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Published on: December 14, 2017
Comprehensive power quality performance assessment for electrical system of a nuclear research reactor
Asmaa M Elsotohy1, Ahmed Mohammed Attiya Soliman2, Ahmed S Adail3
1Nuclear Safety and Radiation Emergencies Research Department, NCRRT, Egyptian Atomic Energy Authority, Cairo, Egypt. Asmaa.sotohy@eaea.org.eg.
Abstract:
Studying the power quality (PQ) is an essential issue to ensure the safe and accurate operation of sensitive equipment particularly for nuclear installations. Assessment of PQ involves collecting and analysing data resources and then evaluating it with reference to PQ standards. There are many alternatives for PQ and it is difficult to make an appropriate selection among them in the existence of their multiple criteria which are usually conflicted. So this selection subject can be classified as a Multi Criteria Decision Making (MCDM) problem. To do so, a reliable and scientific method for studying and evaluating the overall system PQ is required. This study aims to assess performance of PQ for the electrical power system at a Nuclear Research Reactor (NRR) during a certain period using multiple measures for the most decisive PQ phenomena. It focuses on a number of the most important PQ phenomena namely frequency fluctuation (deviation), unbalances of current and voltage, current and voltage harmonic distortion, flicker and power factor. After combining all results into six samples (alternatives), the criteria weights are determined based on an objective method for weighting which is called CRITIC method. Then, the alternatives are ranked using compromise MCDM method-VIKOR method. The obtained results are analyzed and discussed to evaluate performance of NRR electrical system from the PQ view. It showed that the compromise solution that obtained by CRITIC-VIKOR can be a guide to facilitate the PQ evaluation of nuclear installation electrical system. Also, it can empower the operators with the benefits of benchmarking and monitoring a single index instead of several indices. Moreover, it is very useful for helping stakeholders to understand how the PQ performance changes under a certain operating condition of the facility. Finally, it is can be considered as a good model to weight each PQ phenomena and identify the time intervals for best and worst total PQ in NRR.
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