Risk Prediction Method of Low Frequency Oscillation in Maintenance Power Network Based on Long Short Term Memory Neural Network

doi:10.12096/j.2096-4528.pgt.22152

Abstract

Abstract:

With the expansion of power grid scale and the increase of power components, the maintenance methods of power system become more and more complex. It is difficult to evaluate the low-frequency oscillation risk of power grid under massive maintenance only by traditional methods. To solve this problem, a risk prediction method of low-frequency oscillation in maintenance power network based on long short term memory (LSTM) neural network was proposed. Firstly, the unified coding method of power system maintenance mode was proposed, so that the computer can quickly and accurately identify the operation state of power grid under various maintenance modes. Then, based on the historical data measured in real time by phasor measurement unit (PMU), the number of low-frequency oscillation of power grid under different maintenance modes was predicted by using LSTM neural network, so as to evaluate the risk of low-frequency oscillation of power grid under maintenance. Finally, a regional power grid in central China was taken as an example to verify the accuracy and rapidity of the proposed method.

Key words: power system, maintenance method, computer coding, low frequency oscillation, risk prediction, long short term memory (LSTM)

CLC Number:

TK 01

Hongjun FU, Shaoxuan ZHU, Buhua WANG, Yan XIE, Haoqing XIONG, Xiaojun TANG, Xiaoyong DU, Chenghao LI, Xiaomeng LI. Risk Prediction Method of Low Frequency Oscillation in Maintenance Power Network Based on Long Short Term Memory Neural Network[J]. Power Generation Technology, 2024, 45(2): 353-362.

