| Summary: | 碩士 === 國立中央大學 === 電機工程學系 === 106 === A distribution static compensator (DSTATCOM) is proposed to improve power quality, including the grid current harmonic and power factor, resulted from the nonlinear and linear loads. On the other hands, since the instantaneous power following into or out of the DC-link capacitor on the DC side of the DSTATCOM, a sudden load change may cause a serious DC-link voltage fluctuation across the dc capacitor. Hence, the DC-link voltage regulation control of the DSTATCOM is important especially under load variation. In this study, to improve the power quality and keep the DC-link voltage of the DSTATCOM constant under variation of nonlinear and linear loads effectively, the traditional proportional-integral (PI) controller is substituted with a novel online trained compensatory neural fuzzy network with an asymmetric membership function (CFNN-AMF) controller. In the proposed CFNN-AMF, the compensatory parameter to integrate pessimistic and optimistic operations of fuzzy systems is embedded in the CFNN. Moreover, the dimensions of the Gaussian membership functions are directly extended to AMFs for the optimization of the fuzzy rules and the upgrade of learning ability of the networks. Furthermore, the network structure and online learning algorithms of the proposed CFNN-AMF are introduced in detail. Finally, the effectiveness and feasibility of the DSTATCOM using the proposed CFNN-AMF controller for the improvement of power quality and maintaining the constant DC-link voltage under nonlinear and linear load change have been demonstrated by some experimental results.
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