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https://doi.org/10.17559/TV-20150209073924

ELM-ANFIS Based Controller for Plug-In Electric Vehicle to Grid Integration

Kalaiselvi Kandasamy ; Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai-625015, India
Renuga Perumal ; Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai-625015, India
Suresh Kumar Velu ; Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai-625015, India

Puni tekst: engleski, pdf (600 KB) str. 15-22 preuzimanja: 153* citiraj
APA 6th Edition
Kandasamy, K., Perumal, R. i Velu, S.K. (2018). ELM-ANFIS Based Controller for Plug-In Electric Vehicle to Grid Integration. Tehnički vjesnik, 25 (Supplement 1), 15-22. https://doi.org/10.17559/TV-20150209073924
MLA 8th Edition
Kandasamy, Kalaiselvi, et al. "ELM-ANFIS Based Controller for Plug-In Electric Vehicle to Grid Integration." Tehnički vjesnik, vol. 25, br. Supplement 1, 2018, str. 15-22. https://doi.org/10.17559/TV-20150209073924. Citirano 18.07.2019.
Chicago 17th Edition
Kandasamy, Kalaiselvi, Renuga Perumal i Suresh Kumar Velu. "ELM-ANFIS Based Controller for Plug-In Electric Vehicle to Grid Integration." Tehnički vjesnik 25, br. Supplement 1 (2018): 15-22. https://doi.org/10.17559/TV-20150209073924
Harvard
Kandasamy, K., Perumal, R., i Velu, S.K. (2018). 'ELM-ANFIS Based Controller for Plug-In Electric Vehicle to Grid Integration', Tehnički vjesnik, 25(Supplement 1), str. 15-22. doi: https://doi.org/10.17559/TV-20150209073924
Vancouver
Kandasamy K, Perumal R, Velu SK. ELM-ANFIS Based Controller for Plug-In Electric Vehicle to Grid Integration. Tehnički vjesnik [Internet]. 2018 [pristupljeno 18.07.2019.];25(Supplement 1):15-22. doi: https://doi.org/10.17559/TV-20150209073924
IEEE
K. Kandasamy, R. Perumal i S.K. Velu, "ELM-ANFIS Based Controller for Plug-In Electric Vehicle to Grid Integration", Tehnički vjesnik, vol.25, br. Supplement 1, str. 15-22, 2018. [Online]. doi: https://doi.org/10.17559/TV-20150209073924

Sažetak
An Adaptive Neuro Fuzzy Inference System (ANFIS) based Extreme Learning Machine (ELM) theory is utilised in this research work. In particular, the proposed algorithm is applied for designing a controller for electric vehicle to grid (V2G) integration in smart grid scenario. Initially, learning speed and accuracy of this proposed approach are continuously monitored and then, the performance of ELM-ANFIS (e-ANFIS) based controller is examined for its transient response. The proposed new learning technique overcomes the slow learning speed of the conventional ANFIS algorithm without sacrificing the generalization capability. Hence, a control practice for their charge and discharge patterns can be easily calculated even with the presence of large numbers of Plug-in Hybrid Electric Vehicles (PHEV). To examine the computational performance and transient response of the e-ANFIS based controller, it is evaluated with the usual ANFIS supported controller. The IEEE 33 bus radial distribution system based approach is implemented to ensure the sturdiness of this prescribed approach.

Ključne riječi
ANFIS; distribution system; electric vehicle; extreme learning machine; grid integration

Hrčak ID: 200593

URI
https://hrcak.srce.hr/200593

Posjeta: 267 *