引用本文: | 尹项根,乔 健,贺儒飞,等.基于FFT-LSTM的变速抽蓄机组转子绕组短路故障和偏心故障诊断方法[J].电力系统保护与控制,2023,51(6):73-81.[点击复制] |
YIN Xianggen,QIAO Jian,HE Rufei,et al.FFT-LSTM-based fault diagnosis method for a rotor winding short circuit fault and rotoreccentricity fault of a variable-speed pumped storage unit[J].Power System Protection and Control,2023,51(6):73-81[点击复制] |
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摘要: |
变速抽水蓄能机组是适应系统功率波动的重要调节手段。转子绕组短路故障和转子偏心故障是其常见的故障类型,两种故障均会在定子侧感应生成特征频带相近的谐波环流,导致两种故障难以被区分。提出了一种基于快速傅里叶变换-长短期记忆(fast Fourier transform-long short-term memory, FFT-LSTM)网络的故障诊断方法,以细化分辨故障特征相近的转子绕组短路故障和转子偏心故障。所提方法以定子分支环流的谐波分量为特征量进行故障诊断,分别推导了两种故障发生时定子侧环流谐波特征,并总结二者间的相似性和差异性。鉴于该差异较为微弱,引入长短期记忆(long short-term memory,LSTM)神经网络算法对其进行辨识。利用内部故障仿真模型对可能发生的转子绕组短路故障和偏心故障进行批量仿真,以得到用于LSTM网络训练和测试的数据集。仿真结果表明FFT-LSTM能够准确诊断不同转速下变速抽蓄机组的转子绕组短路故障和转子偏心故障。 |
关键词: 变速抽蓄机组 转子绕组短路故障 转子偏心故障 LSTM神经网络 |
DOI:10.19783/j.cnki.pspc.236139 |
投稿时间:2022-06-28修订日期:2023-01-31 |
基金项目:国家自然科学基金项目资助(5187708);中国南方电网有限责任公司重点科技项目资助(STKJXM20210102) |
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FFT-LSTM-based fault diagnosis method for a rotor winding short circuit fault and rotoreccentricity fault of a variable-speed pumped storage unit |
YIN Xianggen1,QIAO Jian1,HE Rufei2,ZHANG Hao2,PENG Yumin2,WANG Wenhui2 |
(1. State Key Laboratory of Advanced Electromagnetic Engineering and Technology (Huazhong University of Science and
Technology), Wuhan 430074, China; 2. CSG PGC Power Storage Research Institute, Guangzhou 510950, China) |
Abstract: |
The variable speed pumped storage unit is an important means for adjusting the power fluctuation of a system. Rotor winding short-circuit and rotor eccentricity faults are common fault types. Both faults will generate harmonic circulations with similar characteristic frequency bands on the stator side. These lead to a difficulty in distinguishing between the two faults. In this paper, a fault diagnosis method based on fast Fourier transform-long short-term memory (FFT-LSTM) is proposed to distinguish between the rotor winding short circuit and rotor eccentric faults with similar fault characteristics. The proposed method takes the harmonic component of stator branch circulations as the characteristic component for fault diagnosis, deduces the harmonic characteristics of stator side circulations when two kinds of faults occur, and summarizes the similarities and differences between them. In view of the weak difference, an LSTM neural network algorithm is introduced to identify it. The possible short-circuit and eccentricity faults of rotor winding are simulated in batches by using the internal fault simulation model to obtain the data set for LSTM network training and testing. Simulation results show that the FFT-LSTM can accurately diagnose rotor winding short-circuit and rotor eccentricity faults of variable speed pumping and storage units. |
Key words: variable speed pumping storage unit rotor winding short circuit fault rotor eccentric fault LSTM neural network |