Open AccessTransient stability assessment combined model framework based on cost‐sensitive method
Author(s) -
Wang Huaiyuan,
Chen Qifan,
Zhang Baohui
Publication year - 2020
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2019.1562
Subject(s) - computer science , transient (computer programming) , frame (networking) , stability (learning theory) , electric power system , fault (geology) , scheme (mathematics) , artificial intelligence , power (physics) , machine learning , data mining , reliability engineering , engineering , operating system , telecommunications , mathematical analysis , physics , mathematics , quantum mechanics , seismology , geology
The real‐time transient stability assessment (TSA) is critical for emergency control of power systems. Accurate and fast TSA can provide an important basis for post‐fault control of power systems. At present, the accuracy of the evaluation model based on machine learning is very high, but there are still some misjudgements in the results. In order to build a high‐accuracy evaluation model, a novel frame based on the cost‐sensitive method is proposed. Firstly, a deep belief network (DBN) is applied to build a TSA frame. The DBN is effectively trained by means of pre‐training and fine tuning. Secondly, two models with the opposite preference are constructed based on the cost‐sensitive method. Based on the output results of the two models, the deterministic or uncertain evaluation results are obtained. The samples that may be misclassified are divided into uncertain evaluation results. Thus, the accuracy of the deterministic evaluation results can be improved greatly. Through this design, the practicability of the evaluation model based on machine learning is greatly improved. The effectiveness of the proposed scheme is verified by the simulation results in the IEEE‐39 bus system and a realistic regional system.