Figures/Tables 8

Fig. 1 Power grid network topology diagram

Fig. 2 Internal unit structure diagram of LSTM

Fig. 3 Overall flow chart

Fig. 4 Training error curve during LSTM training

Fig. 5 Comparison curve of prediction results

Tab. 1 Result statistics of different prediction methods

LSTM

CNN

86%

78%

0.14

0.26

Fig. 6 Curve of prediction accuracy with proportional coefficient

Fig. 7 Curve of prediction accuracy varying with different scale coefficients

References 36

1	范栋琛，张宸宇，姜云龙，等．基于PET控制特性的交直流配电网直流侧低频振荡分析[J]．电力工程技术，2023，42(3)：36-43． doi:10.12158/j.2096-3203.2023.03.005
	FAN D C， ZHANG C Y， JIANG Y L，et al ．Analysis for low frequency oscillation on the DC side of AC/DC distribution network based on PET control characteristics[J]．Electric Power Engineering Technology，2023，42(3)：36-43． doi:10.12158/j.2096-3203.2023.03.005
2	张程，刘佳静，蔡思静，等．基于MEEMD与MP的电网低频振荡模态识别[J]．智慧电力，2022，50(4)：89-95． doi:10.3969/j.issn.1673-7598.2022.04.014
	ZHANG C， LIU J J， CAI S J，et al ．Low frequency oscillation modal identification of power grid based on MEEMD and MP[J]．Smart Power，2022，50(4)：89-95． doi:10.3969/j.issn.1673-7598.2022.04.014
3	DEMELLO F ．Concepts of synchronous machine stability as affected by excitation control[J]．IEEE Transactions on Power Apparatus and Systems，1969，88(4)：316-329． doi:10.1109/tpas.1969.292452
4	李生虎，叶剑桥，张浩，等．基于DFIG功率振荡阻尼器的电力系统低频振荡抑制综述[J]．电力建设，2022，43(9)：25-33．
	LI S H， YE J Q， ZHANG H，et al ．Review on low-frequency oscillation damping in power systems with DFIG-POD[J]．Electric Power Construction，2022，43(9)：25-33．
5	汪康康，孙昕炜，周波，等．基于压缩空气储能附加阻尼控制的电力系统低频振荡抑制策略[J]．中国电力，2023，56(10)：115-123．
	WANG K K， SUN X W， ZHOU B，et al ．Low-frequency oscillation suppression strategy for power system based on supplementary damping control of compressed air energy storage[J]．Electric Power，2023，56(10)：115-123．
6	汤涌．电力系统强迫功率振荡的基础理论[J]．电网技术，2006，30(10)：29-33． doi:10.3321/j.issn:1000-3673.2006.10.006
	TANG Y ．Fundamental theory of forced power oscillation in power system[J]．Power System Technology，2006，30(10)：29-33． doi:10.3321/j.issn:1000-3673.2006.10.006
7	王铁强，贺仁睦，王卫国，等．电力系统低频振荡机理的研究[J]．中国电机工程学报，2002，22(2)：22-26． doi:10.3321/j.issn:0258-8013.2002.02.005
	WANG T Q， HE R M， WANG W G，et al ． The mechanism study of low frequency oscillation in power system[J]．Proceedings of the CSEE，2002，22(2)：22-26． doi:10.3321/j.issn:0258-8013.2002.02.005
8	辛建波，舒展，冯双，等．原动机及调速系统对强迫功率振荡的影响[J]．南京工业大学学报(自然科学版)，2018，40(6)：79-83．
	XIN J B， SHU Z， FENG S，et al ．Effects of power turbine and its governor on forced oscillations[J]．Journal of Nanjing Tech University (Natural Science Edition)，2018，40(6)：79-83．
9	黄荣泽．百色—兴义区域互联电网低频振荡模式分析及抑制措施[J]．广西电力，2019，42(5)：52-56． doi:10.3969/j.issn.1671-8380.2019.05.012
	HUANG R Z ．Analysis of the low frequency oscillation mode in Baise-Xingyi regional interconnected power grid and its suppression measures[J]．Guangxi Electric Power，2019，42(5)：52-56． doi:10.3969/j.issn.1671-8380.2019.05.012
10	张晋宝．基于特征值法的黑龙江电网小干扰稳定性分析[J]．黑龙江电力，2019，41(6)：488-494．
	ZHANG J B ．Small disturbance stability analysis in Heilongjiang power grid based on eigenvalue method[J]．Heilongjiang Electric Power，2019，41(6)：488-494．
11	黄瑞，肖宇，刘谋海，等．复频谱插值DFT的电力系统低频振荡信号测量方法[J]．湖南大学学报(自然科学版)，2021，48(10)：170-177．
	HUANG R， XIAO Y， LIU M H，et al ．Estimation method of power system oscillation signal under power swing with using complex spectral interpolation DFT[J]．Journal of Hunan University (Natural Sciences)，2021，48(10)：170-177．
12	姜涛，刘方正，陈厚合，等．基于多通道快速傅里叶小波变换的电力系统主导振荡模式及模态协同辨识方法研究[J]．电力自动化设备，2019，39(7)：125-132． doi:10.16081/j.issn.1006-6047.2019.07.019
