Machine Learning-Based Reliability Improvement of Ambient Mode Extraction for Smart Grid Utilizing Isolation Forest
Han Gao, Deyou Yang, Guowei Cai, Zhe Chen, Jin Ma, Lixin Wang, Fangwei Duan
Abstract
Reliable and accurate extraction of ambient modes is an essential means of assessing the safety and stability of smart grids. While previous works have mainly concentrated on the introduction of novel identification tools. In this paper, the isolation forest ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iForest</i> ) was presented to build an intelligent ambient mode extraction scheme with high reliability, which aims at detecting and eliminating abnormal ambient modes from extracted results. As the core technology of the proposed intelligent scheme, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iForest</i> exploits the concept of isolation and intends to combine the features of the outliers, which are ‘few’ and ‘different’, with the technique of ensemble learning to isolate them precisely. And the abnormal modes can be eliminated according to their anomaly scores in the proposed intelligent ambient mode extraction scheme. The evaluation of the proposed scheme is carried out through the database constructed by an IEEE 16-generator system and a real power system, the results of which indicate that the proposed scheme is intelligent enough to deal with a large amount of data with high accuracy and improve the reliability of existing identification tools.