Uncertainty of Thermographic Temperature Measurement of Electric Units Contained in Switchgear

The result of the works presented is the uncertainty budget of a thermographic temperature measurement taken through an IR window. The type B uncertainty determination method has been employed. Publication of European Accreditation EA-4/02 has been patterned. Conditions prevailing in course of the thermographic temperature measurement of low-voltage electric units contained in the switchgear were recreated as part of the works. The measurement system has been presented. Components of the infrared radiation reaching the camera lens in case when an IR window was used and when an IR window was not used have been discussed. Uncertainties estimated for the measurement done with an IR window and without an IR window have been compared.

Traceable measurements of harmonic (2 to 150) kHz emissions in smart grids: uncertainty calculation

The necessity of measuring harmonic emissions between 2 and 150 kHz is outlined by several standard committees and electrical utilities. This paper presents a measurement system and its traceable characterization designed to acquire and analyse voltages up to 230 V and currents up to 100 A with harmonics up to 150 kHz that may occur in smart grids. The uncertainty estimation is carried out and described in detail for both the fundamental and supraharmonics components. From a metrological point of view, ensuring the traceability of current measurements for frequencies higher than 100 kHz and dealing with the complexity of uncertainty determination are bottlenecks related to supraharmonics measurements that this paper proposes an approach to deal with.

Modelling complex measurement processes for measurement uncertainty determination

Purpose Measurement uncertainty is present in all measurement processes in the field of production engineering. However, this uncertainty should be minimized to avoid erroneous decisions. Present methods to determine the measurement uncertainty are either only applicable to certain processes and do not lead to valid results in general or require a high effort in their application. To optimize the costs and benefits of the measurement uncertainty determination, a method has to be developed which is valid in general and easy to apply. The paper aims to discuss these issues. Design/methodology/approach This paper presents a new technique for determining the measurement uncertainty of complex measurement processes. The approximation capability of artificial neural networks with one hidden layer is proven for continuous functions and represents the basis for a method for determining a measurement model for continuous measurement values. Findings As this method does not require any previous knowledge or expertise, it is easy to apply to any measurement process with a continuous output. Using the model equation for the measurement values obtained by the neural network, the measurement uncertainty can be derived using common methods, like the Guide to the expression of uncertainty in measurement. Moreover, a method for evaluating the model performance is presented. By comparing measured values with the output of the neural network, a range in which the model is valid can be established. Combining the evaluation process with the modelling itself, the model can be improved with no further effort. Originality/value The developed method simplifies the design of neural networks in general and the modelling for the determination of measurement uncertainty in particular.

Development of a virtual metrological CT for numerical measurement uncertainty determination using aRTist 2

The development of a virtual metrological CT for numerical measurement uncertainty determination at the Institute of Manufacturing Metrology (Friedrich-Alexander-University Erlangen-Nuremberg (FAU), Germany) using the software aRTist 2 by the BAM German Federal Institute for Materials Research and Testing is described. The virtual metrological CT uses a Monte-Carlo approach for numerical measurement uncertainty determination. Results demonstrating that numerical uncertainty determination according to GUM Supplement 1 and in accordance with uncertainty determination according to guideline VDI/VDE 2630 Part 2.1 is possible for selected measurement tasks are presented.