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Based on the context of increasingly serious aging problem in China, the psychological characteristics of elders in using public self-service facilities and the development status and the future trend of public self-service ticketing service. The approach is analysing physiological and psychological characteristics, education level of the elderly and studying its characteristics of consumer psychology and regional cultural characteristics profoundly before conducting comprehensive analysis and research in combination with the interface features of public self-service ticketing machine. The interface design will be more personalized, intelligent, regional and international. Strategies of caring for the elderly in the regional public self-service facility interface design innovation develops the concept of taking care of the elderly in the entire region as an indispensable people-benefiting optimization system in the modern social services.

The widespread deployment of distributed generation (DG) has significantly impacted the planning and operation of current distribution networks. The environmental benefits and the reduced installation cost have been the primary drivers for the investment in large-scale wind farms and photovoltaics (PVs). However, the distribution network operators (DNOs) face the challenge of conductor upgrade and selection problems due to the increasing capacity of DG. In this paper, a hybrid optimization approach is introduced to solve the optimal conductor size selection (CSS) problem in the distribution network with high penetration of DGs. An adaptive genetic algorithm (AGA) is employed as the primary optimization strategy to find the optimal conductor sizes for distribution networks. The aim of the proposed approach is to minimize the sum of life-cycle cost (LCC) of the selected conductor and the total energy procurement cost during the expected operation periods. Alternating current optimal power flow (AC-OPF) analysis is applied as the secondary optimization strategy to capture the economic dispatch (ED) and return the results to the primary optimization process when a certain conductor arrangement is assigned by AGA. The effectiveness of the proposed algorithm for optimal CSS is validated through simulations on modified IEEE 33-bus and IEEE 69-bus distribution systems.

Flywheel energy storage systems (FESS) are technologies that use a rotating flywheel to store and release energy. Permanent magnet synchronous machines (PMSMs) are commonly used in FESS due to their high torque and power densities. One of the critical requirements for PMSMs in FESS is low torque ripple. Therefore, a PMSM with eccentric permanent magnets is proposed and analyzed in this article to reduce torque ripple. Cogging torque, a significant contributor to torque ripple, is investigated by a combination of finite element analysis and the analytical method. An integer-slot distribution winding structure is adopted to reduce vibration and noise. Moreover, the effects of eccentric permanent magnets and harmonic injection on the cogging torque are analyzed and compared. In addition, the electromagnetic performance is analyzed, and the torque ripple is found to be 3.1%. Finally, a prototype is built and tested, yielding a torque ripple of 3.9%, to verify the theoretical analysis.

Under the background of energy transition, the Integrated Energy System (IES) of the park has become a key carrier for enhancing the consumption capacity of renewable energy due to its multi-energy complementary characteristics. However, the high proportion of wind and solar resource access and the fluctuation of diverse loads have led to the system facing dual uncertainty challenges, and traditional optimization methods are difficult to adapt to the dynamic and complex dispatching requirements. To this end, this paper proposes a new dynamic energy management method based on Deep Reinforcement Learning (DRL) and constructs an IES hybrid integer nonlinear programming model including wind power, photovoltaic, combined heat and power generation, and storage of electric heat energy, with the goal of minimizing the operating cost of the system. By expressing the dispatching process as a Markov decision process, a state space covering wind and solar output, multiple loads and energy storage states is defined, a continuous action space for unit output and energy storage control is constructed, and a reward function integrating economic cost and the penalty for renewable energy consumption is designed. The Deep Deterministic Policy Gradient (DDPG) and Deep Q-Network (DQN) algorithms were adopted to achieve policy optimization. This study is based on simulation rather than experimental validation, which aligns with the exploratory scope of this research. The simulation results show that the DDPG algorithm achieves an average weekly operating cost of 532,424 yuan in the continuous action space scheduling, which is 8.6% lower than that of the DQN algorithm, and the standard deviation of the cost is reduced by 19.5%, indicating better robustness. Under the fluctuation of 10% to 30% on the source-load side, the DQN algorithm still maintains a cost fluctuation of less than 4.5%, highlighting the strong adaptability of DRL to uncertain environments. Therefore, this method has significant theoretical and practical value for promoting the intelligent transformation of the energy system.

With the accelerated transition of the global energy structure towards decarbonization, the share of PV power generation in the power system continues to rise. IEA predicts PV will account for 80% of new global renewable installations during 2025–2030. However, latent faults emerging from the long-term operation of photovoltaic (PV) power plants significantly compromise their operational efficiency. The existing EL detection methods in PV plants face challenges including grain boundary interference, probe band artifacts, non-uniform luminescence, and complex backgrounds, which elevate the risk of missing small defects. In this paper, we propose a high-precision defect detection method based on BiFDRep-YOLOv8n for small target defects in photovoltaic (PV) power plants, aiming to improve the detection accuracy and real-time performance and to provide an efficient solution for the intelligent detection of PV power plants. Firstly, the visual transformer RepViT is constructed as the backbone network, based on the dual-path mechanism of Token Mixer and Channel Mixer, to achieve local feature extraction and global information modeling, and combined with the structural reparameterization technique, to enhance the sensitivity of detecting small defects. Secondly, for the multi-scale characteristics of defects, the neck network is optimized by introducing a bidirectional weighted feature pyramid network (BiFPN), which adopts an adaptive weight allocation strategy to enhance feature fusion and improve the characterization of defects at different scales. Finally, the detection head part uses DyHead-DCNv3, which combines the triple attention mechanism of scale, space, and task awareness, and introduces deformable convolution (DCNv3) to improve the modeling capability and detection accuracy of irregular defects.

