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.

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.

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.

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.

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.

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.

In grid-forming converters with weak overcurrent capability, it is generally necessary to introduce the current limiter to ensure fault current limiting. However, the synchronization stability of the system is weakened after triggering the current limiter, which is seldom comprehensively considered and addressed in existing studies. To this end, the phasor diagram method is used to reveal the fault current characteristics in this paper. Then, the adverse impact of the current limiter on the synchronization stability of the converter is analyzed based on the power angle characteristic curves and the extended equal area criterion. Thus, a virtual inertia current limiting method for grid-forming converters with enhanced synchronous stability is proposed, which simultaneously solves the fault current limiting and synchronous stability problems of converters by quantitatively controlling the virtual inertia. Finally, the correctness of the theoretical analysis and the effectiveness of the proposed method are verified by Matlab/Simulink simulation experiments.

The online monitoring system for converter transformers is a system that evaluates the transformer condition based on characteristic parameters. This system records various parameters during the operation of the converter transformer, including gas composition in the oil, SF₆ gas pressure in the bushings, and leakage current in the core clamps. Among these, the core clamp current is a crucial indicator for determining the grounding condition and insulation strength of the transformer core. However, the internal electromagnetic environment of a converter transformer is quite complex during operation, and due to the limitations of sensor precision and operational conditions, the traditional threshold-based abnormal diagnosis methods for core clamp current have a high false alarm rate, posing challenges for refined operation and maintenance. This paper establishes a neural network-based core clamp current prediction model and uses the error between the predicted values and the online values as the observation metric. An abnormal diagnosis method for core clamp current based on the control chart method is proposed. The feasibility of this method is validated using core clamp current data from a ±800kV converter station. The experimental results show that the proposed method can avoid false alarms and accurately diagnose true alarms.

To improve the forcasting accuracy of short and medium term loads, this article proposes an autoregressive moving average model based on Markov residual correction. The autoregressive moving average model is used to predict the load and calculate the residual, and the Markov residual correction algorithm is used to correct the prediction results. The engineering case verification shows that the average absolute error of load forecasting obtained by the autoregressive moving average model is 13.67%. After Markov residual correction, the average absolute error of load forecasting is 6.912%, and the prediction accuracy is improved by 49.4%. It is concluded that the load forecasting model proposed in this article has certain significance for guiding industrial users in short and medium term loads forecasting.