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江苏省智能电网技术与装备重点实验室(东南大学电气工程学院),江苏省南京市 210096
Received:06 June 2021,
Revised:2022-01-22,
Published Online:25 April 2022,
Published:25 June 2022
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崔昊,冯双,陈佳宁等.基于自编码器与长短期记忆网络的宽频振荡广域定位方法[J].电力系统自动化,2022,46(12):194-201.
CUI Hao,FENG Shuang,CHEN Jianing,et al.Wide-area Location Method of Wide-band Oscillations Based on Autoencoder and Long Short-term Memory Network[J].Automation of Electric Power Systems,2022,46(12):194-201.
崔昊,冯双,陈佳宁等.基于自编码器与长短期记忆网络的宽频振荡广域定位方法[J].电力系统自动化,2022,46(12):194-201. DOI: 10.7500/AEPS20210606003.
CUI Hao,FENG Shuang,CHEN Jianing,et al.Wide-area Location Method of Wide-band Oscillations Based on Autoencoder and Long Short-term Memory Network[J].Automation of Electric Power Systems,2022,46(12):194-201. DOI: 10.7500/AEPS20210606003.
高比例新能源与高比例电力电子设备引发的宽频振荡问题日益凸显,而现有基于同步相量数据的振荡监测方法受到现有通信带宽的限制,难以对频率在数赫兹至数百赫兹范围内的宽频振荡进行全局化监测。为此,提出一种基于自编码器信号压缩与长短期记忆(LSTM)网络的宽频振荡广域定位方法。该方法利用自编码器的数据压缩与解码还原能力实现宽频振荡信号的广域监测分析。首先,在子站对电力系统量测信号进行编码压缩,在现有带宽下实现宽频振荡信号的传输,并有效降低振荡数据的冗余度。然后,在主站侧,可直接基于压缩数据生成特征矩阵,利用LSTM网络定位振荡源。此外,主站还能解码子站上传的压缩数据,并根据需求利用压缩数据或解码还原数据,从而进行宽频振荡的分析与控制。最后,全面考虑次同步、超同步以及中高频段的宽频振荡,并计及负荷变动和随机噪声进行仿真,所得结果表明该方法具有较高的还原与定位精度以及较好的抗噪性能。
The problem of wide-band oscillations caused by the high proportion of renewable energy and power electronic equipment is becoming increasingly prominent. However
the existing oscillation monitoring methods based on synchrophasor data are limited by the current communication bandwidth
and it is difficult to monitor the wide-band oscillations from several hertz to hundreds of hertz globally. Therefore
a wide-area location method of wide-band oscillations based on the signal compression of the autoencoder and long short-term memory (LSTM) network is proposed
which uses the data compression and decoding capability of the autoencoder to realize the wide-area monitoring analysis of wide-band oscillation signals. Firstly
the power system measurement signals are encoded and compressed at sub-stations to realize the transmission of wide-band oscillation signals under the existing bandwidth
and effectively reduce the redundancy of oscillation data. Secondly
a feature matrix can be directly generated based on the compressed data
and the LSTM network can be adopted to locate the source of oscillations at the master station. In addition
the master station can decode the compressed data uploaded by the sub-stations
so the compressed data or decoded data can be used for the analysis and control of wide-band oscillations according to the requirements. Finally
the subsynchronous
supersynchronous
as well as medium and high-frequency oscillations are fully considered
and the load changes and random noise are taken into account for simulation. The results show that this method has a high reproduction
location accuracy
and good anti-noise performance.
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