Abstract:In electrical machines, iron losses are essential for electromagnetic and thermal designs and analyses. Although many models have been proposed to predict iron losses in magnetic materials, the calculation of iron losses under non-sinusoidal excitations is still an open field. Most works concern the influences of the value, the change rate or the frequency of flux density in the frequency domain. In this paper, we propose an engineering model for predicting loss characteristics with given waveforms of flux density in the time domain. The characteristics are collected from the knowledge of the iron loss in a laminated ring-shaped transformer. In the proposed model, we derive mathematical formulas for exciting currents in terms of flux density by describing the function methods through multi-frequency tests with sinusoidal excitations. The non-linearity of the material is interpreted by branches of conductances accounting for hysteresis and eddy-current losses. Then, iron losses are calculated based on the law of conservation of energy. An experimental system was built to evaluate the magnetic properties and iron losses under sinusoidal and non-sinusoidal excitations. Actual measurement results verify the effectiveness of the proposed model.

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This paper proposes a method that indirectly measures the contact erosion of alternating current (AC) contactors via mapping electrical signals to the contacting alloy erosion condition which is represented by the accumulated contact mass loss (ACML). Electrical signal waveforms and their corresponding ACMLs of every make-and-break operation are acquired in endurance tests. A supervised convolutional neural network regression (CNNR) architecture containing six onedimensional convolution layers is proposed to model the relation between waveforms and ACMLs. We compare different CNNR architectures as well as different training schemes by the test precision to obtain the optimal solution. Experiments prove that the proposed CNNR architecture with an optimized training scheme can achieve a precise ACML measurement when only voltage waveforms of make operations are used. The best results reach mean absolute errors of 3.29% and 1.59% corresponding to two datasets respectively, which are superior to the results of other regression methods in the comparison and prove the theoretical significance and applicational values.

High-temperature superconductors have great potential for various engineering applications such as a flywheel energy storage system. The levitation force of bulk YBCO superconductors can be drastically increased by increasing the strength of the external field. Therefore, a 6T conduction-cooled superconducting magnet has been developed for levitation force measurement application. Firstly, to protect the magnet from mechanical damage, reliable stress analysis inside the coil is paramount before the magnet is built and tested. Therefore, a 1/4 two-dimensional (2D) axisymmetric model of the magnet was established, and the mechanical stress in the whole process of winding, cooling down and energizing of the magnet was calculated. Then, the charging, discharging, and preliminary levitation force performance tests were performed to validate the operating stability of the magnet. According to the simulation results, the peak stresses of all coil models are within the allowable value and the winding maintains excellent mechanical stability in the superconducting magnet. The test results show that the superconducting magnet can be charged to its desired current of 150 A without quenching and maintain stable operation during the charging and discharging process. What is more, the superconducting magnet can meet the requirements for the levitation force measurement of both low magnetic field and high magnetic field.