A study of frequency prediction for power systems

A frequency predictor is identified from simulated measurements of power and frequency on a power system. An on-line Ieast-squares algorithm is used along with a new system structure test for model order identification. A comparison of this system structure test with other model order identification tests is also included. The performance of the resultant predictor is then determined as a function of both the prediction interval and the sampling rate and measurement noise levels on the power and frequency measurements used for the predictor. The results indicate an increase in prediction error with the length of the prediction interval because the predictor loses its principal dependence of "P-f" (power-frequency) dynamics in the power system and depends more strongly on the random load fluctuations over the prediction interval. The modeling error was shown to be unaffected by sampling rate and by measurement noise levels below that of the present power-frequency recorder [2], but was affected by measurement noise levels above the values on the present recorder. This accuracy of the model for small prediction intervals justifies the future use of frequency measurements in power system identification and justifies the use of least-squares algorithms using these measurements. The results on sampling rate and measurement noise imply that the present recorder [2] is an "optimal" design and that the RTDAS [5] will be an even better tool for use in power system model identification.