Topology Optimization Design Method for Acoustic Imaging Array of Power Equipment.

Acoustic imaging technology has the advantages of non-contact and intuitive positioning. It is suitable for the rapid positioning of defects such as the mechanical loosening, discharge, and DC bias of power equipment. However, the existing research lacks the optimization design of microphone array topology. The acoustic frequency domain characteristics of typical power equipment are elaborately sorted out. After that, the cut-off frequencies of acoustic imaging instruments are determined, to meet the needs of the full bandwidth test requirements. Through a simulation calculation, the circular array is demonstrated to be the optimal shape. And the design parameters affect the imaging performance of the array to varying degrees, indicating that it is difficult to obtain the optimal array topology by an exhaustive method. Aimed at the complex working conditions of power equipment, a topology optimization design method of an acoustic imaging array for power equipment is proposed, and the global optimal solution of microphone array topology is obtained. Compared with the original array, the imaging performance of the improved LF and HF array is promoted by 54% and 49%, respectively. Combined with the simulation analysis and laboratory test, it is verified that the improved array can not only accurately locate the single sound source but also accurately identify the main sound source from the interference of the contiguous sound source.

Optimized OTSU Segmentation Algorithm-Based Temperature Feature Extraction Method for Infrared Images of Electrical Equipment.

Infrared image processing is an effective method for diagnosing faults in electrical equipment, in which target device segmentation and temperature feature extraction are key steps. Target device segmentation separates the device to be diagnosed from the image, while temperature feature extraction analyzes whether the device is overheating and has potential faults. However, the segmentation of infrared images of electrical equipment is slow due to issues such as high computational complexity, and the temperature information extracted lacks accuracy due to the insufficient consideration of the non-linear relationship between the image grayscale and temperature. Therefore, in this study, we propose an optimized maximum between-class variance thresholding method (OTSU) segmentation algorithm based on the Gray Wolf Optimization (GWO) algorithm, which accelerates the segmentation speed by optimizing the threshold determination process using OTSU. The experimental results show that compared to the non-optimized method, the optimized segmentation method increases the threshold calculation time by more than 83.99% while maintaining similar segmentation results. Based on this, to address the issue of insufficient accuracy in temperature feature extraction, we propose a temperature value extraction method for infrared images based on the K-nearest neighbor (KNN) algorithm. The experimental results demonstrate that compared to traditional linear methods, this method achieves a 73.68% improvement in the maximum residual absolute value of the extracted temperature values and a 78.95% improvement in the average residual absolute value.

TGLFusion: A Temperature-Guided Lightweight Fusion Method for Infrared and Visible Images.

The fusion of infrared and visible images is a well-researched task in computer vision. These fusion methods create fused images replacing the manual observation of single sensor image, often deployed on edge devices for real-time processing. However, there is an issue of information imbalance between infrared and visible images. Existing methods often fail to emphasize temperature and edge texture information, potentially leading to misinterpretations. Moreover, these methods are computationally complex, and challenging for edge device adaptation. This paper proposes a method that calculates the distribution proportion of infrared pixel values, allocating fusion weights to adaptively highlight key information. It introduces a weight allocation mechanism and MobileBlock with a multispectral information complementary module, innovations which strengthened the model's fusion capabilities, made it more lightweight, and ensured information compensation. Training involves a temperature-color-perception loss function, enabling adaptive weight allocation based on image pair information. Experimental results show superiority over mainstream fusion methods, particularly in the electric power equipment scene and publicly available datasets.

Research on Miniaturized UHF Sensing Technology for PD Detection in Power Equipment Based on Symmetric Cut Theory.

