Harmonics Estimation of a Noisy Power System Signal using Cubature Kalman Filter

被引：0

作者：

Pramanik, Meghabriti ^{[1
]}

Ghosh, Agnimesh ^{[2
]}

Routray, Aurobinda ^{[3
]}

Mitra, Pabitra ^{[4
]}

机构：

[1] Indian Inst Technol, Adv Technol Dev Ctr, Kharagpur 721302, W Bengal, India

[2] Manipal Inst Technol, Dept Instrumentat & Control Engn, Manipal 576104, Karnataka, India

[3] Indian Inst Technol, Dept Elect Engn, Kharagpur 721302, W Bengal, India

[4] Indian Inst Technol, Dept Comp Sci & Engn, Kharagpur 721302, W Bengal, India

来源：

IECON 2018 - 44TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY | 2018年

关键词：

Cubature Kalman filter(CKF); nonlinear dynamic system; parameters estimation; power system harmonics; unscented Kalman filter(UKF); ALGORITHM; TRACKING;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Fast and accurate estimation of harmonics of a typical power system signal is very much desirable for power quality assessment. This paper proposes an application of the cubature Kalman filter (CKF) for estimating the parameters of harmonics, sub-harmonics, and inter-harmonics of a signal in presence of noise. CKF utilizes a third-degree spherical radial cubature rule to estimate the probability density functions of the states as well as the measurements. At the same time, this technique does not require any linearization and saves the computational time. The effectiveness of CKF has been compared with UKF by performing various test cases. It is observed from the simulation results that CKF exhibits superior performance in estimating the parameters of harmonics, inter-harmonics, and sub-harmonics of a distorted power system static as well as the dynamic signal by virtue of execution time and accuracy.

引用

页码：170 / 175

页数：6

共 50 条

[31] Improved mixture correntropy cubature Kalman filter for attitude estimation
Qiao, Meiying
Gao, Kefei
Qiu, Yunqiang
Han, Haotian
MEASUREMENT SCIENCE AND TECHNOLOGY, 2024, 35 (09)
[32] State estimation based on improved cubature Kalman filter algorithm
Zhu, Jun
Liu, Bingchen
Wang, Haixing
Li, Zihao
Zhang, Zhe
IET SCIENCE MEASUREMENT & TECHNOLOGY, 2020, 14 (05) : 536 - 542
[33] Nonlinear and Constrained State Estimation Based on the Cubature Kalman Filter
Zarei, Jafar
Shokri, Ehsan
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2014, 53 (10) : 3938 - 3949
[34] Double-Layer Cubature Kalman Filter for Nonlinear Estimation
Yang, Feng
Luo, Yujuan
Zheng, Litao
SENSORS, 2019, 19 (05)
[35] Fast recursive Gauss-Newton adaptive filter for the estimation of power system frequency and harmonics in a noisy environment
Dash, P. K.
Krishnanand, K. R.
Padhee, M.
IET GENERATION TRANSMISSION & DISTRIBUTION, 2011, 5 (12) : 1277 - 1289
[36] Railway Suspension System Fault Diagnosis using Cubature Kalman Filter Techniques
Zoljic-Beglerovic, Selma
Stettinger, Georg
Luber, Bernd
Horn, Martin
IFAC PAPERSONLINE, 2018, 51 (24): : 1330 - 1335
[37] Noisy Reading Correction in Low Power MPPT using Kalman Filter
Haidar, Mohammad
Chible, Hussein
Caviglia, Daniele D.
2019 26TH IEEE INTERNATIONAL CONFERENCE ON ELECTRONICS, CIRCUITS AND SYSTEMS (ICECS), 2019, : 133 - 134
[38] Nonlinear filtering for sequential spacecraft attitude estimation with real data: Cubature Kalman Filter, Unscented Kalman Filter and Extended Kalman Filter
Garcia, R. V.
Pardal, P. C. P. M.
Kuga, H. K.
Zanardi, M. C.
ADVANCES IN SPACE RESEARCH, 2019, 63 (02) : 1038 - 1050
[39] Estimation of power quality using an unscented Kalman filter
Zhang, Jian
Swain, Akshya
Nair, Nirmal-Kumar C.
Liu, J. J.
TENCON 2007 - 2007 IEEE REGION 10 CONFERENCE, VOLS 1-3, 2007, : 393 - +
[40] Estimation of Voltage Signal Analysis using Extended Kalman Filter
Muthupandi, G.
Elango, S.
Manikandan, V
2014 INTERNATIONAL CONFERENCE ON COMMUNICATIONS AND SIGNAL PROCESSING (ICCSP), 2014,

← 1 2 3 4 5 →