基于DRSN与电压幅值分析的航空HVDC系统逆变器故障诊断

doi:10.7527/S1000-6893.2023.28685

Abstract

Abstract:

The fault diagnosis of the airborne 270 V high-voltage direct current （HVDC） system has always been a difficult problem in the field of avionics. Therefore， a fault module identification algorithm based on deep residual contraction network （DRSN） and a fault component localization algorithm based on line voltage amplitude analysis have been proposed. Firstly， the total current of the system is collected， and the difference is standardized to obtain characteristic data. Based on the feature data， the Flatten layer is used to improve the original DRSN structure， so as to improve the algorithm recognition accuracy for fault modules. After determining the fault of the system inverter module， the fault phase is determined using the ratio of the two phase line-voltages， and then the fault device is determined using the average line-voltage model. Compared to existing methods， the proposed method only uses one current sensor and two voltage sensors to achieve system fault diagnosis， meeting the weight limitation requirements of aircraft. The experiment proves that the proposed method has good practicality in identifying fault modules and locating fault components with an accuracy of over 97%.

Key words: 270 V HVDC system, deep residual shrinkage network, line voltage amplitude analysis, fault diagnosis, fault module identification

CLC Number:

V242.2

Zhanjun HUANG, Xin DONG, Muyu LU, Ruitao ZHANG, Zhaoyang YAN, An ZHANG. Fault diagnosis of inverter of aviation HVDC sysytem based on DRSN and voltage amplitude analysis[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(3): 328685-328685.

Figures/Tables 14

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Table 1

Fault characteristics of inverter

序号	相	IGBT	r_u	$U ¯$
1	A	T1	［-3.59，-1.3］	U_ab <0
2	A	T2	［-3.59，-1.3］	U_ab >0
3	B	T3	［0.68，1.48］	U_ab >0
4	B	T4	［0.68，1.48］	U_ab <0
5	C	T5	［-0.77，-0.28］	U_bc >0
6	C	T6	［-0.77，-0.28］	U_bc <0

