Open-Circuit Fault Diagnosis Method of Energy Storage Converter Based on MFCC Feature Set

doi:10.11930/j.issn.1004-9649.202205007

Abstract

Abstract: Power conversion system (PCS) fault diagnosis plays an important role in the intelligent operation and maintenance of battery energy storage power stations. Considering the difficulties in signal feature extraction, data dimension explosion, and instability of threshold determination interval in the non-invasive identification of IGBT open-circuit fault in PCS by the existing methods, this paper proposes an open-circuit fault diagnosis method of energy storage converter based on Mel-scale frequency cepstral coefficients (MFCC) feature set, so as to support the normal operation and maintenance of PCS. Firstly, the three-phase current on the alternating current (AC) side is taken as the input signal, and an MFCC fault feature data set is constructed by analyzing the signal spectrum energy distribution and envelope characteristics in different frequency intervals. Then, through kernel principal component analysis (KPCA), the dimension reduction screening of nonlinear fault features under charge and discharge conditions is realized. Secondly, a fault state diagnosis model based on the Bayesian optimization algorithm (BOA) and one-dimensional convolutional neural network (CNN-1D) is constructed with a low-dimensional fault feature set as an input. Finally, through the fault simulation experiment of a grid-connected energy storage converter, the proposed method is compared with existing methods, and the results show that the proposed method has better robustness and accuracy in complex noise environments.

Key words: battery energy storage, converter, fault diagnosis, Mel-scale frequency cepstral coefficients, diagnosis model

YU Bin, SONG Xingrong, ZHOU Ting, LUO Linbo, LI Hui, CHE Liang. Open-Circuit Fault Diagnosis Method of Energy Storage Converter Based on MFCC Feature Set[J]. Electric Power, 2022, 55(12): 34-42.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202205007

