A Rule Based Expert System for Identification of Harmonics Originating from Single Phase Nonlinear Loads

Mohamed, Azah; Umeh, K.C.; Hussain, Aini; Mohamed, Ramizi

doi:10.2202/1553-779x.1161

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…Manmek et al (2004) used the linear least-squares algorithm for a real-time measurement technique that could track the harmonics in power system waveforms and it did not require matrix inversion. Mohamed et al (2006) proposed a rule-based expert system for the identification of harmonic sources originating from various single-phase non-linear loads such as uninterruptible power supply, personal computer, fluorescent lamp with magnetic and electronic ballasts, PC monitor and oscilloscope. A method based on a real coded genetic algorithm was developed by Zamanan et al (2006) for identification and tracking of harmonics in a power system.…”

Section: Introductionmentioning

confidence: 99%

Identification of acceleration harmonic for an electro-hydraulic servo shaking table based on Kalman filter

Yao

Jiang

et al. 2013

Transactions of the Institute of Measurement and Control

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Non-linearities commonly exist in an electro-hydraulic servo shaking table, causing acceleration harmonics distortion when the shaking table is excited by a sinusoidal acceleration signal, because its acceleration response includes higher harmonics, which lower the control performance for an electro-hydraulic servo shaking table. To cancel the harmonics in the system response, thus to improve the shaking table performance, we need to know about the harmonics information. An identification algorithm is developed here based on a Kalman filter for dynamically tracking the acceleration harmonics for the electro-hydraulic servo shaking table. A linear system in state space is modelled. The system acceleration response is applied as an observation value and is imported to the Kalman filter, which recursively estimates the state vector of the linear system. The amplitude and phase of each harmonic are calculated from the estimated state vector, and their estimated values are validated. A simulation example is presented and experiments were performed on the electro-hydraulic servo shaking table. Both results show a good estimation performance of the proposed acceleration harmonic identification algorithm.

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Section: Introductionmentioning

confidence: 99%