Table 1

Fig.7

Fig.8

Fig.9

Fig.10

Fig.11

Fig.12

Fig.13

References 22

1	苟斌. 电力牵引变流器故障诊断与容错控制技术研究［D］. 西南交通大学， 2016.
	GOU B. Research on fault diagnosis and fault-tolerant control technology of electric traction converter［D］. Southwest Jiaotong University， 2016 （in Chinese）.
2	陈卫华. 飞机270V高压直流供电系统结构及仿真技术研究［D］. 南京：南京航空航天大学， 2010.
	CHEN W H. Research on the structure and simulation technique for aircraft 270V HVDC power supply system［D］. Nanjing： Nanjing University of Aeronautics and Astronautics， 2010 （in Chinese）.
3	王立坤. 飞机270V发电系统的鲁棒控制技术研究［D］. 南京：南京航空航天大学， 2021.
	WANG L K. Research on robust control technology of aircraft 270 V power system［D］. Nanjing： Nanjing University of Aeronautics and Astronautics， 2021 （in Chinese）.
4	ZHANG C W， ZHANG T L， CHEN N， et al. Reliability modeling and analysis for a novel design of modular converter system of wind turbines［J］. Reliability Engineering & System Safety， 2013， 111： 86-94.
5	LI Z， MA H， BAI Z H， et al. Fast transistor open-circuit faults diagnosis in grid-tied three-phase VSIs based on average bridge arm pole-to-pole voltages and error-adaptive thresholds［J］. IEEE Transactions on Power Electronics， 2018， 33（9）： 8040-8051.
6	HUANG Z J， WANG Z S， SONG C H. Complementary virtual mirror fault diagnosis method for microgrid inverter［J］. IEEE Transactions on Industrial Informatics， 2021， 17（11）： 7279-7290.
7	HUANG Z J， WANG Z S， ZHANG H G. A diagnosis algorithm for multiple open-circuited faults of microgrid inverters based on main fault component analysis［J］. IEEE Transactions on Energy Conversion， 2018， 33（3）： 925-937.
8	GANGSAR P， TIWARI R. Signal based condition monitoring techniques for fault detection and diagnosis of induction motors： A state-of-the-art review［J］. Mechanical Systems and Signal Processing， 2020， 144： 106908.
9	XU Z Y， SALEH J H. Machine learning for reliability engineering and safety applications： Review of current status and future opportunities［J］. Reliability Engineering & System Safety， 2021， 211： 107530.
10	ZHANG J J， CHEN Y， CHEN Z Y， et al. Open-switch fault diagnosis method in voltage-source inverters based on phase currents［J］. IEEE Access， 2019， 7： 63619-63625.
11	徐小健，于飞. 基于输出电压轨迹的三相逆变器开关管开路故障诊断［J/OL］.中国电机工程学报：1-11［2023-03-04］.
	XU X J， YU F. Open-circuit fault diagnosis of three-phase inverter switch based on output voltage trace［J/OL］. Chinese Journal of Electrical Engineering 1-11 ［2023-03-04］（in Chinese）.
12	LI Z， WHEELER P， WATSON A， et al. A fast diagnosis method for both IGBT faults and current sensor faults in grid-tied three-phase inverters with two current sensors［J］. IEEE Transactions on Power Electronics， 2020， 35（5）： 5267-5278.
13	LIANG Y Q， WANG R K， HU B T. Single-switch open-circuit diagnosis method based on average voltage vector for three-level T-type inverter［J］. IEEE Transactions on Power Electronics， 2021， 36（1）： 911-921.
14	LI Z， WANG B R， REN Y N， et al. L- and LCL-filtered grid-tied single-phase inverter transistor open-circuit fault diagnosis based on post-fault reconfiguration algorithms［J］. IEEE Transactions on Power Electronics， 2019， 34（10）： 10180-10192.
15	赵晋泉，夏雪，徐春雷，等. 新一代人工智能技术在电力系统调度运行中的应用评述［J］. 电力系统自动化， 2020， 44（24）： 1-10.
	ZHAO J Q， XIA X， XU C L， et al. Review on application of new generation artificial intelligence technology in power system dispatching and operation［J］. Automation of Electric Power Systems， 2020， 44（24）： 1-10 （in Chinese）.
16	杨博，陈义军，姚伟，等. 基于新一代人工智能技术的电力系统稳定评估与决策综述［J］. 电力系统自动化， 2022， 46（22）： 200-223.
	YANG B， CHEN Y J， YAO W， et al. Review on stability assessment and decision for power systems based on new-generation artificial intelligence technology［J］. Automation of Electric Power Systems， 2022， 46（22）： 200-223 （in Chinese）.
17	赵靖英，吴晶晶，张雪辉，等. 基于萤火虫扰动麻雀搜索算法-极限学习机的光伏阵列故障诊断方法研究［J］. 电网技术， 2023， 47（4）： 1612-1625.
	ZHAO J Y， WU J J， ZHANG X H， et al. Fault diagnosis of photovoltaic arrays based on sparrow search algorithm with firefly perturbation-extreme learning machine［J］. Power System Technology， 2023， 47（4）： 1612-1625 （in Chinese）.
18	梁睿，王全金，孔令昌，等. 考虑行波波速衰减及波头时间误差的复杂输电网故障精准定位［J］. 电网技术， 2022， 46（11）： 4512-4524.
	LIANG R， WANG Q J， KONG L C， et al. Precise location of complex power grid faults considering velocity attenuation and time error of traveling waves［J］. Power System Technology， 2022， 46（11）： 4512-4524 （in Chinese）.
19	郭方洪，刘师硕，吴祥，等. 基于联邦学习的含不平衡样本数据电力变压器故障诊断［J］. 电力系统自动化， 2023， 47（10）： 145-152.
	GUO F H， LIU S S， WU X， et al. Federated learning based fault diagnosis of power transformer with unbalanced sample data［J］. Automation of Electric Power Systems， 2023， 47（10）： 145-152 （in Chinese）.
20	张大海，张晓炜，孙浩，等. 基于卷积神经网络的交直流输电系统故障诊断［J］. 电力系统自动化， 2022， 46（5）： 132-145.
	ZHANG D H， ZHANG X W， SUN H， et al. Fault diagnosis for AC/DC transmission system based on convolutional neural network［J］. Automation of Electric Power Systems， 2022， 46（5）： 132-145 （in Chinese）.
21	WU X， CHEN C Y， CHEN T F， et al. A fast and robust diagnostic method for multiple open-circuit faults of voltage-source inverters through line voltage magnitudes analysis［J］. IEEE Transactions on Power Electronics， 2020， 35（5）： 5205-5220.
22	ZHAO M H， ZHONG S S， FU X Y， et al. Deep residual shrinkage networks for fault diagnosis［J］. IEEE Transactions on Industrial Informatics， 2020， 16（7）： 4681-4690.

