Jianxin Lin
ABSTRACT
In the transient stability assessment, machine learning based model has the problem that sample imbalance makes the model have a certain degree of evaluation tendency. From the loss function of the machine learning based model, it is found that the fitting degree to various samples after training can be mirrored by the loss function value of the samples. Therefore, a cost-sensitive strategy based on the imbalance degree of the loss function is proposed. Firstly, the machine learning based model is trained to obtain the mean value of loss functions of various samples. Then, the sample imbalance degree is calculated by the mean ratio of the loss function of unstable samples to that of stable samples. After that, the loss function is modified by the sample imbalance degree combined with the cost-sensitive strategy. Finally, to remedy the assessment inclination, the model is trained once again. When compared to conventional approaches, the suggested method comprehensively considers the impact of the imbalance in quantity and spatial distribution of samples. This model achieves higher global accuracy and greater corrective effect. Simulation findings on IEEE 39-bus and IEEE 145-bus systems are used to confirm the usefulness of the suggested strategy.
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Index Terms
- Imbalanced correction method for power systems transient stability assessment based on the imbalance degree of image samples
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