In answer to the demand for high sensitivity and miniaturization of ultra-high frequency (UHF) sensors for partial discharge (PD) detection in power equipment, this paper proposes research on miniaturized UHF-sensing technology for PD detection in power equipment based on symmetric cut theory. The symmetric cut theory is applied for the first time to the miniaturization of PD UHF sensors for power equipment. A planar monopole UHF sensor with a size of only 70 mm × 70 mm × 1.6 mm is developed using an exponential asymptotic feed line approach, which is a 50% size reduction. The frequency-response characteristics of the sensor are simulated, optimized and tested; the results show that the standing wave ratio of the sensor developed in this paper is less than 2 in the frequency band from 427 MHz to 1.54 GHz, and less than 5 in the frequency band from 300 MHz to 1.95 GHz; in the 300 MHz~1.5 GHz band; the maximum and average gains of the sensor E-plane are 4.76 dB and 1.02 dB, respectively. Finally, the PD simulation experiment platform for power equipment is built to test the sensor's sensing performance; the results show that the sensor can effectively detect the PD signals; the sensing sensitivity is improved by about 95% relative to an elliptical monopole UHF sensor.

Molecular Simulation Analysis of Polyurethane Molecular Structure under External Electric Field.

Polyurethane (PU) materials are extensively utilized in power equipment. This paper introduces a comprehensive evaluation method that combines electromagnetics and computational chemistry based on the Density Functional Theory (DFT) to elucidate the impact of external electric fields on the molecular structure of PU during electrical contact. The study focuses on the microstructural and molecular energy changes in the hard (HS) and soft (SS) segments of PU under the influence of an electric field of uniform intensity. Findings indicate that the total energy of HS molecules decreases markedly as the electric field intensity increases, accompanied by a significant rise in both the dipole moment and polarizability. Conversely, the total energy and polarizability of the SS molecules decrease, while the dipole moment experiences a slight increase. Under the influence of a strong electric field, HS molecules tend to stretch towards the extremities of the main chain, leading to structural instability and the cleavage of hydroxyl O-H bonds. Meanwhile, the carbon chain of the SS molecules twists towards the center under the electric field, with no chemical bond rupture observed. At an electric field intensity of 8.227 V/nm, the HOMO-LUMO gap of the HS molecule narrows sharply, signifying a rapid decline in the molecular structure stability, corroborated by infrared spectroscopy analysis. These findings offer theoretical insights and guidance for the modification of PU materials in power equipment applications.

Design and Optimization of Multi-Stage TMR Sensors for Power Equipment AC/DC Leakage Current Detection.

Tunnel magnetoresistance (TMR) can measure weak magnetic fields and has significant advantages for use in alternating current/direct current (AC/DC) leakage current sensors for power equipment; however, TMR current sensors are easily perturbed by external magnetic fields, and their measurement accuracy and measurement stability are limited in complex engineering application environments. To enhance the TMR sensor measurement performance, this paper proposes a new multi-stage TMR weak AC/DC sensor structure with high measurement sensitivity and anti-magnetic interference capability. The front-end magnetic measurement characteristics and interference immunity of the multi-stage TMR sensor are found to be closely related to the multi-stage ring size design via finite element simulation. The optimal size of the multipole magnetic ring is determined using an improved non-dominated ranking genetic algorithm (ACGWO-BP-NSGA-II) to derive the optimal sensor structure. Experimental results demonstrate that the newly designed multi-stage TMR current sensor has a measurement range of 60 mA, a fitting nonlinearity error of less than 1%, a measurement bandwidth of 0-80 kHz, a minimum AC measurement value of 85 µA and a minimum DC measurement value of 50 µA, as well as a strong external electromagnetic interference. The TMR sensor can effectively enhance measurement precision and stability in the presence of intense external electromagnetic interference.

Multi-Type Missing Imputation of Time-Series Power Equipment Monitoring Data Based on Moving Average Filter-Asymmetric Denoising Autoencoder.