https://www.electricpower.com.cn/EN/Y2022/V55/I12/34

References

[1] 李彦林, 王明彦, 郑载满. 微网运行条件下储能并网VSC多目标控制策略[J]. 电网技术, 2013, 37(7): 1792–1798
LI Yanlin, WANG Mingyan, ZHENG Zaiman. A multi-objective control strategy for grid-connecting VSC under grid-connected operation of microgrid[J]. Power System Technology, 2013, 37(7): 1792–1798
[2] 元博, 张运洲, 鲁刚, 等. 电力系统中储能发展前景及应用关键问题研究[J]. 中国电力, 2019, 52(3): 1–8
YUAN Bo, ZHANG Yunzhou, LU Gang, et al. Research on key issues of energy storage development and application in power systems[J]. Electric Power, 2019, 52(3): 1–8
[3] 宋平岗, 吴继珍, 邹欢. 电网电压不平衡时模块化多电平换流器直接功率补偿控制策略[J]. 高电压技术, 2016, 42(7): 2184–2192
SONG Pinggang, WU Jizhen, ZOU Huan. Direct power compensation control strategy for modular multilevel converter under unbalanced grid voltage conditions[J]. High Voltage Engineering, 2016, 42(7): 2184–2192
[4] 欧名勇, 王逸超, 刘文军, 等. 电网电压不平衡下储能变流器的控制策略[J]. 中国电力, 2022, 55(7): 93–101
OU Mingyong, WANG Yichao, LIU Wenjun, et al. Control strategy of power conversion system under unbalanced grid voltage[J]. Electric Power, 2022, 55(7): 93–101
[5] 余斌, 朱维钧, 徐浩, 等. 电池储能电站电池管理系统关键技术[J]. 湖南电力, 2020, 40(5): 55–59
YU Bin, ZHU Weijun, XU Hao, et al. Key technology of battery management system in battery storage power station[J]. Hunan Electric Power, 2020, 40(5): 55–59
[6] 张筱辰, 朱金大, 杨冬梅, 等. 基于t-SNE流形学习与快速聚类算法的光伏逆变器故障预测技术[J]. 中国电力, 2020, 53(6): 41–47
ZHANG Xiaochen, ZHU Jinda, YANG Dongmei, et al. Photovoltaic inverter fault prediction technology based on t-SNE manifold learning and fast clustering algorithm[J]. Electric Power, 2020, 53(6): 41–47
[7] 马铭遥, 凌峰, 孙雅蓉, 等. 三相电压型逆变器智能化故障诊断方法综述[J]. 中国电机工程学报, 2020, 40(23): 7683–7699
MA Mingyao, LING Feng, SUN Yarong, et al. Review of intelligent fault diagnosis methods for three-phase voltage-mode inverters[J]. Proceedings of the CSEE, 2020, 40(23): 7683–7699
[8] ZHANG J H, ZHAO J, ZHOU D H, et al. High-performance fault diagnosis in PWM voltage-source inverters for vector-controlled induction motor drives[J]. IEEE Transactions on Power Electronics, 2014, 29(11): 6087–6099.
[9] LI M S, YU D, CHEN Z M, et al. A data-driven residual-based method for fault diagnosis and isolation in wind turbines[J]. IEEE Transactions on Sustainable Energy, 2019, 10(2): 895–904.
[10] CAI B P, ZHAO Y B, LIU H L, et al. A data-driven fault diagnosis methodology in three-phase inverters for PMSM drive systems[J]. IEEE Transactions on Power Electronics, 2017, 32(7): 5590–5600.
[11] NTALAMPIRAS S. Fault diagnosis for smart grids in pragmatic conditions[J]. IEEE Transactions on Smart Grid, 2018, 9(3): 1964–1971.
[12] 杨彦杰, 董哲, 姚芳, 等. 基于1 D-CNN-LSTM混合神经网络模型的双桥并联励磁功率单元故障诊断[J]. 电网技术, 2021, 45(5): 2025–2032
YANG Yanjie, DONG Zhe, YAO Fang, et al. Fault diagnosis of double bridge parallel excitation power unit based on 1 D-CNN-LSTM hybrid neural network model[J]. Power System Technology, 2021, 45(5): 2025–2032
[13] WANG T Z, QI J, XU H, et al. Fault diagnosis method based on FFT-RPCA-SVM for Cascaded-Multilevel Inverter[J]. ISA Transactions, 2016, 60: 156–163.
[14] 姜媛媛, 王友仁, 吴祎, 等. 基于小波包能量谱和ELM的光伏逆变器多故障在线诊断[J]. 仪器仪表学报, 2015, 36(9): 2145–2152
JIANG Yuanyuan, WANG Youren, WU Yi, et al. Online multiple fault diagnosis for PV inverter based on wavelet packet energy spectrum and extreme learning machine[J]. Chinese Journal of Scientific Instrument, 2015, 36(9): 2145–2152
[15] 陈勇, 刘志龙, 陈章勇. 基于电流矢量特征分析的逆变器开路故障快速诊断与定位方法[J]. 电工技术学报, 2018, 33(4): 883–891
CHEN Yong, LIU Zhilong, CHEN Zhangyong. Fast diagnosis and location method for open-circuit fault in inverter based on current vector character analysis[J]. Transactions of China Electrotechnical Society, 2018, 33(4): 883–891
[16] GOMATHY V, SELVAPERUMAL S. Fault detection and classification with optimization techniques for a three-phase single-inverter circuit[J]. Journal of Power Electronics, 2016, 16: 1097–1109.
[17] 张建忠, 耿治, 徐帅. 基于T型逆变器的APF故障诊断与容错控制[J]. 中国电机工程学报, 2019, 39(1): 245–255, 339
ZHANG Jianzhong, GENG Zhi, XU Shuai. Fault diagnosis and fault tolerant control for APF based on T-type inverter[J]. Proceedings of the CSEE, 2019, 39(1): 245–255, 339
[18] 严亚兵, 朱维钧, 欧阳帆, 等. 基于频域分析的储能变流器控制相互作用研究[J]. 湖南电力, 2019, 39(5): 13–17
YAN Yabing, ZHU Weijun, OUYANG Fan, et al. Research on control interaction of energy storage converter based on frequency domain analysis[J]. Hunan Electric Power, 2019, 39(5): 13–17
[19] MA J C, SHANG J N, ZHAO X, et al. Bayes-DCGRU with Bayesian optimization for rolling bearing fault diagnosis[J]. Applied Intelligence, 2022, 52(10): 11172–11183.
[20] 曹浩, 黄韬, 周舟, 等. 基于主成分分析法的变压器直流偏磁声学异常检测[J]. 湖南电力, 2021, 41(5): 1–6
CAO Hao, HUANG Tao, ZHOU Zhou, et al. Detection of DC bias magnetic acoustic abnormality of transformer based on principal component analysis[J]. Hunan Electric Power, 2021, 41(5): 1–6