The running state of lithium batteries has the problems of insufficient accuracy of ontology state estimation and high difficulty of battery pack fault state diagnosis. A joint state estimation method based on electrothermal coupling model considering the influence of multiple factors is proposed, and a multi-sensor fault diagnosis method based on detection window and correlation coefficient is designed. For the state of lithium battery, firstly, the electrothermal coupling model of lithium battery is constructed based on the equivalent circuit model method. Secondly, the mechanism of joint estimation of state of charge (SOC) and state of health (SOH) is analyzed. Using extended Kalman filter (EKF) and particle filter (PF), combined with online parameter identification method, an online joint estimation model covering the whole life cycle of lithium battery is constructed to achieve accurate joint state estimation. For the battery pack fault state, the multi-sensor fault diagnosis method based on detection window and correlation coefficient realizes the accurate diagnosis and location of short circuit and open circuit faults. The experimental results verify the effectiveness of the proposed method, which can accurately reflect the operation state of the battery, and has certain engineering application value.

Solving complex engineering problems is one of the core abilities that students of engineering education in domestic universities should possess. The elements of complex engineering problems are analyzed. Taking the Internet of Things course as an example, the knowledge structure is analyzed in this course. From diversified teaching mode and curriculum education reform which includes teaching philosophy, teaching effect, teaching method and curriculum assessment, the exploration of teaching mode which is driven by case seminar format is proposed to solve complex engineering problems, cultivating the ability of students to solve problems by using basic theoretical knowledge in complex engineering practice.

In order to solve the problems that the magnetomotive force angles of parallel branches of each phase are different in the rotor single pole-changing equal-turn winding scheme of the brushless doubly-fed generator, and the current amplitudes of rotor winding branches are different resulting in the percentages of the magnetomotive force changing with the load and the reduction of the wire utilization, the unequal-turn design scheme and improved scheme for reversing all the coils of the three self-shorted branches to the corresponding connecting line are proposed. The generator operating characteristics of equal-turn, unequal-turn and improved scheme are simulated based on the finite element. Results show that the unequal-turn scheme reduces the harmonic contents, cuts down the difference among the current amplitudes of parallel branches and improves the waveform quality. In the improved scheme, the rotor branches waveforms quality is good, the current amplitudes are equal, the wire utilization rates are improved, the harmonic contents are furtherly reduced, and the magnetomotive force are fixed, which is easy for the generator control.

This paper introduces the analysis process of a 500 kV substation DC system inter-pole short circuit fault. In order to deeply explore this special kind of DC insulation fault, based on the balance bridge principle of the DC insulation monitor, a circuit model of the DC inter-pole short circuit is established, and the process of obtaining the short circuit resistance of mutual short circuit is given. By calculating and analyzing the relationship between the control bus voltage and the short circuit resistance during DC inter-pole short circuit, the characteristics of DC inter-pole short circuit are summarized to provide reference for handling similar DC system faults.

In the context of professional certification in engineering education, to ensure the cultivation of students’ practical abilities and the enhancement of engineering qualities, while deeply implementing the outcome based education (OBE) teaching philosophy, a questionnaire survey is designed for the newly established course “comprehensive training in control theory” to collect students’ opinions and suggestions. Based on the analysis of survey results and the achievement of course objectives, combined with quality feedback channels such as discussions with instructors, this paper discusses continuous improvement measures for the practical course “comprehensive training in control theory” in terms of teaching content, teaching methods, assessment, and management. The aim is to better leverage the role of the course in cultivating practical and innovative abilities of students majoring in automation.

The clustering of distribution networks optimizes resource allocation and achieves load balancing through node partitioning. Existing clustering indicators primarily rely on modularity and power balance metrics, neglecting the impact of electric vehicle (EV) schedulable characteristics on distribution network flexibility. To address this, a new sub-indicator, which is bilateral EV schedulable capacity matching load demand and EV response, is defined, and a comprehensive indicator is constructed using a combined weighting method. Simulations based on the IEEE 33-node system are conducted with various indicator types, EV penetration levels, and time period scenarios. By comparatively analyzing the impacts of these factors on clustering results, the practicality and effectiveness of the proposed method are validated.

