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Precise current decoupling is required to achieve good control performance in the vector control of permanent magnet synchronous motor (PMSM), but traditional decoupling methods are unable to solve the problem of poor current decoupling effectiveness when the system inductance parameters are mismatched. Therefore, a decoupling control method of PMSM based on improved nonlinear extended state observer (INESO) is proposed in this paper. Firstly, a smooth, differentiable nonlinear function is used to replace the traditional fal function, which overcomes the problem of discontinuous gain changes. Secondly, a time-varying gain is designed to address the problem of reduced observer performance due to differential peak values in traditional NESO. Finally, the stability of INESO and the current loop closed-loop system is proven, followed by simulation and experimental verification. The simulation and experimental results show the proposed method effectively observes and compensates for system uncertainties, including parameter perturbations, external disturbances, and unmodeled dynamics. This method achieves complete decoupling of d and q -axes currents, while suppressing interference and enhancing the control performance of PMSM. The proposed INESO decoupling method reduces the d -axis current fluctuation about 96% and 90% when the load changes abruptly under motor inductance parameter mismatch, and can reduce q -axis current fluctuation about 88% and 78% when the d -axis current steps, respectively, compared with the conventional Current Feedback Decoupling Control (CFDC) and CDDC decoupling methods. [ABSTRACT FROM AUTHOR]

This study addresses the challenges faced by coal-fired power plants in adapting to energy fluctuations following the integration of renewable energy sources into the power grid. The flexible operation of thermal power plants has become a focal point in academic research. A numerical model of a coal-fired power plant was developed in this study using the Long Short-Term Memory (LSTM) algorithm and the Particle Swarm Optimization (PSO) algorithm based on actual operation data analysis. The combined PSO-LSTM approach improved the accuracy of the model by optimizing parameters. Validation of the model was performed using a Dymola physical simulation model, demonstrating that the PSO-LSTM coupled numerical model accurately simulates coal-fired power plant operations with a goodness of fit reaching 0.998. Overall system performance for comprehensively evaluating the rate and accuracy of unit operation is proposed. Furthermore, the model's capability to simulate the load variation process of automatic generation control (AGC) under different load command groups was assessed, aiding in optimizing the best load command group. Optimization experiments show that the performance index of output power is optimal within the experimental range when the set load starts and stops are the same and the power of load command γ = 1.8. Specifically, the 50–75% Turbine Heat Acceptance (THA) load rise process enhanced the overall system performance index by 55.1%, while the 75–50% THA load fall process improved the overall system performance index by 54.2%. These findings highlight the effectiveness of the PSO-LSTM approach in optimizing thermal power plant operations and enhancing system performance under varying load conditions. [ABSTRACT FROM AUTHOR]

The detection of surface defects in metal materials has been a challenging issue in the industrial domain. The existing algorithms for metal surface defect detection are limited to a few specific types of defects and exhibit low performance with detection of defects of varying scales. A novel detection method based on the Information Enhancement YOLOv5 Network (IE-YOLOv5) for surface defects in metal parts is proposed, to realize efficient detection, which introduces a lightweight Federated Fusion Slim Neck module (FF-Slim-Neck) and a Parameter-free Spatial Attention mechanism (PSA) in YOLOv5 network. Comparative experiments were conducted using the NEU-DET dataset. The experimental results indicate that the proposed algorithm for detecting defects on metal surfaces achieves an average precision of 96.7% when identifying six different types of surface imperfections: crazing, inclusions, patches, pitting, scaling, and scratches. This represents a 2.4% enhancement in precision compared to the YOLOv5 algorithm. The measured processing velocity of this approach stands at 46.17 frames per second (FPS), highlighting its remarkable qualities of resilience, precision, and real-time capability. [ABSTRACT FROM AUTHOR]

To improve the performance and efficiency of ordinary galloping energy harvesters (GEHs), this paper proposes a GEH with a striped bluff body. The fluid-structure coupling simulation of the bluff body and the oscillator of the energy harvester was carried out using COMSOL Multiphysics. The cloud diagram in the flow field, tip displacement at the tip of the beam, lifting and dragging force, and trace and frequency response of the harvester are analyzed. Simulation results show that the GEH with a striped bluff body has the characteristics of rising frequency and falling peak compared with the ordinary GEH, which can be locked at a fixed peak to form a stable periodic response. Experimental results show that the striped bluff body energy harvester displays better energy harvest performance. The output root mean square voltage can be increased by 119.2% at 2 m/s wind speed, which means that the galloping harvester with a striped bluff body has more stable dynamic characteristics and shows better energy harvest performance. [ABSTRACT FROM AUTHOR]

A one-pot and green synthesis methodology was successfully designed to prepare nitrogen-doped carbon (NC) cohered Fe3O4 nanoparticles with rich oxygen vacancies (Fe3O4-OVs/NC). The preparation was achieved via cold-atmospheric-pressure air plasma using Fe2O3 nanoparticles as the only precursor, and pyridine as the carbon and nitrogen source. Systematic characterization results of the as-prepared Fe3O4-OVs/NC confirmed the transition from Fe2O3 to Fe3O4, along with the generation of oxygen vacancies, while preserving the original needle-like morphology of Fe2O3. Moreover, the results indicated the formation of the NC attaching to the surface of the formed Fe3O4 nanoparticles with a weight percent of ~13.6%. The synthesized nanocomposite was further employed as a heterogeneous Fenton catalyst to remove phenol from an aqueous solution. The material has shown excellent catalytic activity and stability, demonstrating a promising application for wastewater treatment. [ABSTRACT FROM AUTHOR]