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.

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.

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.

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.

Aiming at the problem of the lack of historical data on arc faults in most photovoltaic power stations, this paper proposes a photovoltaic system series arc fault diagnosis method based on ultrasonic sensors and isolation forest after collecting arc ultrasonic signals and analyzing their characteristics. Firstly, arc ultrasonic signals are collected and their characteristics and advantages are analyzed. Secondly, the S-transform is used to convert the transient voltage signal of the ultrasonic wave during the occurrence of series arc faults to the time-frequency domain. Then, the Teager energy operator is used to amplify the spectral differences. Subsequently, the time-frequency entropy is used to extract the time-frequency domain features of arc faults. Finally, arc faults are diagnosed based on dynamic thresholds and isolation forest without the need for historical data. Experimental results show that the proposed method can accurately identify series arc faults, with a diagnosis accuracy rate of 97.25%, and has strong anti-interference ability.

In order to actively respond to the new round of scientific and technological revolution and industrial transformation, and explore the innovative teaching design of the curriculum suitable for the cultivation of new engineering talents, taking the electronic design automation (EDA) technology course of electronic information major as an example, this paper proposes a teaching reform plan of “three new integrations, three methods in one, and three views strengthening”. “Three new integrations” organically integrate new EDA development tools, new field programmable gate array (FPGA) devices and new online hardware debugging methods into the traditional teaching content. Combined with the teaching method, the collaborative teaching method of tool platform and the project-driven method, the “three methods in one” teaching mode is established, which not only allows students to master theoretical knowledge, but also helps to improve the practical engineering ability of students. The design of “three views strengthening” experiment cultivates the ability of students to carry out system-level design and innovative design for practical engineering problems. Through this teaching reform, students were organized to participate in the 2023 National College Student Electronic Design Competition and won two second prizes in the Hainan Provincial Division, and a school-level education reform project based on the curriculum reform was approved in the same year.

Wind power prediction plays a crucial role in ensuring the reliable integration of wind energy into the grid. This study proposes a novel hybrid model combining random forest (RF) and convolutional neural network (CNN), referred to as the RF-CNN model, specifically designed for short-term wind power prediction. The model integrates the advantages of RF integration technology, random selection of attributes, and CNN capturing the spatiotemporal characteristics of wind power, to enhance prediction accuracy and robustness. Firstly, by analyzing the analog equivalence between decision trees and CNNs, the theoretical basis for combining RF and CNN is established. Next, an evaluation system for wind power prediction models that includes root mean square error (RMSE), determination coefficient, and Spearman correlation coefficient is introduced. Finally, validatinos are conducted using three open-source wind power datasets from European wind farms. The results demonstrate that, compared to other five models, the RF-CNN model outperforms in all three datasets, thus confirming the model’s effectiveness and accuracy for wind power prediction.

The proposed practical case takes the signal acquisition project of BeiDou satellite system as the demands traction. The teaching case contains the knowledge and theory learned in the backbone courses, such as analog electronics, digital electronics, integrated circuit design, signals and systems, into the design of analog-to-digital conversion (ADC) circuit. It is expected to provide a coherent body of knowledge that will enable the students to use it for learning purposes. The practical case is a comprehensive, design-based experiment. According to the teaching design concept and the task requirements of the three levels (basic tasks, competence enhancement and engineering application), students complete the design of each unit in turn in accordance with the electronic system design process. Through the design of the competence enhancement and engineering application parts, the students' sense of innovation and enterprise is stimulated, their ability to solve practical problems is enhanced and their thinking about system design and engineering application is cultivated.

In order to effectively cultivate excellent talents who can overcome difficulties in the field of analog electronic technology, so as to effectively support China’s strategic goal of becoming self-reliant and self-improving in science and technology, and to address three major challenges of difficult knowledge system construction, theory and practice disconnection and difficultly persistent active learning in course teaching, this paper takes students as the center, uses information technology to reconstruct teaching content, enrich teaching resources, innovate teaching design and optimize teaching evaluation Thus students’ learning is personalized and efficient, and students’ development becomes comprehensive, which achieves the training goals of knowledge learning, ability training and quality improvement.

The failure of photovoltaic power generation equipment and various factors such as external environment lead to a large number of abnormal data during the power generation process. In order to improve the accuracy and efficiency of data processing, this paper proposes a distributed photovoltaic abnormal data identification method based on improved K-means algorithm and weighted dynamic time warping (WDTW). Firstly, the distributed photovoltaic power generation data is analyzed, and the abnormal data is preliminary eliminated by means of the simultaneous power mean method, and a photovoltaic data similarity day partitioning method based on improved K-means algorithm is proposed by normalizing the light intensity data. Secondly, considering the variability and complexity of photovoltaic data in the time dimension, a data similarity analysis method based on WDTW is proposed by introducing the best time period and threshold factor for identifying abnormal data. The similarity is used to calculate the contour coefficient, and the residual abnormal photovoltaic power generation data is culled twice. The simulation results show that the proposed method has significant advantages in identifying distributed photovoltaic abnormal data. Compared with the existing quartile method, 3-sigma method, and feature clustering method, the identification accuracy has been improved by 6.92%, 9.00%, and 8.12% respectively, while the computational complexity is reduced.