In this paper, a fuzzy-based paradigm for data compression aimed at reducing the computational burden of data analysis in smart grids (SGs) is proposed. In the SG context, it is challenging achieving an efficient use of the channel communication bandwidth and a reduced need of the storage space for operational data. Thus, we discuss a fuzzy-based mathematical kernel which transforms the data into a new domain where their cardinality can be sensibly reduced, consequently allowing the development of more efficient data analysis algorithms. Detailed numerical results obtained from several test systems are presented and discussed in order to demonstrate the effectiveness of the proposed approach to handle SG operation problems.
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Loia et al. (2016) studied this question.
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