The increasing electrification of urban transportation has formulated a tightly coupled energy-mobility nexus. Under extreme disaster events or grid faults, rapidly restoring power supply capacity and re-dispatching shared electric vehicle (EV) fleets are critical for enhancing system resilience. Existing co-optimization methods face the curse of dimensionality when dealing with high-dimensional discrete grid reconfigurations and continuous spatio-temporal EV queuing dynamics. While multi-agent deep reinforcement learning (MADRL) offers real-time responsiveness, it inherently struggles to satisfy strict physical constraints, frequently generating infeasible and unsafe actions. To bridge this gap, this paper proposes a heuristic-guided safe multi-agent reinforcement learning (Safe-MADRL) framework for the resilient dispatch of the energy-mobility nexus. Instead of relying solely on black-box neural networks, the framework structurally embeds physical models and heuristic solvers into the learning loop. A quantum particle swarm optimization (QPSO) algorithm acts as a heuristic action refiner to ensure that grid topology actions strictly comply with non-linear power flow and voltage constraints. Simultaneously, a mixed-integer linear programming (MILP) model coupled with a single-queue multi-server (SQMS) model serves as a safety projection layer. This layer mathematically guarantees EV battery energy continuity and accurately quantifies spatio-temporal queuing delays at charging stations. Case studies on a coupled IEEE 33-node distribution system and a regional transportation network demonstrate that the proposed Safe-MADRL framework achieves zero physical violations during training and significantly outperforms traditional mathematical optimization and pure learning-based methods in computational efficiency, system power loss reduction, and overall operational economy.

The fuel properties of the solid product obtained from hydrothermal carbonization (HTC) are substantially influenced by the interaction reactions of the lignocellulose components. This study focused on the interaction reactions of cellulose and hemicellulose (represented by glucose and xylose, respectively) and the formation mechanism of the hydrochar through experiments and density functional theory methods. Results indicated that furfural was the main intermediate product of xylose and then converted into benzenes, which were seldom produced from the hydrothermal conversion of glucose. When glucose and xylose were co‐carbonized hydrothermally, more benzenes were generated because glucose and xylose exhibited a synergistic effect in forming alkenes, which were then cracked remarkably into C2H2. Furthermore, C2H2 reacted with furan that transformed from furfural and formed benzenes. The aqueous product of co‐HTC also contained a high concentration of 5‐hydroxymethylfurfural (5‐HMF). The 5‐HMF could polymerize with benzenes and furfural to form insoluble furan−benzene polymers, gradually aggregating into organic micronucleus. Subsequently, 5‐HMF, furfural, and benzenes in the aqueous phase were immobilized on the surface of the organic micronucleus through surface binding sites, eventually causing the growth of organic micronucleus into hydrochar particles. The polymerization between furan−benzene polymers could also increase the aromatization degree of the hydrochar, thereby enhancing the energy density of the hydrochar. Formation mechanism of hydrochar from glucose mixed with xylose.

To address the vibration issues in bearingless interior permanent magnet synchronous motors (BIPMSMs) caused by rotor mass unbalance and inverter dead-time (DT) effects during operation, a vibration suppression strategy based on a Proportional–Integral–Resonant (PIR) controller is proposed. Firstly, the mathematical model of the BIPMSM is established, and the principle of suspension force generation is analyzed. Secondly, the mechanism underlying rotor vibration is theoretically investigated. Thirdly, a PIR controller is designed by connecting a modified Proportional–Resonant (PR) controller in parallel with a Proportional–Integral (PI) controller. The proposed controller combines the ideal PR controller’s characteristic of achieving infinite gain at the resonant frequency, enabling zero steady-state error tracking for sinusoidal signals at the resonant frequency. Finally, a vibration suppression system based on the PIR controller is constructed, and simulation experiments are conducted for verification. The simulation results show that the PIR controller effectively reduces both rotor mass unbalance vibration and DT vibration in the BIPMSM, while also suppressing current harmonics during the motor’s operation.

This paper proposes a double-three-phase permanent magnet fault-tolerant machine (DTP-PMFTM) with low short-circuit current for flywheel energy storage systems (FESS) to balance torque performance and short-circuit current suppression. The key innovation lies in its modular winding configuration that ensures electrical isolation between the two winding sets. First, the structural characteristics of the double three-phase windings are analyzed. Subsequently, the harmonic features of the resultant magnetomotive force (MMF) are systematically investigated. To verify the performance, the proposed machine is compared against a conventional winding structure as a baseline, focusing on key parameters such as output torque and short-circuit current. The experimental results demonstrate that the proposed machine achieves an average torque of approximately 14.7 N·m with a torque ripple of about 3.27%, a phase inductance of approximately 3.7 mH, and a short-circuit current of approximately 50.9 A. Crucially, compared to the conventional winding, the modular structure increases the phase inductance by about 32.1% and reduces the short-circuit current by 29.7%. Finally, an experimental platform is established to validate the performance of the machine.

To enhance the operational dependability of renewable energy power systems with high proportions, this study proposes a multi-timescale optimization strategy based on the inertia evaluation model. Firstly, the inertia evaluation model is established based on the factors influencing the inertia demand of the power system, and the concept of the inertia margin coefficient is introduced. Secondly, to address the uncertainties associated with sustainable energy output and the cost of carbon emissions, a multi-timescale optimization operation model is formulated for day-ahead, intraday, and real-time operations, aimed at economic optimization. The output status of each unit is obtained and adjusted in a timely manner in the next stage, while meeting the system’s inertia demand, to derive the final scheduling strategy. Lastly, a sensitivity analysis of the inertia margin coefficient is conducted through simulations to validate the effectiveness and cost-efficiency of the proposed scheduling strategy.