Supervisory control and data acquisition (SCADA) systems are widely utilized in power equipment for condition monitoring. For the collected data, there generally exists a problem-missing data of different types and patterns. This leads to the poor quality and utilization difficulties of the collected data. To address this problem, this paper customizes methodology that combines an asymmetric denoising autoencoder (ADAE) and moving average filter (MAF) to perform accurate missing data imputation. First, convolution and gated recurrent unit (GRU) are applied to the encoder of the ADAE, while the decoder still utilizes the fully connected layers to form an asymmetric network structure. The ADAE extracts the local periodic and temporal features from monitoring data and then decodes the features to realize the imputation of the multi-type missing. On this basis, according to the continuity of power data in the time domain, the MAF is utilized to fuse the prior knowledge of the neighborhood of missing data to secondarily optimize the imputed data. Case studies reveal that the developed method achieves greater accuracy compared to existing models. This paper adopts experiments under different scenarios to justify that the MAF-ADAE method applies to actual power equipment monitoring data imputation.

Power Equipment Fault Diagnosis Method Based on Energy Spectrogram and Deep Learning.

With the development of industrial manufacturing intelligence, the role of rotating machinery in industrial production and life is more and more important. Aiming at the problems of the complex and changeable working environment of rolling bearings and limited computing ability, fault feature information cannot be effectively extracted, and the current deep learning model is difficult to be compatible with lightweight and high efficiency. Therefore, this paper proposes a fault detection method for power equipment based on an energy spectrum diagram and deep learning. Firstly, a novel two-dimensional time-frequency feature representation method and energy spectrum feature map based on wavelet packet transform is proposed, and an energy spectrum feature map dataset is made for subsequent diagnosis. This method can realize multi-resolution analysis, fully extract the feature information contained in the fault signal, and accelerate the convergence of the subsequent diagnosis model. Secondly, a lightweight residual dense convolutional neural network model (LR-DenseNet) is proposed. This model combines the advantages of residual learning and a dense connection, and can not only extract deep features more easily, but can also effectively use shallow features. Then, based on the lightweight residual dense convolutional neural network model, an LR-DenseSENet model is proposed. By introducing the transfer learning strategy and adding the channel domain, an attention mechanism is added to the channel feature fusion layer, with the accuracy of detection up to 99.4%, and the amount of parameter calculation greatly reduced to one-fifth of that of VGG. Finally, through an experimental analysis, it is verified that the fault detection model designed in this paper based on the combination of an energy spectrum feature map and LR-DenseSENet achieves a satisfactory detection effect.

Research on Blind Super-Resolution Technology for Infrared Images of Power Equipment Based on Compressed Sensing Theory.

Infrared images of power equipment play an important role in power equipment status monitoring and fault identification. Aiming to resolve the problems of low resolution and insufficient clarity in the application of infrared images, we propose a blind super-resolution algorithm based on the theory of compressed sensing. It includes an improved blur kernel estimation method combined with compressed sensing theory and an improved infrared image super-resolution reconstruction algorithm based on block compressed sensing theory. In the blur kernel estimation method, we propose a blur kernel estimation algorithm under the compressed sensing framework to realize the estimation of the blur kernel from low-resolution images. In the estimation process, we define a new Lw norm to constrain the gradient image in the iterative process by analyzing the significant edge intensity changes before and after the image is blurred. With the Lw norm, the salient edges can be selected and enhanced, the intermediate latent image generated by the iteration can move closer to the clear image, and the accuracy of the blur kernel estimation can be improved. For the super-resolution reconstruction algorithm, we introduce a blur matrix and a regular total variation term into the traditional compressed sensing model and design a two-step total variation sparse iteration (TwTVSI) algorithm. Therefore, while ensuring the computational efficiency, the boundary effect caused by the block processing inside the image is removed. In addition, the design of the TwTVSI algorithm can effectively process the super-resolution model of compressed sensing with a sparse dictionary, thereby breaking through the reconstruction performance limitation of the traditional regularized super-resolution method of compressed sensing due to the lack of sparseness in the signal transform domain. The final experimental results also verify the effectiveness of our blind super-resolution algorithm.

Blur Kernel Estimation and Non-Blind Super-Resolution for Power Equipment Infrared Images by Compressed Sensing and Adaptive Regularization.

