Transformer winding hot-spot temperature (HST) is one of the important factors affecting transformer oil-paper insulation deterioration. This study presents a three-dimensional coupled electromagnetic-fluid-thermal analysis method for HST calculation in a 10 kV oil-immersed distribution transformer, the influence of the transformer internal metal structure parts on the HST of the winding is considered in the simulation. Combining electromagnetic-field calculation with no-load test and load test of the transformer provides a more accurate method to determine internal losses of the transformer. Taking those power losses as heat sources, the transformer fluid-thermal field analysis is conducted with the finite volume method. The variation of physical parameters of transformer oil with temperature is considered in the simulation. On the basis of the equivalent thermal resistance theory, the equivalent thermal conductivities of transformer windings are obtained. The simulation results deduced from the proposed method agree well with the experimental ones, which are obtained with fibre optic temperature sensors during the transformer temperature rise test, the maximum temperature difference is <3°C. The results validated the validity and accuracy of the proposed transformer HST calculation method.

This paper proposed a prediction method to predict a 10-kV oil-immersed transformer hot spot temperature (HST). A set of feature temperature points on the transformer iron shell is proposed based on fluid-thermal field calculation. These feature points, as well as transformer load rate, are taken as the input parameters of a machine learning model established by support vector regression (SVR), thus to describe their relationships with the HST. This model is trained by nine samples selected by L 9 (3 4 ) orthogonal array and applied to predict the HST of 20 test samples. The training samples are all obtained by simulation, and the test samples have consisted of simulation and transformer temperature rise test results. With effective parameter optimization of the SVR model, the predicted results agree well with the experimental and simulation data, the mean absolute percentage error (MAPE) is 1.55%, and the maximum temperature difference is less than 3 • C. The results validated the validity and the generalization performance of the prediction model. INDEX TERMSHot spot temperature, oil-immersed transformer, support vector regression, multi-physical field analysis.

This study aimed to explore the risk factors for foot ulcer recurrence in patients with comorbid diabetic foot osteomyelitis (DFO) and diabetic nephropathy (DN). This is a prospective cohort study. Between May 2018 and May 2021, we selected 120 inpatients with comorbid severe diabetic foot infection (PEDIS Grade 3 or above) and DN for inclusion in our study. All cases were followed up for 36 months. The study outcomes were whether foot ulcer recurred and the time to recurrence. The risk factors of ulcer recurrence were analysed by comparing the data of the three groups. According to the recurrence of foot ulcer, the participants were divided into three groups: Group A (no foot ulcer recurrence, n = 89), Group B (foot ulcer recurrence within 12‐36 months, n = 19) and Group C (foot ulcer recurrence within 6‐12 months, n = 12). The multivariate Cox regression analysis showed that urine albumin‐creatinine ratio (UACR) (HR: 1.008, 95% CI: 1.005‐1.011, P < .001) and vibration perception threshold (VPT) (HR: 1.064, 95% CI: 1.032‐1.096, P < .001) were identified as independent risk factors. Kaplan‐Meier curves showed a significant positive association between UACR or VPT and the risk of foot ulcer recurrence (log rank, all P < .05). Areas under the ROC curves for UACR, VPT and the combination of UACR and VPT were 0.802, 0.799 and 0.842, respectively. The best cut‐off values of UACR and VPT were 281.51 mg/g and 25.12 V, respectively. In summary, elevated UACR and VPT were independent risk factors. The best clinical cut‐off values of UACR and VPT for prediction of foot ulcer recurrence were 281.51 mg/g and 25.12 V, respectively. Besides, our results suggested that microcirculation disorders rather than macrovascular complications play a major role in the recurrence of foot ulcer in patients with comorbid DFO and DN.

Transformer overheating faults have an important impact on the transformer safe and stable operation. Taking 10 kV oil‐immersed transformer as the research object, using the indirect coupling analysis method of electromagnetic thermal fluid multi‐physical field, the transformer heating characteristics under overheating faults, such as winding inter‐turn short circuit and core multi‐point grounding, are analysed, and the transformer abnormal heating state identification method is proposed based on the inversion of transformer top oil temperature rise. By inverting the transformer normal top oil temperature rise under different load rates and comparing it with the actual measured value, taking into account the top oil temperature rise inversion error, it is determined that when the top oil temperature rise measured value is higher than the inversion value by 2.9 K, it indicates the transformer is in an abnormal heating state, and the higher the deviation degree is, the deeper the degree of abnormal heating fault. The accuracy of the transformer abnormal heating state identification method is verified by the multi‐working condition temperature rise test, which can provide a certain reference for the detection and maintenance of transformer abnormal internal heating faults.

The trip mechanism is a weakness in circuit breakers. Traditional fault identification based on the coil current is difficult to report early mechanical defects such as coil‐plunger jam. Here, the vibration signal during the trip process was extracted. Based on the coil current signal and vibration signal, the characteristics of the trip mechanism are analyzed. The phase space reconstruction (PSR) method is used to extract features from the vibration signal. Combined with the features from the coil current waveform, the feature set representing the health condition of the trip mechanism is proposed. The fault simulation tests are carried out and the variation of current vibration characteristics under fault conditions is studied. The fault identification model based on a support vector machine (SVM) is proposed and compared with the identification results when features are extracted from a single signal. When the power supply voltage is dispersed, the prediction accuracy of fault identification is 83.3% considering only the features of the current waveform or vibration signal. And the identification accuracy rises to 96.7% while using the feature set of current and vibration signals. On basis of the current signal, the method further combines the vibration signal so that the robustness of defect identification improves.

No abstract

No abstract

No abstract