ELM-ANFIS based controller for plug-in electric vehicle to grid integration [articol]

Abstract

In this paper, the authors propose adaptive neuro fuzzy inference system (ANFIS) algorithm, based on extreme learning machine (ELM) concepts for designing a controller for electric vehicle to grid (V2G) integration. First, learning speed and accuracy of the proposed algorithm is checked and second the transient response of the ELM-ANFIS (e-ANFIS) based controller is analyzed. The proposed new learning technique overcomes the slow learning speed of the conventional ANFIS algorithm without sacrificing the generalization capability. Thus, even with an involvement of a large number of plug-in hybrid electric vehicles (PHEV), a control technique for their charge and discharge pattern can be easily designed. To study the computational performance and transient response of the e-ANFIS based controller, it is compared with conventional ANFIS based controller. To implement the vehicle to grid integration concept, IEEE 33 bus radial distribution system is modelled in MATLAB environment.