In order to meet the requirements of the scenario of power supply and power protection in the power system, and improve the emergency response capability and power supply reliability of the system, this paper applies a number of key technologies and methods such as compact weight reduction of transformer, shock absorption and isolation of equipment, verification of mechanical impact resistance, anti-corrosion of cabin roof, rapid layout of lightning protection device, modular design of secondary equipment and skid-mounted mobile substation digital twin. A large-capacity skid-mounted mobile intelligent substation with a main transformer capacity of 63 MV∙A and a voltage level of 110 kV is designed. Taking the large-capacity skid-mounted mobile intelligent substation that is actually applied in a new project of 110 kV temporary substation in Leshan City, Sichuan Province as an example, the effectiveness of this design is verified. The practice shows that this kind of power equipment has good promotion value and application prospects.

Modal analysis is the basis for studying the torsional vibration characteristics of the generator set shaft system. Current researches mainly focus on modal analysis under conventional shaft system constraints or the characteristic analysis of key parts of the shaft system under certain operating conditions. However, there is insufficient research on the impact of actual operating conditions on the modal characteristics of the shaft system. Taking the shaft system of a 600 MW steam turbine generator set as the research object, firstly, a three-dimensional model of the shaft system is established using the family table parameterization function of Creo modeling software. Then, theoretical analysis and finite element simulation are performed on the conventional natural modes, rotational speed pre-stress modes, thermal pre-stress modes, and modes under the combined effect of rotational speed and thermal pre-stress. The simulation results show that when rotational speed or thermal pre-stress acts separately, the natural frequencies of the shaft system decrease. When both rotational speed and thermal pre-stress act together, the natural frequencies of the shaft system are lower than those under no pre-stress conditions. However, under the premise that the rotational speed is at the nominal operating speed, a phenomenon is observed where the natural frequencies increase gradually with the rise in temperature, compared with the case where only temperature pre-stress is applied. This provides a reference for the design of the shaft system considering actual operating conditions.

Based on the 2×660 MW units of a power plant in Ximeng area, this paper studies the problem of power system subsynchronous oscillation/resonance, and puts forward the joint suppression measure based on supplementary excitation damping control (SEDC) and generator terminal subsynchronous damping control (GTSDC). The mechanism of the two systems is verified by experiments, and the oscillation attenuation characteristics of the independent scheme and the joint scheme under different working conditions are studied. The results show that the joint suppression measure based on SEDC and GTSDC achieves efficient collaborative control through parameter matching and multi time scale coordination, and significantly improves the suppression effect and response speed. Two actual fault cases verify the effectiveness and reliability of the joint suppression measure. The research also found that the sig-nal-to-noise ratio suppression effect of the speed signal is very important, so the suggestions of optimizing the reliability of the speed measurement system are put forward. The reseatch of this paper shows that the joint suppression measure based on SEDC and GTSDC can effectively solve the problem of unit subsyn-chronous oscillation, improve the dynamic stability rate of power system, and provide a technical refer-ence for the treatment of subsynchronous oscillation of similar units.

The current methods for predicting the state of health of lithium batteries often suffer from low accuracy. This paper introduces a method for state of health prediction using a seagull optimization algorithm optimized deep extreme learning machine. Key health feature parameters, such as constant voltage charging and discharging times during battery cycles, are selected and their correlation with the battery state of health is analyzed using Pearson correlation. The proposed model predicts subsequent state of health values by learning from samples. Experiments conducted with battery data compare the proposed method with single extreme learning machine, single deep extreme learning machine, and other literature. Evaluation metrics, including maximum absolute error and root mean square error, demonstrate that the seagull optimization algorithm optimized deep extreme learning machine model achieves higher accuracy and faster prediction times, with errors below 1.1%, indicating superior prediction accuracy and applicability.