http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
볼테라 시리즈 입력을 이용한 냉연 산세 라인 산농도 모델 추정
박찬은(Chan Eun Park),송주만(Ju-man Song),박태수(Tae Su Park),노일환(Il-Hwan Noh),박형국(Hyoung-Kuk Park),최승갑(Seung Gab Choi),박부견(PooGyeon Park) 제어로봇시스템학회 2015 제어·로봇·시스템학회 논문지 Vol.21 No.12
This paper deals with estimating the acid concentration of pickling process using the Volterra inputs. To estimate the acid concentration, the whole pickling process is represented by the grey box model consists of the white box dealing with known system and the black box dealing with unknown system. Because there is a possibility of nonlinear term in the unknown system, the Volterra series are used to estimate the acid concentration. For the white box modeling, the acid tank solution level and concentration equations are used, and for the black box modeling, the acid concentration is estimated using the Volterra Least Mean Squares (LMS) algorithm and Least Squares (LS) algorithm. The LMS algorithm has the advantage of the simple structure and the low computation, and the LS algorithm has the advantage of lowest error. The simulation results compared to the measured data are included.
Distribution System Dynamic State Estimation via Mathematical Model Based Approach
Chan-eun Park(박찬은),In Seok Park(박인석),PooGyeon Park(박부견) 대한전기학회 2019 전기학회논문지 Vol.68 No.7
Recently, some novel researches regarding the state estimation for power distribution system have been focused on the dynamic state estimation algorithm due to trend abrupt changes in the distribution system such as actions of prosumers. The previous works studied the Kalman filter for nonlinear system such as extended Kalman filter, unscented Kalman filter. They assume that the state transient model follows smoothing model of the previous states. However, it is hard to set the smoothing coefficient for the estimated states. Thus, this paper proposes the mathematical model of state transient model of power distribution system. Based on the proposed mathematical model, extended Kalman filter algorithm is adopted for dynamic state estimation. All jacobian matrices for the extended Kalman filter is derived as a function of system states, and the proposed algorithm is verified by using IEEE 15 test bus.