Currently, transient stability assessment based on deep learning requires offline generation of a large amount of data to train predictive models. However, when there is insufficient training data, the accuracy of the model's predictions significantly decreases. To address this challenge, this paper proposes a method for selecting and enhancing key samples. Firstly, a quantitative analysis of the correlation between samples and the training of the model and transient stability boundaries is conducted. Based on this analysis, a sample importance index is defined, and key samples are selected based on this index. Then, these key samples are enhanced using Generative Adversarial Networks (GANs) to improve the representation capability of the sample set and help the transient stability model learn classification boundaries that better fit real transient stability boundaries. This method is applied to the New England 10-machine 39-node system and a provincial-level power grid in China. The test results show that with the enhancement of key samples to 10%, the misclassification rate of the predictive model decreased by 86.54%. This approach significantly improves the predictive capability of the model under the condition of limited training samples.
When the frequency of power signal is offset, it is difficult to achieve synchronous sampling by using discrete Fourier transform in synchronous phasor measurement. The fence effect caused by this will seriously affect the precision of synchronous phasor measurement, especially the precision of phase measurement. Therefore, this paper proposes an improved DFT synchronous phasor measurement algorithm. Firstly, an algorithm for frequency tracking, which is based upon the extended Kalman filter, is put forward. Besides, the algorithm for particle swarm optimization is described with a view to optimizing the noise weight of Kalman gain. Based on the frequency offset rate obtained by frequency tracking, the improved DFT algorithm is adopted for the sake of attaining the corrected phase result. These consequences of simulation indicate that the frequency tracking error of this algorithm doesn`t reach 0.015Hz, and the phase calculation error doesn`t reach 0.018°, which satisfies the accuracy engineering demands, and the convergence speed is fast.
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