Updating stochastic model coefficients for prediction of arc furnace reactive power

The time varying nature of electric arc furnace (EAF) gives rise to voltage fluctuations which produce the effect known as flicker. The ability of static VAr compensator (SVC) in flicker reduction is limited by delays in thyristor ignition. To improve SVC performance in flicker compensation, EAF reactive power can be predicted for a half-cycle ahead, by using appropriate autoregressive moving average (ARMA) models. This paper uses huge field data collected from ac arc furnaces, and demonstrates that the EAF reactive power models coefficients are different from one data record to another and do not follow any specific law. Therefore, it is necessary to update the model coefficients for prediction purposes. For this purpose, two major adaptation algorithms, the least mean square (LMS) and recursive least square (RLS) are used to determine online the prediction relationship coefficients. By applying the methods to the data records and using some indices such as newly defined indices based on concepts of flicker frequencies and power spectral density, the transient and steady state performances of the methods are studied in EAF reactive power prediction. A simulation example on the application of the predictive models in a SVC control system is presented.