Aiming at the problems of dense distribution of elements, similar elements and large number of small-sized elements in power dispatching control system diagrams, which lead to poor recognition effect, an improved you only look once v7 (YOLOv7) element recognition method for the power dispatching control system diagrams is proposed. Firstly, the lightweight dilated reparam block net with cross stage partial and efficient layer aggregation network (DRBNCSPELAN) module is used to replace the efficient layer aggregation network (ELAN) module in the backbone network to capture spatial patterns of different scales simultaneously. Secondly, an information-guided fusion module is proposed to replace the Concat in the neck network, and the sequeeze-and-excitation (SE) attention mechanism is integrated to enhance the global information interaction ability. Then, the minimum point distance intersection over union (MPDIoU) loss function is introduced to improve the recognition effect of the element bounding box. Finally, experimental validation is performed via the power dispatch control system diagram dataset. The results show that compared with the baseline model, the precision, recall and mean average precision of the proposed method are improved by 5.1 percentage points, 3.1 per-centage points and 3.5 percentage points respectively, which is helpful to achieve accurate recognition of elements in the power dispatching control system diagrams.

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.

Establishing the correlation between macrolevel indexes and power supply reliability is essential to analyzing the power supply reliability of distribution network on a large scale (provincial and municipal). However, the existing methods rely on the detailed distribution network topology, which is difficult to meet the requirements of regional power supply reliability analysis. This paper firstly establishes a macrolevel index system of power supply reliability for planning, construction, operation and management of distribution networks. Then a calculation method of power supply reliability is proposed by taking into account the automated isolation of distribution networks and pre-scheduled outages. On this foundation, the calculation method of power supply reliability based on the macrolevel index system is proposed by dividing the equivalence assessment sub-models and calculating the parameters of the sub-models. Finally, a case study is carried out to verify the proposed method. The results show that the relative deviation between the calculation results of the proposed method and the actual data is within 11%. The method proposed in this paper can analyze power supply reliability in the region by using only the macrolevel indexes, which provides a certain degree of accuracy and operability for the planning of distribution networks, reliability management and reliability target setting.

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 discharge rod is crucial for the safety of working personnel in substations. However, traditional discharge rods have long discharge times and low work efficiency. A rapid discharge rod is designed to achieve rapid discharge of residual voltage in equipment while avoiding damage to the equipment caused by excessive discharge current. On the basis of the traditional RC discharge circuit, inductive elements are added to form RLC and RL discharge circuits. By selecting RLC circuit or RL circuit through metal-oxide-semiconductor field-effect transistor (MOSFET), rapid discharge of equipments can be achieved, thereby improving the work efficiency of operators.

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.

Due to the complex structure of urban distribution networks, which are mostly cable and overhead mixed lines, and the neutral point is often grounded through arc suppression coils, when the system is single-phase grounded, the grounding current at the fault point is often weak, making it difficult to directly select the fault line by steady-state electrical quantities. This article creatively proposes a fault line selection method that combines transient zero sequence current and wavelet decomposition. The method quantifies the fault characteristic values and provides a fault diagnosis logic. Multiple sets of simulation data show that the fault line selection method proposed in this paper is not affected by factors such as distribution network structure, fault point location, closing angle during grounding fault, and resistance of fault point, and has good prospedts for engineering application.

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.

The concept and technology of online education are applied to experimental teaching, and the research and exploration are conducted on remote intelligent online physical experiment technology. A remote intelligent experiment system has been constructed to achieve deep integration of intelligent software and hardware systems with electronic circuit experimental teaching, and applied to electronic circuit related experimental courses. The characteristics and problems of traditional offline experimental teaching are explored, and the advantages of remote intelligent experiment system in experimental teaching are analyzed. Practice show that it breaks through the limitations of time, space and resources in traditional experimental teaching in universities, and provides students with more flexible and convenient experimental experiences, and plays an important role in improving students’ self-learning and innovation abilities.

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.

Joint Sino-foreign education is a new model of higher education reform and an important way of cross-cultural education. The course-based ideological and political education of which is a new task of higher education in the new era. The electrical and electronic engineering course of Ulster College of Shaanxi University of Science and Technology is taken as an example of joint Sino-foreign education to discuss how to combine professional characteristics, dig deeply into ideological and political elements, in order to realize the natural integration of course-based ideological and political education, as well as the imperceptibly cultivation of virtues, and promote the same direction development of knowledge imparting and value leading. This practice not only enriches the connotation of course-based ideological and political education, but also provides reference for the ideological and political education of Sino-foreign cooperative projects under the background of globalization.

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.

In the context of a full credit system, the course Foundations of Mathematics for Artificial Intelligence faces the challenge of linking foundational mathematical knowledge to real-world engineering problems. This paper introduces the teaching philosophy of “integration of theory and practice” and the model of “interest inspiration-project progression-AI coaching-classroom competition”, supported by diverse evaluation mechanisms to ensure course objectives are met. Curriculum reform has led to excellent student performance, with an average score of 3.61, far surpassing the 3-point graduation benchmark, and a 93% pass rate. Surveys show that 100% of students appreciate the course content design and its comprehensive assessment system. Additionally, 92% believe the course model significantly enhanced their learning efficiency.