the efficiency and accuracy of electricity management for these stations. Initially, the study analyzes the electricity consumption behavior characteristics of low-voltage charging stations, determining the differences in load characteristic curves between normal and abnormal electricity consumption states. Based on this, a clustering analysis algorithm is employed to extract load curve characteristics from operational charging stations and compare them with standard load curves to assess the presence of abnormal electricity consumption behaviors. Additionally, considering potential misjudgments arising from the “fast charging” and “slow charging” phases during the charging process, the concept of sliding difference linear fitting is introduced. This involves calculating the slope between each pair of 96-point load data points and using the number of slope changes to assist in the judgment of clustering analysis results. Through the aforementioned methods, users exhibiting abnormal electricity consumption behaviors have been successfully identified, providing technical support for the management of electricity consumption in charging stations.

Based on the characteristics of the fully domesticated chip relay protection device’s software and hardware platforms, and addressing the key and difficult points in the testing process, this paper proposes a multi-CPU internal communication testing method for fully domesticated chip relay protection devices. Utilizing a dedicated internal detection module, the method simulates and monitors various normal and abnormal data without altering the original wiring layout, to verify the stability of internal communications between CPU boards. Additionally, this paper presents key technologies of internal communication reliability testing and platform application testing, details test plans for critical applications such as platform self-check, intelligent IO self-check, abnormal reset functions, and memory overflow risks, and summarizes typical on-site problem verification and key testing items. Finally, by comparing and analyzing the differences in key technical indicators between the “nine unified” devices and fully domesticated chip relay protection devices, the study offers insights for the product development of fully domesticated chip relay protection devices.

Both sides of the high and low voltages of the auxiliary transformer, which is in the standby state for long time in the nuclear power plant, is still balanced after the single phase open on the high voltage side, posing a threat to the safe operation of nuclear power units. In order to detect the open-phase timely, the optical current transformer is selected to measure the current of the high voltage side accurately according to other electrical characteristics of the auxiliary transformer after phase open, and the corresponding identifying logic is developed according to different working conditions to determine the open-phase monitoring scheme. The setting method, resolution and measuring range of the optical current transformer are studied to understand its technical characteristics. The open-phase monitoring system is introduced from the aspects of hardware, software and logic. At last, maintenance strategy of regular inspection for optical current transformer is given.

For a 220kV substation with 35kV capacitive voltage transformer secondary voltage abnormal drop fault, this paper introduces the structure of capacitive voltage transformer and its working principle. Then, combined with fault phenomenon, diagnostic test data, equipment disintegration inspection, the cause of the fault as ferromagnetic resonance overvoltage causing insulation breakdown of the differential and angular coils is analyzed and determined. Finally, some suggestions are put forward for the infrared temperature measurement and secondary voltage monitoring in the daily operation and maintenance inspection to improve the reliability of capacitive voltage transformer operation.