Online dynamic total transfer capability estimation using cotraining-style semi-supervised regression

Abstract

With the increasing integration of wind power, the operating condition of the power system varies more rapidly. As the total transfer capability (TTC) of the transmission interface changes with the operating condition, the offline TTC estimation has become less suitable for online security control. In this paper, an efficient online dynamic TTC estimation method using semi-supervised learning approach is proposed. First, considering the high-order uncertainties of wind and load, a sample database of expected operating conditions with or without corresponding TTCs is generated. Then, the pivotal features which greatly correlate with the TTC are selected. Finally, the relationship between the TTC and pivotal features is learned, using the cotraining-style semi-supervised regression algorithm (COREG), thus the dynamic TTC estimation model is established. With real-time data inputting the model, the TTC can be estimated. The proposed method is validated on Gansu Province Power Grid in China, and the results and accuracy and efficiency comparison with other typical existing methods indicate that, the proposed method can provide accurate TTC estimation, and because of the high efficiency of the semi-supervised learning approach, the whole process of model establishment and TTC estimation can be refreshed every 15 minutes. Therefore, the proposed method of online dynamic TTC estimation is suitable for online security control.