Steady-state analysis of switching converters via frequency-domain circuit equivalents

This brief presents a frequency-domain approach for the steady-state analysis of pulsewidth-modulated converters and switched circuits with nonideal switching behavior. The proposed strategy generalizes recent methodologies based on the Fourier expansion of the steady-state responses of a periodically switching circuit and on the simulation of an augmented linear-time-invariant system. This system is now also given an interpretation in terms of an equivalent circuit, which is simulated at a single frequency point to solve for all the harmonics. The method offers a modular topological approach that is combined with standard tools for circuit analysis and enables the simulation of networks with an arbitrary number of switches and driving mechanisms. Single, multiple, and possibly nonideal commutation events within the switching period are handled in the same framework, without additional complexity. The technique allows for the full frequency-domain characterization of both the functional and the noisy behavior of the circuit responses. The feasibility and strength are demonstrated via comparisons with simulations and measurements on two application examples, i. e., a full-bridge single-phase inverter and a dc-dc boost converter

Combined parametric and worst case circuit analysis via Taylor models

This paper proposes a novel paradigm to generate a parameterized model of the response of linear circuits with the inclusion of worst case bounds. The methodology leverages the so-called Taylor models and represents parameter-dependent responses in terms of a multivariate Taylor polynomial, in conjunction with an interval remainder accounting for the approximation error. The Taylor model representation is propagated from input parameters to circuit responses through a suitable redefinition of the basic operations, such as addition, multiplication or matrix inversion, that are involved in the circuit solution. Specifically, the remainder is propagated in a conservative way based on the theory of interval analysis. While the polynomial part provides an accurate, analytical and parametric representation of the response as a function of the selected design parameters, the complementary information on the remainder error yields a conservative, yet tight, estimation of the worst case bounds. Specific and novel solutions are proposed to implement complex-valued matrix operations and to overcome well-known issues in the state-of-the-art Taylor model theory, like the determination of the upper and lower bound of the multivariate polynomial part. The proposed framework is applied to the frequency-domain analysis of linear circuits. An in-depth discussion of the fundamental theory is complemented by a selection of relevant examples aimed at illustrating the technique and demonstrating its feasibility and strength

Stochastic Time-Domain Mapping for Comprehensive Uncertainty Assessment in Eye Diagrams

The eye diagram is one of the most common tools used for quality assessment in high-speed links. This article proposes a method of predicting the shape of the inner eye for a link subject to uncertainties. The approach relies on machine learning regression and is tested on the very challenging example of flexible link for smart textiles. Several sources of uncertainties are taken into account related to both manufacturing tolerances and physical deformation. The resulting model is fast and accurate. It is also extremely versatile: rather than focusing on a specific metric derived from the eye diagram, its aim is to fully reconstruct the inner eye and enable designers to use it as they see fit. This article investigates the features and convergence of three alternative machine learning algorithms, including the single-output support vector machine regression, together with its least squares variant, and the vector-valued kernel ridge regression. The latter method is arguably the most promising, resulting in an accurate, fast and robust tool enabling a complete parametric stochastic map of the eye

A high-efficiency portable system for insulation condition assessment of wind farm inter-array cables with double-sided partial discharge detection and localisation

Partial discharge (PD) diagnosis is a crucial tool to assess the insulation condition of wind farm cables. Among PD diagnosis techniques, PD localisation is promising as it can provide target maintenance indicators on the insulation weak points of the cables. Accordingly, this paper developed a portable PD detection and location system for wind farm inter-array cables. The system consists of two non-invasive and lightweight testing units, which can be conveniently deployed on an energised cable, enabling highly efficient online PD diagnosis of the widely distributed inter-array cables. The system achieves accurate PD localisation of the energised cable via an improved double-sided travelling wave method. The method exhibits two superior features: the double-sided testing units are accurately synchronised via the joint application of Global Position Systems and a pulse-based interaction process, and a windowed phase difference method is proposed and integrated into the system to robustly estimate the time-of-arrival difference in low signal-to-noise ratio environment. Validation experiments were conducted on both a 10-kV cable in the laboratory and a real 35-kV cable in an on-shore wind farm