基于增强型去噪自编码器与随机森林的电力系统扰动分类方法
摘要：实时准确的电力系统扰动分类有利于避免大规模停电事故的发生。然而同步相量测量单元的数据质量问题严重影响其在扰动分类上的应用。针对此问题，提出了一种基于增强型去噪自编码器与随机森林的扰动分类方法。首先，利用长短期记忆构造一种增强型去噪自编码器，建立不良数据与正常数据间的映射关系。进一步，根据不同量测的验证损失变化趋势，提出了一种自适应权重多任务去噪网络，能够自适应更新各量测对应的损失函数权重以降低重构误差。最后，利用随机森林对特征进行分类，并通过贝叶斯优化对其超参数调优。基于IEEE 39系统，在不同不良数据比例下对该方法测试，验证所提方法的准确性和快速性。最后，通过现场数据验证了所提方法具有较高的泛化性。
Abstract:To avoid the occurrence of large-scale power outage, the realtime and accurate power system disturbance classification is a helpful measure. However, unsatisfying data quality of synchronous phasor measurement units seriously affects its application in disturbance classification. For this reason, a power system disturbance classification method based on the combination of adaptively weighted long short-term denoising autoencoder and random forest was proposed. Firstly, the long short-term memory was utilized to construct a kind of enhanced denoising autoencoder to establish mapping relation between bad data and normal data was constructed. Secondly, according to the change trend of verification loss of different measurements, an adaptive weighted multitask denoising network, which could adaptively update the weight of loss function corresponding to each measurement to reduce the reconstruction error, was presented. Finally, the random forest classifier was used to classify the encoding features, and the Bayesian optimization algorithm was used to optimize the hyperparameters. The simulation results on IEEE 39 bus system show that the proposed method possesses better accuracy and real-time performance for bad data level from different phase measurement units, and it is verified by field data that the proposed method has higher generalization.
标题：基于增强型去噪自编码器与随机森林的电力系统扰动分类方法
英文标题：A Power System Disturbance Classification Method Based on Enhanced Denoising Autoencoder and Random Forest
作者：李子康, 刘灏, 毕天姝, 杨奇逊,
关键词：同步相量测量单元, 电力系统扰动分类, 长短期记忆网络, 去噪自编码器, 自适应权重, 随机森林, 贝叶斯优化,
发表日期：2022-04-10
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