This paper proposed a prediction method to predict a 10 kV oil-immersed transformer hot spot temperature. 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 hot spot temperature. This model is trained by 9 samples selected by L9(34) orthogonal array and applied to predict the hot spot temperature of 20 test samples. The training samples are all obtained by simulation, and the test samples are 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.

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.

With the fast development of China's power grid construction, many transmission lines' corridors are inevitable across mountain with luxuriant vegetation. The occurrence of forest fires under transmission lines may lead to power interruptions, seriously affecting the stability of the grid operation. The characteristics of fire-induced flashovers are analyzed by statistical data of 220–500-kV lines trips in recent years. The line trip out rate caused by forest fires varied according to seasons and climate. The fire-induced flashover is much more serious than lighting trips. AC and DC voltage discharge tests of transmission line gap under different fire conditions are carried out by using wood crib fire. Test results indicate that: the gap's insulation strength under fire conditions is reduced to about 20% of that under pure air; the values of breakdown voltage have been decreased about 30% after alkali metal added; the gap breakdown under DC voltage has obvious polarity effect, and the flame shape and leakage current waveform are different under positive DC voltage and negative DC voltage. The analysis of gap breakdown mechanism under fire conditions shows that temperature, flame conductivity and ashes are main factors. Their combined effect significantly reduces the gap's insulation strength. Copyright © 2014 John Wiley & Sons, Ltd.

The bushing hot spot temperature seriously affects its performance and design. In this paper, a novel method for calculating the hot spot temperature of bushing is proposed, in which the principle of constant joule heating is adopted to transform the fluctuating current into the steady-state current. Firstly, the fluctuating current based on time is segmented. Then, the fluctuating current in each period of time is transformed into steady-state current which used as heat source. Next, the Finite Element Method (FEM) is employed to determine a bushing’s temperature distribution and specify its hottest spot. According to the calculation results of hot spot temperature rise, the time interval is adjusted. Finally, the optimal time segment and the steady-state equivalent current are obtained by solving the hot spot temperature of transformer bushing iteratively. In addition, the method is used to calculate the hot spot temperature of 220kV Oil Impregnated Paper (OIP) bushing. Compared with the results of traditional transient calculation method, the validity of the method is verified, and the computing time is greatly reduced.

This article studies a multi-segment capacitively driven asynchronous induction coil launcher. First, a segment with six-stage asynchronous induction coil launcher is studied. Based on the results of the single-segment induction coil launcher and the orthogonal design method, a six-factor and five-level orthogonal table is established. The factors are, namely, trigger position, armature thickness, initial capacitance voltage, inner radius of coil, turns of coil, and coil axial length. The results of the orthogonal test show that the influencing factors from large to small are coil turns, coil axial length, initial capacitance voltage, coil radius, trigger position, and armature thickness. Based on the above results, a two-segment asynchronous induction coil launcher is designed. The coil parameters of the second section are the same as those of the first section, and the number of coils is twice that of the first section. By studying the trigger position, capacitance, and voltage combination of the second stage, the optimal trigger position is 163 mm, capacitance value is 1125 μF, and capacitance voltage is 4 kV. On account of the above research results, a four-segment induction coil launcher with two armatures is proposed. The parameters of the first and third segments are the same, and the parameters of the second and fourth segments are the same. The first and second segments drive armature 1, and the third and fourth segments drive armature 2. In the case of cross ignition, the standard deviation of armature force is 18.62 kN, while the standard deviation of armature force for sequential ignition is 25 kN. Through cross ignition, higher armature muzzle velocity and smaller acceleration jitter can be obtained.