Transfer function estimation of turboalternator from practical operating data by spectral analysis

On the dynamic system identification of the response behaviour of linear viscoelastic materials

یکی از فرایند­های تصادفی که در اکثر آزمایش‌­های هسته‌­ای مشاهده می‌­شود، فرایند تصادفی پواسون است. با توجه به اثر زمان‌­مرده در سامانه‌­های آشکارسازی حالت پالسی، فرایند اندازه‌­گیری شده در خروجی آشکارساز نسبت به فرایند تصادفی پواسون دارای اعوجاج است. در این تحقیق با استفاده از روش‌­های تحلیل آماری به شناسایی سامانه‌­های آشکارسازی حالت پالسی پرداخته شده است. یکی از آشکارسازهای نوعی که در حالت پالسی کار می‌­کند، آشکارساز BF3 است. در این پژوهش یک آشکارساز BF3 نوعی برای پیاده­‌سازی روش فوق انتخاب شد و سپس پالس‌­های مشاهده شده در خروجی آشکارساز در حوزه زمان با استفاده از تحلیل آماری افت­و­خیزها اندازه­‌گیری و تحلیل گردید. در آزمایش‌­هایی مانند اندازه‌­گیری‌­های مربوط به تئوری نوفه رآکتور قدرت صفر، تابع انتقال سامانه آشکارسازی استفاده شده نیز خود بر نتایج به‌­دست آمده اثرگذار است. از این‌­رو آگاهی از تابع انتقال سامانه آشکارسازی به‌­کار گرفته شده در این آزمایش‌­ها از اهمیت ویژه­‌ای برخوردار است. همچنین، در اندازه‌­گیری‌­هایی که در آن­ها تصحیح زمان‌­مرده سامانه آشکارسازی ضروری است، مشخص بودن تابع انتقال می­‌تواند اطلاعات مفیدی از اثرات زمان‌مرده را در اختیار قرار دهد. در این پژوهش تابع انتقال یک سامانه ­آشکارسازی نوترون نوعی که بر پایه‌­ی آشکارساز BF3 است بررسی شده است.

This paper proposes a simple method to achieve the optical transfer function of a circular pupil wavefront coding system with a separable phase mask in Cartesian coordinates. Based on the stationary phase method, the optical transfer function of the circular pupil system can be easily obtained from the optical transfer function of the rectangular pupil system by modifying the cut-off frequency and the on-axial modulation transfer function. Finally, a system with a cubic phase mask is used as an example to illustrate the way to achieve the optical transfer function of the circular pupil system from the rectangular pupil system.

A procedure is developed for obtaining the transfer function of a linear system from the frequency-response data in terms of its poles and zeros. The method is based upon the fact that the transfer function is an analytic function of the complex variable s at all points in the s plane except the isolated points which are the poles of the function. Hence if the function is specified along the jω axis, it is possible to extend the function in the left-half or right-half s plane by means of conformai mapping techniques. The curvilinear squares in the G(s) plane corresponding to a grid of constant σ and constant ω lines in the s plane can be constructed with fair accuracy after a little practice. The particular values of σ and ω, for which the lines pass through the origin of the s plane, can thus be determined by locating the zeros of the transfer function. The poles are similarly obtained by working with the inverse function. Since there are several values of s satisfying G(s) = 0, the mapping from the s plane to the G(s) plane is not entirely a one-to-one mapping, and several regions in the s plane are mapped on to the same region in the G(s) plane. A method is suggested to overcome this difficulty and to obtain, step-by-step, all the zeros and poles of the transfer function corresponding to the given frequency-response locus. The method is illustrated by means of three numerical examples.

This chapter considers the approximation of the transfer function of a linear system in terms of a causal filter bank. Assume that \(f\left( {{\rm{e}}^{{\rm{i}}\theta } } \right)\) with \(\theta \in [-\pi,\pi)\) is the transfer function of an arbitrary discrete-time linear system \({\cal L}\), and let \(\Phi = \left\{ {\varphi _k } \right\}_{k = 1}^\infty \) be a set of transfer functions of an orthonormal filterbank. It is assumed that f as well as all \(\varphi_{k}\) are elements of a certain Banach algebra \({\cal B}\) which characterizes the system theoretical properties of \({\cal L}\) and of the filterbank \(\Phi \). Moreover, since f as well as \(\{\varphi_{k}\}^{\infty}_{k=1}\) should represent causal systems, these transfer functions have to belong to the causal subspace \({\cal B}_ + \,{\rm{of}}\,{\cal B}\). Then, it is desirable to obtain an approximation of f in this filterbank of the form

This paper illustrates the use of Prony signal analysis tools to derive a system model of the thyristor controlled series capacitor (TCSC) located at the Bonneville Power Administration's (BPA's) Slatt 500 KV substation near Arlington Oregon. The Slatt TCSC is part of EPRI's flexible AC transmission systems (FACTS) initiative. The analysis presented in this paper was carried out to find linear models for the TCSC system using Prony analysis and to determine the range of validity for these linear models. Prony analysis is performed on output data files generated by simulations using BPA's transient stability program (TSP) on the TCSC and surrounding system under both closed and open loop TCSC operation. TSP simulations of the power system are performed using a 2623 bus 455 machine stability model of the western power grid. Root loci of the open loop and closed loop identified systems were generated and the motion of the system's transfer function poles was compared to those obtained by analyzing the system responses for various TCSC modulation controller gains. Comparisons are also made to the system (via TSP simulations) and transfer function responses in the time domain for both the open and closed loop identified transfer functions. The results shown here illustrate the advantages of closed loop identification and the nonlinearity of the system.

