The increase in the internal temperature of high voltage electrical instruments is due to a variety of factors, particularly, contact problems; environmental factors; unbalanced loads; and cracks in the high voltage current transformers, voltage transformers, insulators, or terminal junctions. This increase in the internal temperature can cause unusual disturbances and damage to high voltage electrical equipment. Therefore, early prevention measures of thermal anomalies in equipment are necessary to prevent high voltage equipment failure that might shut down the whole grid system. In this article, we propose a novel non-destructive approach to defect analysis in high voltage equipment by taking advantage of the infrared thermography and the deep learning (DL) approach from the machine learning paradigm. The infrared images of the components were captured using the FLIR T630 without disturbing the operations of the power grid. In the first stage, rich features maps from the convolutional layers of the AlexNet pretrained model were extracted. After feature extraction, the random forest (RF) and support vector machines (SVM) were trained for learning of the defective and non-defective high voltage electrical equipment. In an experimental analysis, the RF optimally learned the separation between defective and non-defective equipment with greater than 96% accuracy, outperforming all the other comparative approaches for deep and nondeep features. The proposed approach based on the RF is reliable and shows its efficacy for fault detection in high voltage electrical equipment.

A variety of reasons, specifically contact issues, irregular loads, cracks in insulation, defective relays, terminal junctions and other similar issues, increase the internal temperature of electrical instruments. This results in unexpected disturbances and potential damage to power equipment. Therefore, the initial prevention measures of thermal anomalies in electrical tools are essential to prevent power-equipment failure. In this article, we address this initial prevention mechanism for power substations using a computer-vision approach by taking advantage of infrared thermal images. The thermal images are taken through infrared cameras without disturbing the working operations of power substations. Thus, this article augments the non-destructive approach to defect analysis in electrical power equipment using computer vision and machine learning. We use a total of 150 thermal pictures of different electrical equipment in 10 different substations in operating conditions, using 300 different hotspots. Our approach uses multi-layered perceptron (MLP) to classify the thermal conditions of components of power substations into “defect” and “non-defect” classes. A total of eleven features, which are first-order and second-order statistical features, are calculated from the thermal sample images. The performance of MLP shows initial accuracy of 79.78%. We further augment the MLP with graph cut to increase accuracy to 84%. We argue that with the successful development and deployment of this new system, the Technology Department of Chongqing can arrange the recommended actions and thus save cost in repair and outages. This can play an important role in the quick and reliable inspection to potentially prevent power substation equipment from failure, which will save the whole system from breakdown. The increased 84% accuracy with the integration of the graph cut shows the efficacy of the proposed defect analysis approach.

The thermography camera is widely used to inspect electrical equipment. The existing defect assessment indicators are mainly the hot spot temperature and the relative temperature difference (RTD), and the analyzing process used to calculate the indicators is usually off-site. A smart thermography camera is designed and its application in the diagnosis of electrical equipment is investigated in this article. For the camera, the regional RTD is calculated automatically based on equipment detection and image registration algorithms, and the defects can be judged according to the existing criteria. Firstly, the regional RTD and its implementation method are proposed to reduce the error when the hot spot is unmeasurable. Then the object detection method based on the convolutional neural network (CNN) is modified dedicated to infrared (IR) images. Over 12 000 historical images and 18 000 labels are used for training and tests. The modified model can identify 16 classes of substation equipment with 86.4% mAP. With low latency, the inference speed of this on-site smart camera reaches 30 frames/s. The test results show that the similarity of diagnosis result between our method and the criteria is 92.2%.

Objective: A theoretical study on the mechanisms through which Tumor Treating Fields (TTFields) affect dividing tumor cells. Methods: Numerical analysis was used to revisit two previously proposed mechanisms and introduce a third. We examine the previous hypotheses that: a) TTFields generate a moment that affects microtubule assembly during early mitosis, and b) dielectrophoretic (DEP) forces cause neutral particles to move toward the cleavage furrow during the telophase stage. We further introduce a new hypothesis that TTFields modify cell membrane potential in dividing tumor cells. Results: a) The Brownian energy is several orders of magnitude larger than the moment induced by TTFields on tubulin dimers. b) Adding Stokes drag forces to DEP forces shows that the motion of the particles in the cytoplasm is very slow, approximately 0.003 μm/s, and therefore, unless the duration of the telophase is long enough there will be no substantial effect from the DEP forces. c) The Schwan equation shows that electric fields at the frequencies of clinical TTFields can cause a 10% - 17% change in tumor cell membrane potential. Conclusion: Our studies find limited support for the previously suggested hypotheses and suggest that the TTFields effect on cell membrane potential by affecting ion channels could be a mechanism of tumor cell death. Significance: Previously suggested mechanisms of tumor cell death from TTFields are found lacking. The effect of TTFields on the tumor cell membrane potential warrants further research.

Single pixel imaging (SPI) combined with compressed sensing techniques can provide solutions for special optical imaging to avoid array detectors and raster scanning. However, the imaging speed should be further improved for real-time SPI and the challenge is to reduce the sampling time and post-processing time. This paper proposes a deep compressive and super-fast single pixel imaging protocol based on reordering Hadamard basis patterns and Fourier domain regularization inversion (FDRI) algorithm. Two reordered Hadamard basis patterns in terms of the improvement in the compressing ratio and reconstruction quality are proposed and compared to other methods. The deterministic Hadamard basis are reordered through their total variation (TV) in ascending order and total wavelet transformed coefficients (TW) in ascending order to have the best performance. Numerical simulation shows that this protocol can reconstruct a 128×128 pixels natural image at the sampling ratio of 5% with the peak-signal-to-noise ratio (PSNR) of 25.56 dB in 0.00039s. Terahertz near-field imaging experiment also verifies the proposed protocol. The inherent advantage and mechanism of reordering approaches are discussed and then revealed by comparing the coherent area generated by these reordered patterns. The TV order and TW order Hadamard basis patterns can be deterministically described mathematically and easily constructed. Meanwhile, it results in a significant improvement both in the compression ratio and image reconstruction quality. Finally, the realization this protocol in real SPI system in the future will bring the real-time SPI closer to practical applications.

Shore power system that powers berthing ships is gradually becoming widely used worldwide because it helps reduce air and noise pollution. However, due to the bad environment of the port, the influence of the port tide on the temperature distribution and the ampacity of the shore power cable connecting the ship and shore can't be ignored. Therefore, in this paper, a thermal field calculation model of shore power cable based on electromagnetic-thermal-flow coupling field is presented, in which the impact of tide has been considered. And the validity of the coupling model is verified by thermal circuit method base on IEC(International Electrotechnical Commission) 60287 standard. Then, the influence of cable immersion depth, water velocity and temperature, wind speed and air temperature on the temperature distribution of shore power cable was analyzed based on the multi-field coupling model. Finally, the ampacity of shore power cable in different environments is calculated by the combined Newton iterative and numerical simulation method. The results indicate that the conductor temperature firstly decreases and then increases with the immersion depth. And when the immersion depth is small, the wind speed dominates the temperature of the conductor. While the water flow speed plays a leading role in the conductor temperature when the immersion depth is deeper. In addition, the relationship between ambient temperature and capacity of shore power cable is approximately linear.