	JIANG T， LIU F Z， CHEN H H，et al ． Cooperated identification method of dominant oscillation modes and mode shapes for power system based on multi-channel fast Fourier transform based continuous wavelet transform[J]．Electric Power Automation Equipment，2019，39(7)：125-132． doi:10.16081/j.issn.1006-6047.2019.07.019
13	赵峰，吴梦娣．EEMD-Robust ICA和Prony算法在电力系统低频振荡模态辨识中的应用[J]．太阳能学报，2019，40(10)：2919-2929．
	ZHAO F， WU M D ．Application of EEMD-Robust ICA and prony algorithm in modes identification of power system low frequency oscillation[J]．Journal of Solar Energy，2019，40(10)：2919-2929．
14	张俊峰，陈珉，杨婷，等．低频振荡参数Prony辨识中的数字滤波器设计[J]．电力系统及其自动化学报，2018，30(12)：99-104． doi:10.3969/j.issn.1003-8930.2018.12.015
	ZHANG J F， CHEN M， YANG T，et al ． Design of digital filter for recognition of low-frequency oscillation parameters using prony algorithm[J]．Proceedings of the CSU-EPSA，2018，30(12)：99-104． doi:10.3969/j.issn.1003-8930.2018.12.015
15	汪颂军，刘涤尘，廖清芬，等．基于EEMD-NExT的低频振荡主导模式工况在线辨识与预警[J]．电力自动化设备，2014，34(12)：111-116． doi:10.3969/j.issn.1006-6047.2014.12.019
	WANG S J， LIU D C， LIAO Q F，et al ．Online dominant mode identification and warning based on EEMD-NExT for low-frequency oscillation in operating conditions[J]．Electric Power Automation Equipment，2014，34(12)：111-116． doi:10.3969/j.issn.1006-6047.2014.12.019
16	杨德友，王文嘉，高际惟，等．随机数据驱动下的机电振荡参数在线提取与阻尼调制(一)：基于ORSSI的模态参数在线辨识[J]．中国电机工程学报，2018，38(8)：2253-2261．
	YANG D Y， WANG W J， GAO J W，et al ．On-line electromechanical oscillation analysis and damping modulation for power system using ambient data (part I)：modal parameters identification based on ORSSI[J]．Proceedings of the CSEE，2018，38(8)：2253-2261．
17	张敏，沈健，侯明国，等．相量测量单元实现次同步振荡在线辨识和告警的探讨[J]．电力系统自动化，2016，40(16)：143-146． doi:10.7500/AEPS20151013003
	ZHANG M， SHEN J， HOU M G，et al ．Discussion on on-line identification and warning os subsynchronous oscillation for phasor measuring unit[J]．Automation of Electric Power Systems，2016，40(16)：143-146． doi:10.7500/AEPS20151013003
18	孙志军，薛磊，许阳明，等．深度学习研究综述[J]．计算机应用研究，2012，29(8)：2806-2810． doi:10.3969/j.issn.1001-3695.2012.08.002
	SUN Z J， XUE L， XU Y M，et al ．Overview of deep learning[J]．Application Research of Computers，2012，29(8)：2806-2810． doi:10.3969/j.issn.1001-3695.2012.08.002
19	周飞燕，金林鹏，董军．卷积神经网络研究综述[J]．计算机学报，2017，40(6)：1229-1251． doi:10.11897/SP.J.1016.2017.01229
	ZHOU F Y， JIN L P， DONG J，et al ．Review of convolutional neural network[J]．Chinese Journal of Computers，2017，40(6)：1229-1251． doi:10.11897/SP.J.1016.2017.01229
20	冯双，崔昊，陈佳宁，等．人工智能在电力系统宽频振荡中的应用与挑战[J]．中国电机工程学报，2021，41(23)：7889-7905．
	FENG S， CUI H， CHEN J N，et al ．Applications and challenges of artificial intelligence in power system wide-band oscillations[J]．Proceedings of the CSEE，2021，41(23)：7889-7905．
21	竺炜，唐颖杰，周有庆，等．基于改进 Prony 算法的电力系统低频振荡模式识别[J]．电网技术，2009，33(5)：44-47．
	ZHU W， TANG Y J， ZHOU Y Q，et al ． Identification of power system low frequency oscillation mode based on improved prony algorithm[J]．Power System Technology，2009，33(5)：44-47．
22	竺炜，马建伟，曾喆昭，等．分段傅里叶神经网络的低频振荡模式识别方法[J]．电力系统保护与控制，2012，40(15)：40-45． doi:10.3969/j.issn.1674-3415.2012.15.009
	ZHU W， MA J W， ZENG Z Z，et al ．Low frequency oscillation mode recognition based on segmental Fourier neural network algorithm[J]．Power System Protection and Control，2012，40(15)：40-45． doi:10.3969/j.issn.1674-3415.2012.15.009
23	赵妍，崔浩瀚，荣子超．次同步振荡在线监测的同步提取变换和朴素贝叶斯方法[J]．电力系统自动化，2019，43(3)：187-192．
	ZHAO Y， CUI H H， RONG Z C ．On-line monitoring of subsynchronous oscillation based on synchroextracting transform and naive bayes method[J]．Automation of Electric Power Systems，2019，43(3)：187-192．
24	赵妍，霍红，徐晗桐．二阶段随机森林分类方法在低频振荡监测中的应用[J]．东北电力大学学报，2020，40(2)：60-67．
