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access icon free Development of EV charging templates: an improved K-prototypes method

  • Author(s): Juhua Hong 1 ; Yue Xiang 1 ; Youbo Liu 1 ; Junyong Liu 1 ; Ran Li 2 ; Furong Li 2 ; Jing Gou 3
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  • Source: Volume 12, Issue 20, 13 November 2018, p. 4361 – 4367
    DOI:  10.1049/iet-gtd.2017.1911 , Print ISSN 1751-8687, Online ISSN 1751-8695
© The Institution of Engineering and Technology
Received 06/12/2017, Accepted 25/04/2018, Revised 13/04/2018, Published 04/05/2018

In order to manage the charging behaviour of electric vehicles (EVs), this study for the first time develops a set of EV charging load profiles: EV templates. EV charging profiles have unique waveforms similar to a rectangular pulse train. This characteristics significantly limits the performance of clustering analysis in that traditional distance calculation, such as Euclidean distance, which cannot reflect the morphological dissimilarities. This study proposes a novel clustering method using rough set theory to accurately measure the dissimilarity between the EV profiles. The pulse train waves are firstly extracted as mixed data features, which are partitioned by an improved K-prototypes method based on rough set distance. The proposed method is implemented on the real charging load profiles and compared with K-means and traditional K-prototypes. Their clustering performances are evaluated by diverse validity indices. The results show that the proposed method outperforms other comparison methods.

Inspec keywords: feature extraction; pattern clustering; rough set theory; electric vehicle charging

Other keywords: rectangular pulse train; EV charging load profiles; traditional distance calculation; rough set theory; improved K-prototypes method; clustering analysis; mixed data feature extraction; pulse train waves; electric vehicle charging behaviour; EV charging templates; morphological dissimilarities; Euclidean distance

Subjects: Transportation; Combinatorial mathematics; Signal processing and detection

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