In this paper, the characteristics of power oscillation of pumped storage synchronous motor under pumped condition are studied, and the causes of misoperation of conventional low power protection are analyzed. Based on digital simulation and actual data analysis, a multi-stage low power protection is proposed. The power setting value of each section matches the delay setting value, which can adapt to the characteristics of motor power oscillation and effectively solve the misoperation problem of conventional low power protection. The simulation results show that its performance is stable and reliable, and the problem of misoperation of conventional low power protection is solved.
The current traditional deep learning-based sound recognition algorithm achieves the classification and recognition of sound by constructing MLP models, which leads to a large computational effort and low sound recognition rate due to the lack of dimensionality reduction processing of sound signal features. In this regard, we propose the study of sound recognition algorithm for power plant equipment by fusing MFCC and IMFCC features. Pre-processing such as pre-emphasis, normalization and framing of the sound signal is performed, and a decentralized approach is used to realize the dimensionality reduction of the sound signal features and achieve the recognition of power plant equipment sounds. In the experiments, the average recognition rate of the proposed algorithm is verified. The analysis of the experimental results shows that the sound recognition algorithm constructed by the proposed method has a high average recognition rate and a good recognition effect.
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