Infrared sensing technology is more and more widely used in the construction of power Internet of Things. However, due to cost constraints, it is difficult to achieve the large-scale installation of high-precision infrared sensors. Therefore, we propose a blind super-resolution method for infrared images of power equipment to improve the imaging quality of low-cost infrared sensors. If the blur kernel estimation and non-blind super-resolution are performed at the same time, it is easy to produce sub-optimal results, so we chose to divide the blind super-resolution into two parts. First, we propose a blur kernel estimation method based on compressed sensing theory, which accurately estimates the blur kernel through low-resolution images. After estimating the blur kernel, we propose an adaptive regularization non-blind super-resolution method to achieve the high-quality reconstruction of high-resolution infrared images. According to the final experimental demonstration, the blind super-resolution method we proposed can effectively reconstruct low-resolution infrared images of power equipment. The reconstructed image has richer details and better visual effects, which can provide better conditions for the infrared diagnosis of the power system.

Multiscale Entropy Feature Extraction Method of Running Power Equipment Sound.

The equipment condition monitoring based on computer hearing is a new pattern recognition approach, and the system formed by it has the advantages of noncontact and strong early warning abilities. Extracting effective features from the sound data of the running power equipment help to improve the equipment monitoring accuracy. However, the sound of running equipment often has the characteristics of serious noise, non-linearity and instationary, which makes it difficult to extract features. To solve this problem, a feature extraction method based on the improved complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and multiscale improved permutation entropy (MIPE) is proposed. Firstly, the ICEEMDAN is utilized to obtain a group of intrinsic mode functions (IMFs) from the sound of running power equipment. The noise IMFs are then identified and eliminated through mutual information (MI) and mean mutual information (meanMI) of IMFs. Next, the normalized mutual information (norMI) and MIPE are calculated respectively, and norMI is utilized to weigh the corresponding MIPE result. Finally, based on the separability criterion, the weighted MIPE results are feature-dimensionally reduced to obtain the multiscale entropy feature of the sound. The experimental results show that the classification accuracies of the method under the conditions of no noise and 5 dB reach 96.7% and 89.9%, respectively. In practice, the proposed method has higher reliability and stability for the sound feature extraction of the running power equipment.

[Effects of workplace health promotion in a wind power equipment manufacturing factory].

Objective: To explore the effects of workplace health promotion in a wind power equipment manufacturing factory. Methods: Based on investigation of occupational hazard factors, personal protective equipment (PPE) application, occupational health management and healthy life style, health promotion strategy and intervention were implemented in this factory. The sample size was 56 for monitoring of occupational hazard factors before and after intervention, 283 and 259 for questionnaire before and after intervention. Results: After intervention, the qualified rate of workplace occupational hazard factors increased from 67.9% to 82.1%. The abnormal rate of occupational health surveillance among workers decreased from 24.7% to 11.6%. The rates of correct use of PPE increased from 39.6% to 80.7%. The rates of awareness of occupational health increased from 10.2% to 71.8%. The rates of awareness of chronic disease increased from 10.2% to 77.2%. Two-week consultation rate decreased from 6.4% to 4.2%. Smoking rate decreased from 12.4% to 10.4%. Conclusion: Workplace health promotion is effective measures for reducing occupational hazard factors exposure and improving workers' health.

Enhancing power equipment defect identification through multi-label classification methods.

Accurate identification and classification of equipment defects are essential for assessing the health of power equipment and making informed maintenance decisions. Traditional defect classification methods, which rely on subjective manual records and intricate defect descriptions, have proven to be inefficient. Existing approaches often evaluate equipment status solely based on defect grade. To enhance the precision of power equipment defect identification, we developed a multi-label classification dataset by compiling historical defect records. Furthermore, we assessed the performance of 11 established multi-label classification methods on this dataset, encompassing both traditional machine learning and deep learning methods. Experimental results reveal that methods considering label correlations exhibit significant performance advantages. By employing balanced loss functions, we effectively address the challenge of sample imbalance across various categories, thereby enhancing classification accuracy. Additionally, segmenting the power equipment defect classification task, which involves numerous labels, into label recall and ranking stages can substantially improve classification performance. The dataset we have created is available for further research by other scholars in the field.

