Magnetostriction caused by the exciting variation of the magnetic core and the current conducted by the winding wired to the core has a significant result impact on a power transformer. This paper presents the sound of a factory transformer before on-site delivery for no-load tests. This paper also discusses the winding characteristics from the transformer full-load tests. The simulation and the measurement for several transformers with capacities ranging from 15 to 60 MVA and high voltage 132kV to low voltage 33 kV are performed. This study compares the sound levels for transformers by no-load test (core/magnetostriction) and full-load test (winding/displacement ε). The difference between the simulated and the measured sound levels is about 3dB. The results show that the sound level depends on several parameters, including winding displacement, capacity, mass of the core and windings. Comparative results of magnetic induction of cores and the electromagnetic force of windings for no-load and full-load conditions are examined.

This study investigated the effect of magnetostriction-induced core magnetomechanical vibrations and noise on the magnetic properties of power transformers. The magnetostriction of grain-oriented Si steels was found to be extremely sensitive to compressive stress applied along the rolling direction and to tensile stress applied along the transverse direction. The compressive stress increased the variation in the magnitude of magnetostriction, which is correlated with core vibration and noise. A 2D model of the power transformer was used to simulate the noise and vibration variables through a finite element analysis. V

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The determination of the critical path (CP) in stochastic networks is difficult. It is partly due to the randomness of path durations and partly due to the probability issue of the selection of the critical path in the network. What we are confronted with is not only the complexity among random variables but also the problem of path dependence of the network. Besides, we found that CP is not necessarily the longest (or shortest) path in the network, which was a conventional assumption in use. The Program Evaluation and Review Technique (PERT) and Critical Path Index (CPI) approaches are not able to deal with this problem efficiently. In this study, we give a new definition on the CP in stochastic network and propose a modified label-correcting tracing algorithm (M-LCTA) to solve it. Based on the numerical results, compared with Monte Carlo simulation (MCS), the proposed approach can accurately determine the CP in stochastic networks.