Dual cost‐sensitivity factors‐based power system transient stability assessment. Issue 24 (5th August 2020)

• Record Type: Journal Article
• Authors: Li, Nan; Li, Baoluo; Han, Yongqiang; Gao, Lei
• Is Part Of: IET generation, transmission & distribution. Volume 14:Issue 24(2020)
• Page Start: 5858
• Page End: 5869
• Publication Date: 2020-08-05
• Subjects: learning (artificial intelligence) -- power system transient stability -- power system simulation -- power engineering computing -- data handling; power system operation -- misclassification costs -- data‐driven method -- TSA method -- balance factor -- modulating factor -- imbalanced sample training process -- incremental learning -- memory function -- light gradient boosting machine -- power system transient stability assessment -- dual cost‐sensitivity factors; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2020.0365
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : In actual power system operation, there is a significant imbalance between the number of stable and unstable samples, and the misclassification costs of the two classes of samples are different. In addition to the class imbalance, there is another imbalance of regional samples in the feature space. In order to reduce the impact of class imbalance and regional imbalance on the performance of the model, a method of dual cost‐sensitivity factors for transient stability assessment is proposed. The method sets the balance factor and modulating factor in the loss function of light gradient boosting machine. The former corrects the classification bias caused by class imbalance, and the latter focuses on improving the classification accuracy of overlapping area samples. Combining the two factors, the model not only improves the accuracy of unstable samples, but also reduces the misjudgement of stable samples. In online application, a fast update scheme with memory function is proposed. In this scheme, incremental learning is used to update the model with fewer samples and less computational time, so as to achieve better evaluation performance. Case studies on three power systems demonstrate the generalisation performance of the proposed model and the effectiveness of the update scheme.