@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},
}

{"_id":"Tvh4j5twyJ4FNAzP2","bibbaseid":"arablouei-doganay-werner-recursivetotalleastsquaresestimationoffrequencyinthreephasepowersystems-2014","authorIDs":[],"author_short":["Arablouei, R.","Dogançay, K.","Werner, S."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["R."],"propositions":[],"lastnames":["Arablouei"],"suffixes":[]},{"firstnames":["K."],"propositions":[],"lastnames":["Dogançay"],"suffixes":[]},{"firstnames":["S."],"propositions":[],"lastnames":["Werner"],"suffixes":[]}],"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","bibtex":"@InProceedings{6952846,\n author = {R. Arablouei and K. Dogançay and S. Werner},\n booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},\n title = {Recursive total least-squares estimation of frequency in three-phase power systems},\n year = {2014},\n pages = {2330-2334},\n 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.},\n 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},\n issn = {2076-1465},\n month = {Sep.},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569909319.pdf},\n}\n\n","author_short":["Arablouei, R.","Dogançay, K.","Werner, S."],"key":"6952846","id":"6952846","bibbaseid":"arablouei-doganay-werner-recursivetotalleastsquaresestimationoffrequencyinthreephasepowersystems-2014","role":"author","urls":{"Paper":"https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569909319.pdf"},"keyword":["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"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/Roznn/EUSIPCO/main/eusipco2014url.bib","creationDate":"2021-02-13T17:43:41.784Z","downloads":0,"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"],"search_terms":["recursive","total","squares","estimation","frequency","three","phase","power","systems","arablouei","dogançay","werner"],"title":"Recursive total least-squares estimation of frequency in three-phase power systems","year":2014,"dataSources":["A2ezyFL6GG6na7bbs","oZFG3eQZPXnykPgnE"]}