Energies 2017, 10(4), 536; doi:10.3390/en10040536
Validation of the Measurement Characteristics in an Instrument for Power Quality Estimation—A Case Study
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Marine Electrical Engineering Faculty, Gdynia Maritime University, 81-225 Gdynia, Poland
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This is an expanded version of a conference paper: Masnicki, R. Validation of the measurement algorithms in instrument for power quality estimation. In Proceedings of the IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC), Florence, Italy, 7–10 June 2016.
Academic Editor: Rodolfo Araneo
Received: 15 February 2017 / Revised: 3 April 2017 / Accepted: 6 April 2017 / Published: 15 April 2017
(This article belongs to the Special Issue Selected Papers from 16 IEEE International Conference on Environment and Electrical Engineering (EEEIC 2016))
Abstract
An acceptable quality of electrical energy is seen today as an important component of ecology. Several instruments for estimating the quality of electrical power have been elaborated. Each supplier assures that the instrument meets the applicable standards and that the uncertainty of the measurement results obtained using the instrument does not exceed the established levels. The accuracy of the measurement results depends on a couple of things, e.g., the correctness of the measurement algorithms implemented in the instrument and the quality of its calibration. In this paper, the basic features of an “estimator/analyzer” (E/A) instrument, as well as the calibration methods of the instrument, the verification of its measurement algorithms, and also the obtained exemplary results, are shown. The proposal of the strategy of the reliable validation of embedded measurement algorithms for the identification of parameters characterizing electrical power quality in the power grid is discussed. View Full-TextKeywords:
electrical power quality estimation; measurement algorithm; measurement track examination; validation procedures
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Masnicki, R. Validation of the Measurement Characteristics in an Instrument for Power Quality Estimation—A Case Study
. Energies 2017, 10, 536.
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