Along with the increase in the use of nonlinear electronic devices, e.g. personal computers, power tools and other electrical appliances, the requirements for uninterruptible power supplies are constantly growing. This paper proposes a method and deep analysis of results viable for checking how single-phase uninterruptible power supplies (UPSs) cope with nonlinear circuits under varying power loads in terms of electric energy quality.Various classes of single-phase UPS systems with different topologies were tested, for instance line-interactive and double conversion (online) single-phase UPS devices. Furthermore, measurements were carried out in view of a power source – loads were supplied both from a power grid and UPS built-in battery. Juxtaposition of the obtained results such as a THDU, THDI (Total Harmonic Distortion) percentage ratio of input/output voltage and current, a power factor and crest factor volume etc. of the tested UPS systems indicated major differences in their performance during laboratory tests.

Hull consistency is a known technique to improve the efficiency of iterative interval methods for solving nonlinear systems describing steady-states in various circuits. Presently, hull consistency is checked in a scalar manner, i.e. successively for each equation of the nonlinear system with respect to a single variable. In the present poster, a new more general approach to implementing hull consistency is suggested which consists in treating simultaneously several equations with respect to the same number of variables.

This paper is devoted to multiple soft fault diagnosis of analog nonlinear circuits. A two-stage algorithm is offered enabling us to locate the faulty circuit components and evaluate their values, considering the component tolerances. At first a preliminary diagnostic procedure is performed, under the assumption that the non-faulty components have nominal values, leading to approximate and tentative results. Then, they are corrected, taking into account the fact that the non-faulty components can assume arbitrary values within their tolerance ranges. This stage of the algorithm is carried out using the linear programming method. As a result some ranges are obtained including possible values of the faulty components. The proposed approach is illustrated with two numerical examples.

Considering the problem to diagnose incipient faults in nonlinear analog circuits, a novel approach based on fractional correlation is proposed and the application of the subband Volterra series is used in this paper. Firstly, the subband Volterra series is calculated from the input and output sequences of the circuit under test (CUT). Then the fractional correlation functions between the fault-free case and the incipient faulty cases of the CUT are derived. Using the feature vectors extracted from the fractional correlation functions, the hidden Markov model (HMM) is trained. Finally, the well-trained HMM is used to accomplish the incipient fault diagnosis. The simulations illustrate the proposed method and show its effectiveness in the incipient fault recognition capability.