Multivariate impedance spectroscopy method for the measuring of quality and status of electric power oils.

With the rapid development of power technology and the complexity of power system equipment, efficient and accurate assessment of the quality and condition of electric power equipment oil (EPEO) has become particularly critical. EPEO is an important factor to ensure the stable operation of power equipment, and its quality and state directly affect the safety and reliability of equipment. However, there are many challenges with traditional oil measuring techniques, which often rely on destructive testing, which not only increases maintenance costs, but can also cause damage to the equipment itself. In the face of these limitations, there is an urgent need to study new oil detection technologies and methods to meet the high standards of modern power systems for high efficiency, non-destructive and comprehensive analytical capabilities. In this paper, a new EPEO measuring technique based on multivariable impedance spectroscopy (MIS) is proposed. Through in-depth analysis of oil's impedance response characteristics under electric field excitation with different frequency., a new approach is provided for the comprehensive evaluation of oil's performance. MIS technology not only has the characteristics of non-destructive testing, ensuring the non-destructive measuring of EPEO, but also its rapid response and real-time analysis ability significantly improves the monitoring efficiency. Based on the proposed MIS detection method, a detection system and experimental prototype which can detect and evaluate the performance and quality of power oil more accurately are designed. Compared with the traditional measuring device, the measuring device utilized in this method can employ three variables. Specifically, it covers a frequency range for the detectable excitation signal spanning from 1 to 100 kHz, an amplitude range from 0.1 to 11.7 V, and a temperature range from -100 °C to 100 °C. The MIS detection method has the capability to identify a variety of parameters, including the dielectric constant, volume resistivity, and dielectric loss factor, among others. This method encompasses a broader spectrum of parameters compared to traditional detection methods, which typically focus on one or two detectable indicators. The correctness and feasibility of the proposed multivariable impedance spectrum detection technique are verified, which provides a new way for the comprehensive evaluation of oil's performance.

Relación de los estilos de liderazgo, cohesión grupal, potencia de equipo y rendimiento en jugadores de fútbol no profesionales / Relationship of Leadership Styles, Group Cohesion, Power Equipment and Performance in Non-Professional Soccer Players

Resumen El objetivo del presente trabajo fue analizar las relaciones entre tipos de liderazgo (tarea y social), cohesión, potencia de equipo y rendimiento en 334 futbolistas federados de las categorías Alevín, Cadete, Juvenil y Absoluta, con una edad media de 15.97 (DE = 3.31). Los cuestionarios utilizados fueron de Liderazgo auténtico (ALQ), tarea (LSS), Cohesión grupal (CG), Potencia de equipo (CPEA) y rendimiento, que mostraron índices de ajuste óptimos (χ2/DF = 1.313 p = 0; CFI = 0.997; TLI = 0.994; RMSEA = 0.032; SRMS = 0.0343). Los resultados indican que el estilo de liderazgo, especialmente liderazgo tarea (p = 0.37) influye indirectamente en el rendimiento a través de la CG (p = 0.17) y CPE (p = 0.21). Como conclusión, se propone la mejora del rendimiento grupal a partir de la optimización de modelos de conducta individuales.

Abstract The objective was to analyze the relationships between types of leadership (task and social), cohesion, team potency and performance in 334 federated players of the categories Alevín, Cadet, Juvenile and Absolute, with a mean age of 15.97 (SD = 3.31). The questionnaires used were Authentic Leadership (ALQ), Task (LSS), Group Cohesion (CG), Team Power (CPEA), and Performance that showed optimal adjustment indexes (χ2 / DF = 1.313 p = 0; CFI = 0.997; TLI = 0.994; RMSEA = 0.032; SRMS = 0.0343). The results indicate that leadership style, especially leadership task (p = 0.37) indirectly influences performance through CG (p = 0.17) and CPE (p = 0.21). In conclusion, it is proposed to improve group performance by optimizing individual behavior models.