Background: As a component of the motor’s electromechanical energy conversion, the winding is very important. When the winding design is improper, the harmonic content is increased, which leads to an increase in the motor loss and torque ripple. Therefore, winding design is very important for motor design. Objective: The effect of winding forms on the induction motor is analysed in this paper. The variation in performance parameters of different winding forms is invesitgated. Methods: This paper takes an 11 kW, 1500 r/min induction motor as the research object; the two- dimensional transient field finite element model has been established to calculate the basic parameters, which are then compared with the experimental data to verify the correctness of the model. Finally, the magnetizing reactance, starting performance, stator leakage reactance, various losses, and efficiency under different winding forms are calculated and compared. Results: When the motor adopts double-layer winding, the magnetizing reactance, stator slot leakage reactance, and harmonic leakage reactance are less than those of the motor with single-layer winding by 13.47%, 25.37% and 20.97%, respectively, while the end-leakage reactance, starting torque, and starting current rotor copper loss are higher than those of the motor with single-layer winding by 7.14%, 9.52%, 18.54%, and 15.23%, respectively. The core loss and stator copper loss of motors with different winding forms are almost unchanged. The efficiency of the motor with single-layer winding is higher than that of the motor with double-layer winding by 1.22%. Conclusion: When the winding form is different, the magnetizing reactance, stator slot leakage reactance, harmonic leakage reactance, and end-leakage reactance are different. Leakage reactance is the main factor that causes a difference in starting torque and starting current between the two winding forms. The core loss and stator copper loss of motor with different winding forms are almost unchanged, while the difference in the rotor copper loss is obvious. In addition, the efficiency of the motor with single-layer winding is higher than that of the motor with double-layer winding.

High speed motors are widely used in industrial applications owing to their unique features, such as compact framework, high performance, and high reliability. Based on the finite volume method and numerical heat transfer theory, this study establishes a stator-air gap-rotor model, and the flow and heat transfer in the air gap between the stator and rotor in a high speed motor are investigated with high pressure natural gas as the cooling medium. Meanwhile, the ?radial tri-vortex partition, alternating axial distribution? feature of the turbulent Taylor-Couette vortex in the air gap of the motor is determined. Then, the optimal structural parameters which can realize the heat transfer enhancement of motor air gap are obtained. Finally, an optical fiber grating temperature measurement system based on the wavelength division multiplexing technology is utilized to attain the temperature distributions on the stator and rotor surfaces. The simulation results are compared with the experimental data to evaluate the simulation method?s precision.

This paper combines the traditional hanging test and corrosion sensor technology to study and explore the corrosion law and its mechanism of Q235 carbon steel in industrial atmospheric environment. The results show that Hangzhou’s industrial atmospheric corrosion is a constantly changing and highly variable corrosion intensity process with the corrosive strength shows a seasonal trend. Through the analysis of weight loss, macroscopic corrosion morphology, microscopic corrosion morphology and elemental distribution, it showed that the traditional hanging test results and sensor test results had a good correspondence, indicating that the sensor values can be used to study the corrosion of the outdoor samples.

An expanded-sandwich system is a nonlinear extended block-oriented system in which memoryless elements in conventional block-oriented systems are displaced by memory submodels. Expanded-sandwich system identification has received extensive attention in recent years due to the powerful ability of these systems to describe actual industrial systems. This study proposes a novel recursive identification algorithm for an expanded-sandwich system, in which an estimator is developed on the basis of parameter identification error data rather than the traditional prediction error output information. In this scheme, a filter is introduced to extract the available system information based on miserly structure layout, and some intermediate variables are designed using filtered vectors. According to the developed intermediate variables, the parameter identification error data can be obtained. Thereafter, an adaptive estimator is established by integrating the identification error data compared with the classic adaptive estimator based on the prediction error output information. Thus, the design framework introduced in this research provides a new perspective for the design of identification algorithms. Under a general continuous excitation condition, the parameter estimation values can converge to the true values. Finally, experimental results and illustrative examples indicate the availability and usefulness of the proposed method.

In the complex environment of the substation, small objects such as long-distance cigarettes, cigarette boxes, and lighters have few imaging pixels and lack texture information, making it difficult for convolutional neural networks to extract small object features. In the case of multiple targets, the missed detection rate and false detection rate of small targets are high, and the fusion of model features is insufficient, making it difficult to accurately identify and detect. Aiming at the above problems, a multi-scale small target detection algorithm is proposed. In the prediction part of the network, a more effective decoupling head is designed. In addition, shallow features are introduced to improve the feature pyramid, extract small target features, increase the correlation between multiple targets, and prevent the loss of small target feature information. At the same time, a multi-layer attention mechanism is embedded in the backbone network to increase the regional features of invisible small targets and reduce the missed detection rate. In the post-processing stage, the Focal Loss loss function is introduced to increase the model's learning of positive sample targets and further reduce the rate of missed detection and false detection. The experimental results show that the method achieves a 𝑚𝐴𝑃@. 5: .95 of 0.6350 on the homemade smoking dataset, and 𝑚𝐴𝑃@0.5 achieves 0.9569. For the self-made multi-scene and multi-scale smoking data set, this model has advantages in detection accuracy compared with the current excellent target detection models such as YOLOX and YOLOv5. The experimental results show that the model method can realize the identification and detection of small targets such as cigarettes and lighters under multi-scale targets, which has a certain reference value for anti-smoking measures.

In order to improve the reliability of power grid operation, it is necessary to analyze the operating state of the power grid under the N-1 fault. In this paper, a method of searching cut sets is proposed to find the transmission interfaces after the N-1 fault occurred and to classify them. The thermal stability limit of the transmission interface is set to the simultaneously decreasing or increasing power flow that the bottleneck equipment is just fully loaded after a fault occurred. A provincial power grid case proves the validity and accuracy of the proposed method.