Sefa Demirtas

Joint Voltage and Phase Unbalance Detector for Three Phase Power Systems

This letter develops a fast detection algorithm for voltage and phase unbalance in three phase power systems. It is suitable for real time applications since the required observation length is one cycle. It is shown to successfully detect small unbalance conditions at low SNRs. Its detection performance is shown to outperform traditional detectors that rely on changes in only a subset of positive, negative and zero sequence voltages. Unbalance detection is formulated as a hypothesis test under a framework of detection theory and solved by applying a generalized likelihood ratio test (GLRT). We first obtain an approximate maximum likelihood estimate (MLE) of the system frequency and then use it to substitute the true unknown frequency in the GLRT. A closed form expression is provided to detect unbalance conditions. Theoretical derivations are supported by simulations.

Effect of trapping on the critical voltage for degradation in GaN high electron mobility transistors

We have performed VDS = 0 V and OFF-state step-stress experiments on GaN-on-Si and GaN-on-SiC high electron mobility transistors under UV illumination and in the dark. We have found that for both stress conditions, UV illumination decreases the critical voltage for the onset of degradation in gate current in GaN-on-Si HEMTs in a pronounced way, but no such decrease is observed on SiC. This difference is attributed to UV-induced electron detrapping, which results in an increase in the electric field and, through the inverse piezoelectric effect, in the mechanical stress in the AlGaN barrier of the device. Due to the large number of traps in GaN-on-Si, this effect is clearer and more prominent than in GaN-on-SiC, which contains fewer traps in the fresh state.

Exact and approximate polynomial decomposition methods for signal processing applications

Signal processing is a discipline in which functional composition and decomposition can potentially be utilized in a variety of creative ways. From an analysis point of view, further insight can be gained into existing signal processing systems and techniques by reinterpreting them in terms of functional composition. From a synthesis point of view, functional composition offers new algorithms and techniques with modular structure. Moreover, computations can be performed more efficiently and data can be represented more compactly in information systems represented in the context of a compositional structure. Polynomials are ubiquitous in signal processing in the form of z-transforms. In this paper, we summarize the fundamentals of functional composition and decomposition for polynomials from the perspective of exploiting them in signal processing. We compare exact polynomial decomposition algorithms for sequences that are exactly decomposable when expressed as a polynomial, and approximate decomposition algorithms for those that are not exactly decomposable. Furthermore, we identify efficiencies in using exact decomposition techniques in the context of signal processing and introduce a new approximate polynomial decomposition technique based on the use of Structured Total Least Norm (STLN) formulation.

Sensitivity of polynomial composition and decomposition for signal processing applications

Polynomial composition is well studied in mathematics but has only been exploited indirectly and informally in signal processing. Potential future application of polynomial composition for filter implementation and data representation is dependent on its robustness both in forming higher degree polynomials from ones of lower degree and in exactly or approximately decomposing a polynomial into a composed form. This paper addresses robustness in this context, developing sensitivity bounds for both polynomial composition and decomposition and illustrates the sensitivity through simulations. It also demonstrates that sensitivity can be reduced by exploiting composition with first order polynomials and commutative polynomials.

A Functional Composition Approach to Filter Sharpening and Modular Filter Design

Designing and implementing systems as an interconnection of smaller subsystems is a common practice for modularity and standardization of components and design algorithms. Although not typically cast in this framework, many of these approaches can be viewed within the mathematical context of functional composition. This paper re-interprets and generalizes within the functional composition framework one such approach known as filter sharpening, i.e., interconnecting filter modules which have significant approximation error in order to obtain improved filter characteristics. More specifically, filter sharpening is approached by determining the composing polynomial to minimize the infinity-norm of the approximation error, utilizing the First Algorithm of Remez. This is applied both to sharpening for FIR, even-symmetric filters and for the more general case of subfilters that have complex-valued frequency responses including causal IIR filters and for continuous-time filters. Within the framework of functional composition, this paper also explores the use of functional decomposition to approximate a desired system as a composition of simpler functions based on a two-norm on the approximation error. Among the potential advantages of this decomposition is the ability for modular implementation in which the inner component of the functional decomposition represents the subfilters and the outer the interconnection.

Modular filters based on filter sharpening and optimal minimax design

A filter design algorithm is presented that can be viewed as a generalization of filter sharpening with guaranteed minimax optimality and that leads to an efficient modular topology. The structure consists of repetitive usage of a given sub-filter in a fashion similar to a traditional tapped-delay line. In cases where a sub-filter is not specified a priori, a low order sub-filter that approximates the given filter specifications can be used in this structure. The transfer functions of the overall modular filters obtained with this algorithm can be expressed mathematically as the functional composition of two transfer functions. This mathematical formulation creates a convenient framework to analyze and reduce sensitivity with respect to coefficients of the sub-filter or the tap coefficients without altering the characteristics of the overall design.