In magnetic coupling resonant wireless power transfer devices, planar spiral coils are often used as the coupling mechanism. In such application area, the coils are often wound sparsely with wide screw pitches and often under complex relative position. An accurate model of mutual inductance for planar spiral coils wound either densely or sparsely is established in this article. Utilizing Euler Angles, an exact expression of mutual inductance for a couple of planar spiral coils under arbitrary relative position is proposed. Based on this expression and geometric mean distance principle, a self-inductance expression for planar spiral coils is derived. Based on numerical calculation of the expressions proposed in this article, the influence of precession and spin angle on mutual inductance is studied; the method of determining radius of each turn when using traditional concentric circular filaments cluster approximation model for self- and mutual inductance calculation is studied; the difference of calculation results between the accurate model and the traditional approximation model is compared. Finite element simulation and experimental results verify the correctness of the proposed models.

Considering the helicity of Archimedean spiral coils, this paper proposes accurate expressions of mutual inductance and their numerical calculation methods, which can be applied in the wireless power transmission field, etc. Accurate expressions of mutual inductance are deduced respectively for two coils that are coaxial, laterally misaligned, or non-parallel, and numerical calculations are performed using Gaussian integration as well. In the case of coaxial coils, the calculation results are verified by the 3D finite element method (3D FEM) and compared with the results gained by the traditional method that approximates two spiral coils to two clusters of series-connected circular coils ignoring helicity. The comparison of the three methods shows that results achieved by the proposed expression are close to that of 3D FEM, while there is increasing error with the screw pitches of the coils when using the traditional circular coil approximation method. The influence of relative position on the mutual inductance of the two coils is also studied and it is further explained through magnetic field distribution. Finally, the validity of the proposed expressions of mutual inductance is verified by experimental results.

This paper investigates the synthesis of lossless snubbers and passive soft-switching converters, as well as their general properties, including topological properties and electrical properties. The aim is to find the possible simplest snubbers and soft-switching converters and to give suggestions on applications of the snubbers and the soft-switching converters. On the one hand, the synthesis and the topological properties of the snubbers and the converters determine the complexity of the topologies and help find the least requirement that passive soft switching needs to fulfill. The topological properties are investigated by addressing the basic issues induced by the inductor/capacitor added for passive soft switching. Based on the topological properties, the snubbers and the converters are synthesized. On the other hand, the electrical properties determine the performance of the snubbers and converters, help understand the limitations and are investigated by two case studies accompanied with experimental verification. By investigation of the general properties, the possible simplest snubber is given, the tradeoff between the complexity and performance is discussed in detail, the suggestion of snubber applications is presented, and three kinds of reported passive soft-switching converters which are believed to have the most potential of wide application are discussed.

Most current proton exchange membrane fuel cell (PEMFC) fault diagnosis models are not updated after offline training is completed, so they cannot learn new data features. For this problem, a PEMFC fault diagnosis model based on an equivalent circuit and an online sequential extreme learning machine (OS-ELM) framework is proposed. Under different operating conditions, the range of equivalent circuit element values is different, which is conducive to fault diagnosis. Element values constitute a dataset, from which OS-ELM is used to learn data features, and PEMFC fault diagnosis and isolation can be accomplished. OS-ELM has online learning capability. During the operation of PEMFC, equivalent circuit fitting is performed on multiple sets of sample data obtained using electrical impedance spectroscopy (EIS). Optimized fuzzy C-means (FCM) is used to cluster and partition sample data for online updating of OS-ELM models. The experimental results show that the proposed PEMFC fault diagnosis model not only has the speed and accuracy of fault diagnosis but also has the ability to learn fault characteristics online.

A wide-range soft switching high-efficiency cyclo- converter-type high-frequency-link (CHFL) inverter with dual- phase-shift (DPS) modulation strategy is proposed in this paper. By adding an auxiliary inductor in primary-side full bridge circuit and adopting center-tapped four-winding transformer structure, the soft-switching range for primary-side switches has been extended. The secondary-side active clamper network is designed to recycle leakage inductor energy, and suppress voltage spikes caused by the resonance of the leakage inductor and the parasitic capacitance of switches. One feature of the proposed inverter is that both the primary-side full-bridge and active clamper network employ phase-shift modulation technique where all switches are driven with nearly 50% duty cycle square signals, hence a simple and reliable transformer-isolated gate drive circuit with small transmission delay is presented. Moreover, all switches in the proposed inverter achieve zero voltage switching (ZVS), which contributes to higher conversion efficiency as well as higher switching frequency. Analysis, design, and implementation of the proposed inverter are discussed in detail. A 200-kHz laboratory prototype for the high voltage audio amplifier application is developed and the experimental results are presented to validate the analysis and performance of the proposed inverter.

DC series arc faults are one of the main causes of fire hazards in photovoltaic power systems. The common method of the traditional dc series arc fault detection uses wideband current sensors to obtain the arc current signal, extract arc characteristic frequency components, and make intelligent judgments based on numerous samples. This kind of detection method requires high-performance hardware and has a high cost. This article takes the common-mode conductive voltage (CMCV) signal, which is produced by arc faults, as the object and confirms the feature frequency band of the arc CMCV signal based on the spectral analysis. To obtain the feature frequency component and perform frequency reduction at the hardware level, multiple frequency selection and detection circuits were designed, and the feature quantities of the time and frequency domains were extracted from the output of the circuits. Subsequently, a two-dimensional arc fault feature plane was constructed, and an arc/normal similarity criterion and arc fault judgment algorithm were proposed. Finally, this article discusses or experimentally verifies the effectiveness, reliability, and anti-interference ability of the proposed method. The proposed method is low cost, simple, reliable, and effective and provides a novel idea and approach for dc series arc fault detection.