A Hybrid Forecasting Method for Wind Power Ramp Based on Orthogonal Test and Support Vector Machine (OT-SVM)

Abstract

In an electric power system with a high penetration of wind power, incoming power ramps pose a serious threat to the power system. To adopt suitable response strategies for wind power ramps, it is important to predict them accurately and in a timely manner. Since power ramps are caused by various factors, their occurrence has irregular characteristics and vary by location, bringing great difficulty in forecasting. To solve this problem, a hybrid forecasting model termed as orthogonal test and support vector machine (OT-SVM) was developed in this paper, which combines an orthogonal test (OT) with a support vector machine (SVM). A novel factor analysis method was established based on the theory of the OT, and applied to determine the optimal inputs of a SVM. The effectiveness of OT-SVM was tested with three wind farms in China, while comparing the results with other related methods. The results show that the proposed OT-SVM has the highest accuracy covering different input numbers and time resolutions. In addition, a novel evaluation index mean accuracy index was proposed, considering both the missed ramps and false ramps, which can be used as a supplementary index for critical success index.