Recursive total least-squares estimation of frequency in three-phase power systems. Arablouei, R., Dogançay, K., & Werner, S. In *2014 22nd European Signal Processing Conference (EUSIPCO)*, pages 2330-2334, Sep., 2014.

Paper abstract bibtex

Paper abstract bibtex

We propose an adaptive algorithm for estimating the frequency of a three-phase power system from its noisy voltage readings. We consider a second-order autoregressive linear predictive model for the noiseless complex-valued αβ signal of the system to relate the system frequency to the phase voltages. We use this model and the noisy voltage data to calculate a total least-square (TLS) estimate of the system frequency by employing the inverse power method in a recursive manner. Simulation results show that the proposed algorithm, called recursive TLS (RTLS), outperforms the recursive least-squares (RLS) and the bias-compensated RLS (BCRLS) algorithms in estimating the frequency of both balanced and unbalanced three-phase power systems. Unlike BCRLS, RTLS does not require the prior knowledge of the noise variance.

@InProceedings{6952846, author = {R. Arablouei and K. Dogançay and S. Werner}, booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)}, title = {Recursive total least-squares estimation of frequency in three-phase power systems}, year = {2014}, pages = {2330-2334}, abstract = {We propose an adaptive algorithm for estimating the frequency of a three-phase power system from its noisy voltage readings. We consider a second-order autoregressive linear predictive model for the noiseless complex-valued αβ signal of the system to relate the system frequency to the phase voltages. We use this model and the noisy voltage data to calculate a total least-square (TLS) estimate of the system frequency by employing the inverse power method in a recursive manner. Simulation results show that the proposed algorithm, called recursive TLS (RTLS), outperforms the recursive least-squares (RLS) and the bias-compensated RLS (BCRLS) algorithms in estimating the frequency of both balanced and unbalanced three-phase power systems. Unlike BCRLS, RTLS does not require the prior knowledge of the noise variance.}, keywords = {adaptive signal processing;autoregressive processes;frequency estimation;inverse problems;power grids;regression analysis;recursive total least-squares frequency estimation;noisy voltage readings;adaptive algorithm;second-order autoregressive linear predictive model;noiseless complex-valued αβ signal;phase voltages;inverse power method;recursive TLS;unbalanced three-phase power systems;balanced three-phase power systems;adaptive signal processing;electric power grids;Frequency estimation;Power systems;Steady-state;Noise measurement;Signal to noise ratio;Estimation;Adaptive signal processing;frequency estimation;inverse power method;linear predictive modeling;total least-squares}, issn = {2076-1465}, month = {Sep.}, url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569909319.pdf}, }

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