On the Estimation of Transfer Functions

In this study, transfer function of an active suspension system of a quarter car model is modeled via parametric system identification methods. It is tried to obtain a method that can provide the best representation of the transfer function of the system by utilizing different transfer function modeling tools. Most of mechanical systems include active suspension system of a quarter car model are modeled as mass spring damping systems. Proposing the best system identification method that represents the transfer function model of a system examined in this study is a crucial task to make a generalization for modeling of these type of systems. Numerical simulation studies are realized in Matlab environment to observe the performances of these methods and present them in a comparative manner. As a result of these simulations it is observed via the comparative results that Output Error (OE) transfer function model is an useful system identification tool for the mentioned purpose.

A newly developed theory for predicting the response of a linear system to an electromagnetic pulse, based only on the measured continuous-wave magnitude, is applied to the problem of possible electromagnetic interferences at a sensitive part of a torpedo. The measured magnitude representing the system's transfer function is deduced first from the measured response at this sensitive point to a known cw source, supplied by the Naval Surface Warfare Center. We derive an analytic expression for the magnitude square of the transfer function to approximate the measured data and obtain a system transfer function in terms of the complex fi"equency, from which we predict the system's cw phase characteristics and its multiple solutions due to a given impulse source.

On Padé-type model order reduction of J-Hermitian linear dynamical systems

The state‐space realization of linear systems is of utmost importance in linear systems theory. After the realization problem for the time‐invariant case was solved, particular attention was paid to the case of linear periodic systems. Recently, such systems have regained importance, for instance, in the context of coding theory where periodic convolutional encoders play an important role. The majority of the contributions within this area only concern the realization of transfer functions (or impulse responses), thus excluding the case of input/output linear systems without coprime representations. By the end of the eighties of the last century, Jan C. Willems suggested an approach (nowadays known as the behavioral approach) that considers a wider class of systems and allows to overcome this drawback. According to this approach, the central object in a system is its behavior which consists of all the signals that satisfy the system laws (also called system trajectories). Consequently, the behavior of a system with an input/output representation that is not coprime contains more trajectories than the set of input/output signals defined by the system transfer function. Our work takes this fact into account. In previous research conducted by the authors, some results on the realization of periodic behavioral systems were obtained, which, despite their importance, are conservative with respect to the dimension of the obtained realizations. In this paper, we revisit the behavioral periodic realization problem and give further insight into this question. This allows to derive new results and set up an algorithm to compute low‐dimensional state‐space realizations.

The transfer function of an optical system is obtained from a direct measurement of the wavefront. The measured wavefront data are utilized to calculate the transfer function by the convolution theorem. The wavefront measurements are done with a Twyman–Green interferometer and a modified form of the Foucault knife-edge test (termed a Foucault photometer test). It is shown that the results of the two independent measurements yield excellent agreement with each other as well as the theoretical calculations from the lens design data. Detailed analysis of the two systems is given.

The unstable problem of speed sensorless induction motor drives (SSIMDs) at low synchronous speed range has not been solved yet. In this paper, the reason of this unstable problem is concluded as two key points: 1) there are poles of the transfer function of the SSIMD system owning positive real part at low speed range; and 2) the rotor speed is unobservable at zero synchronous speed. Correspondingly, to guarantee the stability of the SSIMD system at low speed range, the system has to achieve two conditions simultaneously: the transfer function of the SSIMD system is stable (transfer function stability (TFS) condition) and the rotor speed is observable (rotor speed observability (RSO) condition). To satisfy these two conditions, virtual voltage injection is proposed. The unstable problems can be solved by this proposed method. In addition, the robustness of the virtual voltage injection method is analyzed. When motor parameters are inaccurate, the stability of the system will not be influenced. Finally, the feasibility of the proposed method is verified by experiments.

The two-image depth from defocus problem is considered as a system identification problem. The underlying phenomenon of defocusing is modelled as a linear system with an appropriate transfer function. Using a general measure for the spread, a relation between the spread and the system transfer function is obtained. A parametric transfer function is utilised in deriving an analytical expression for the spread. The two images taken with different camera parameters then become the input-output of such a system. An algorithm for estimating the coefficients of the transfer function is derived. The method is tested on synthetic images as well as images obtained from a camera.

A novel Hankel theory is developed for non-causal multidimensional linear shift invariant systems. It is shown that the rank of a generalized Hankel operator is related in a fundamental way to the rationality of a multidimensional system's transfer function. This theorem may be viewed as a generalization of Kronecker's ID theorem. These results suggest a new algorithm for multidimensional system identification from impulse response measurements.