	ZHAO Y， HUO H， XU H T ．Application of two-stage random forest classification method to low-frequency oscillation monitoring[J]．Journal of Northeast Electric Power University，2020，40(2)：60-67．
25	蒋长江，刘俊勇，刘友波，等．基于广域测量系统和CELL 理论的强迫振荡在线感知与定位[J]．电力自动化设备，2015，35(2)：125-132．
	JIANG C J， LIU J Y， LIU Y B，et al ． Online forced oscillation detection and identification based on wide area measurement system and CELL theory[J]．Electric Power Automation Equipment，2015，35(2)：125-132．
26	BANNA H U， SOLANKI S K， SOLANKI J ．Data-driven disturbance source identification for power system oscillations using credibility search ensemble learning[J]．IET Smart Grid，2019，2(2)：293-300． doi:10.1049/iet-stg.2018.0092
27	李相俊，许格健．基于长短期记忆神经网络的风力发电功率预测方法[J]．发电技术，2019，40(5)：426-433． doi:10.12096/j.2096-4528.pgt.19108
	LI X J， XU G J ．Wind power prediction method based on long short-term memory neural network[J]．Power Generation Technology，2019，40(5)：426-433． doi:10.12096/j.2096-4528.pgt.19108
28	刘君，邓毅，杨延西，等．基于深度学习的空预器转子红外补光图像积灰状态识别[J]．发电技术，2022，43(3)：510-517． doi:10.12096/j.2096-4528.pgt.20122
	LIU J， DENG Y， YANG Y X，et al ．Ash accumulation state identification for infrared compensation images of air preheater rotor based on deep learning method[J]．Power Generation Technology，2022，43(3)：510-517． doi:10.12096/j.2096-4528.pgt.20122
29	高骞，杨俊义，刘凯，等．基于大数据深度强化学习的交流配电网稳定性控制研究[J]．电网与清洁能源，2023，39(6)：26-32． doi:10.3969/j.issn.1674-3814.2023.06.004
	GAO Q， YANG J Y， LIU K，et al ．Research on the stability control of AC distribution networks based on deep reinforcement learning of big data[J]．Power System and Clean Energy，2023，39(6)：26-32． doi:10.3969/j.issn.1674-3814.2023.06.004
30	ZHU L， LUO Y ．Deep feedback learning based predictive control for power system undervoltage load shedding[J]．IEEE Transactions on Power Systems，2021，99：1-12． doi:10.1109/tpwrs.2020.3048681
31	MANNINEN H， RAMLAL C J， SINGH A，et al ．Toward automatic condition assessment of high-voltage transmission infrastructure using deep learning techniques[J]．International Journal of Electrical Power & Energy Systems，2021，128(4)：106726． doi:10.1016/j.ijepes.2020.106726
32	武霁阳，李强，陈潜，等．知识图谱框架下基于深度学习的HVDC系统故障辨识[J]．电力系统保护与控制，2023，51(20)：160-169．
	WU J Y， LI Q， CHEN Q，et al ．Fault identification of an HVDC system based on deep learning in the framework of a knowledge graph[J]．Power System Protection and Control，2023，51(20)：160-169．
33	程超，葛维，郭兰柯，等．基于深度学习的新型电力智能交互平台多任务集成模型研究[J]．电测与仪表，2023，60(6)：81-85．
	CHENG C， GE W， GUO L K，et al ．Research on multi-task ensemble model based on deep learning for novel power intelligent interaction platform[J]．Electrical Measurement & Instrumentation，2023，60(6)：81-85．
34	郭梦轩，管霖，苏寅生，等．基于改进边图卷积网络的电力系统小干扰稳定评估模型[J]．电网技术，2022，46(6)：2095-2103．
	GUO M X， GUAN L， SU Y S，et al ． Small-signal stability assessment model based on improved edge graph convolutional networks of power system[J]．Power System Technology，2022，46(6)：2095-2103．
35	秦心筱，张昌华，徐子豪，等．基于卷积神经网络的电力系统低频振荡主导模态特征定性辨识[J]．电力系统保护与控制，2021，49(10)：51-58．
	QIN X X， ZHANG C H， XU Z H，et al ． Research on qualitative identification of a low frequency oscillations dominant mode in power system based on a convolutional neural network[J]．Power System Protection and Control，2021，49(10)：51-58．
36	冯双，崔昊，陈佳宁，等．基于自编码器信号压缩与LSTM的宽频振荡扰动源定位方法[J]．电力系统自动化，2022，46(12)：194-201．
	FENG S， CUI H， CHEN J N，et al ．Location method of wide-band oscillation disturbance sources based on signal compression of autoencoder and LSTM[J]．Automation of Electric Power Systems，2022，46(12)：194-201．

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