[1]	Minghui HU, Jinji GAO, Zhinong JIANG, Weimin WANG, Limin ZOU, Tao ZHOU, Yunfeng FAN, Yue WANG, Jiaxin FENG, Chenyang LI. Research progress on vibration monitoring and fault diagnosis for aero-engine [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(4): 630194-630194.
[2]	Tianqing LI, Weimin WANG, Xulong ZHANG, Shuhui WANG, Zhenyu FU. Identification method of rotor blade axial displacement based on blade tip timing [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(2): 228682-228682.
[3]	Jie LI, Wenxin HUANG, Yiming CAI, Siyuan WANG, Yufei GAO, Xuefeng JIANG. Fault diagnosis and fault tolerant control of position sensor based on DFPMM [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(10): 329307-329307.
[4]	Baohui JIA, Fan JIANG, Yuxin WANG, Du WANG. Fault diagnosis method based on civil aircraft maintenance text data [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(5): 326598-326598.
[5]	Jinrui WANG, Shanshan JI, Zongzhen ZHANG, Zhenyun CHU, Baokun HAN, Huaiqian BAO. Parallel sparse filtering for fault diagnosis under bearing acoustic signal [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(4): 426887-426887.
[6]	Zehao CHEN, Hui CHEN, Yushan GAO, Hang ZHANG. Review and prospect of model-based fault diagnosis technology for liquid rocket engines [J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(23): 629016-629016.
[7]	Gongye YU, Weidong CAI, Minghui HU, Wencai LIU, Bo MA. Intelligent migration diagnosis of mechanical faults driven by hybrid fault mechanism and domain adaptation [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(2): 426800-426800.
[8]	Fangli WANG, Kai LIU, Wei PAN, Mingbo TONG. Application and development of green structure maintenance for civil aircraft [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(11): 25851-025851.
[9]	Chenghao GUO, Jinsong YU, Yue SONG, Qi YIN, Jiaxuan LI. Application of digital twin⁃based aircraft landing gear health management technology [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(11): 227629-227629.
[10]	Wanli ZHAO, Yingqing GUO, Kejie XU, Cansen WANG, Haojie YING, Xinxin TAO. Review of key technologies for fault diagnosis and accommodation for multi⁃electric distributed engine control system [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(10): 27519-027519.
[11]	ZHANG Zhongqiang, ZHANG Xin, WANG Jiaxu, LIU Zhiwen. Reweighted kurtogram for aero-engine fault diagnosis [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(9): 625445-625445.
[12]	HAN Songyu, SHAO Haidong, JIANG Hongkai, ZHANG Xiaoyang. Intelligent fault diagnosis of aero-engine high-speed bearings using enhanced CNN [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(9): 625479-625479.
[13]	ZHANG Shuo, LIU Zhiwen. Structured Bayesian sparse representation of aero-engine bearing failures [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(9): 625056-625056.
[14]	SHAO Yiwei, CHEN Jiayu, LIN Cuiying, WAN Cheng, GE Hongjuan, SHI Zhilong. Gearbox fault diagnosis with small training samples: An improved deep forest based method [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(8): 625429-625429.
[15]	SUN Canfei, HUANG Linran, SHEN Yong. Fault diagnosis of helicopter planetary gear transmission system under complex operating conditions [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(8): 625480-625480.

Fault diagnosis of inverter of aviation HVDC sysytem based on DRSN and voltage amplitude analysis

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 14

References 22

Related Articles 15

Recommended Articles

Metrics

Comments