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.

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.

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.

The assignment of work orders is an essential part of the power operation and maintenance management system. Timely and accurate assignment methods can effectively improve the efficiency of work order circulation. Based on in-depth analysis of the characteristics of work order management in the field of power operation and maintenance, this article proposes an automatic assignment model for power operation and maintenance work orders based on fit degree. The model adopts criteria importance though intercrieria correlation (CRITIC) weighting method and fuzzy comprehensive evaluation to calculate the compatibility between operation and maintenance personnel and work orders, determine the optimal candidate for work order assignment, and then achieve automatic assignment of work orders. The practical application results show that compared to manual assignment, this automatic assignment model can effectively improve the efficiency and accuracy of work order assignment.

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.

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.

This paper proposes a DC distribution network fault location method combining current integral variation trend and temporal convolutional network (TCN)-support vector machine (SVM), to distinguish and locate DC distribution network faults, and lay the foundation for DC distribution network protection. Firstly, the integral sequence of fault current is calculated, and the integral sequence is decomposed by variational mode decomposition (VMD) algorithm. The eigenvalues of the decomposed high frequency intrinsic mode function are used as the input eigenvectors of the combination model of TCN and SVM, and the fault lines are located and the fault types are determined. The simulation results show that the scheme can not only locate the fault line quickly and identify different faults accurately, but also has good adaptability and certain anti-interference ability.

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.

Against the backdrop of the continuous development of new power system, various regions across the country are actively carrying out new energy storage construction to enhance the regulation capacity of the power system and meet the peak shaving and demand of the power grid. With the characteristics of bidirectional transmission and fast response speed, it is a key issue that should be considered in the site selection stage of energy storage planning, which involves how to effectively improve the security of the power grid and enhance the ability to resist the impact of faults after the new energy storage is access to the power system. A multi-objective decision-making model for energy storage site selection is constructed to address the impact of new energy storage access on the vulnerability of partitioned power grids. Faced with the shortcomings of traditional power grid vulnerability analysis methods, a new vulnerability assessment method based on k-core decomposition is proposed. The system vulnerability indicators under different typical operation scenarios after energy storage access to the power grid are taken as decision-making sub-targets, and the technique for order preference by similarity to an ideal solution (TOPSIS) decision method is used to comprehensively evaluate the optimal solution of energy storage target access point. The rationality and effectiveness of the proposed vulnerability assessment method and the energy storage site selection method are verified through the analysis of the IEEE 39-node system.