Comparison of RMS Value Measurement Algorithms of Non-coherent Sampled Signals

The authors have recently developed a number of instruments for measuring harmonic composition of power grid signals. These instruments have a simple, predominantly digital architecture and they are based on an assumption that the frequency of the measured signals equals its nominal value (50 Hz, 60 Hz or 400 Hz). This approach has enabled the measurement of a high number of harmonics within a single period. However, the internal oscillator in the instrument generates the nominal frequency and cannot adapt to frequency changes in the input signal. This paper presents a method for the improvement of the operation of the developed instruments in cases when the fundamental frequency drifts from its nominal value as is the case with real power grid signals. Based on this method, modified versions of the harmonic measurement instruments have been developed. A comparison of the measurement error with and without the application of the proposed method is presented in the paper.

Section: Subject and Methodsmentioning

confidence: 99%

“…The proposed correction algorithm is far less complex, more reliable and faster than any other known approach based on the assumption of an unknown frequency. A partial solution to the problem can be found in case of a purely sinusoidal signal [20] but the solution cannot be generalized to the nonsinusoidal regime.…”

Section: Introductionmentioning

confidence: 99%

A Method for Harmonic Measurement of Real Power Grid Signals with Frequency Drift using Instruments with Internally Generated Reference Frequency

Antić¹,

Mitrovic²,

Vujičić³

2012

“…Thus, the mathematical model of the dynamic error should be based on some average values for which it is aptly to use the root-mean-square value [8][9][10], provided that process x(t) possesses the quality of ergodicity, i.e.…”

Section: Mathematical Model Of the Dynamic Errormentioning

confidence: 99%

A Model of the Dynamic Error as a Measurement Result of Instruments Defining the Parameters of Moving Objects

Dichev

Koev

Bakalová

et al. 2014

The present paper considers a new model for the formation of the dynamic error inertial component. It is very effective in the analysis and synthesis of measuring instruments positioned on moving objects and measuring their movement parameters. The block diagram developed within this paper is used as a basis for defining the mathematical model. The block diagram is based on the set-theoretic description of the measuring system, its input and output quantities and the process of dynamic error formation. The model reflects the specific nature of the formation of the dynamic error inertial component. In addition, the model submits to the logical interrelation and sequence of the physical processes that form it. The effectiveness, usefulness and advantages of the model proposed are rooted in the wide range of possibilities it provides in relation to the analysis and synthesis of those measuring instruments, the formulation of algorithms and optimization criteria, as well as the development of new intelligent measuring systems with improved accuracy characteristics in dynamic mode.Keywords: dynamic error, measurement in dynamic mode, inertial effects, parameters of moving objects, set-theoretic model.

“…In simple situations, a measuring instrument consists of a front-end sensor and a cascaded tail-end signal inversion or reconstruction unit. 5 A sensor is a systeminstrument interface hardware, serving as a crucial component of most measuring instruments. It converts the information of interest (e.g., about the degree of temperature, pressure, and so forth) carried by the target system's unknown output signal (i.e., time-dependent measurand) Q(t) into a correlated information, carried by the sensor's electrical, optical or some other kind of output signal.…”

Section: Introduction and General Backgroundmentioning

confidence: 99%

“…It is important to bear in mind that in the sensory and reconstruction transformations of signals there is no reference to numbers. The measuring instrument's output signal Q(t) is usually fed automatically into a feedback control system or is transmitted to a 5 Here the notion of reconstruction is similar to the one introduced by R. Z. Morawski in [9]. 6 For example, if the information carried by the signals is described by a Gaussian probability density function, then the inverse problem is usually solved by the method of least squares that determines the signal's least squares estimator.…”

Section: Introduction and General Backgroundmentioning

confidence: 99%

Algebraic Frameworks for Measurement in the Natural Sciences

Domotor¹

2012

The goals of this paper fall into three related areas: (i) we present an overview of a universal algebraic paradigm in which measurement specialists can construct formal models of measurement in a unified manner and systematically reason about a large class classical measurement operations, (ii) we construct convenient von Neumann quantity algebras and quantity-channels between them to represent measurements, and introduce the dual framework of state spaces and state-channels between them to investigate the statistical structure of measurements, and (iii) we provide several detailed examples that illustrate the power and versatility of algebraic approaches to measurement procedures.