Fault Diagnosis of Rotating Rectifier in Nuclear Multi-Phase Brushless Excitation System Based on Convolutional Neural Network
Liang Zhengqiu1, Hao Liangliang1, Zhou Yanzhen2, Duan Xianwen3, Wang Guang4
1. School of Electrical Engineering Beijing Jiaotong University Beijing 100044 China; 2. Department of Electrical Engineering Tsinghua University Beijing 100084 China; 3. China Nuclear Power Operations Co. Ltd Shenzhen 518172 China; 4. Nanjing NR Electric Co. Ltd Nanjing 211102 China
Abstract：The multi-phase brushless excitation system is widely used in large-capacity nuclear power plants. The safe and stable operation of nuclear power plants depends on accurately diagnosing rotating rectifier faults. However, the existing methods cannot accurately distinguish all types of faults, which makes it challenging to meet the demands in practice. Recently, as a representative artificial intelligence method, deep learning has provided new ideas for solving this problem. Therefore, this paper develops a fault diagnosis scheme for the rotating rectifier based on a convolutional neural network. First, taking an 11-phase brushless excitation system as an example, the excitation current characteristics after the rotating rectifier fault are analyzed. The results show that the excitation current waveforms contain amplitude and phase information of all fault harmonics, which can be varied under different fault modes and used for fault diagnosis of the rotating rectifier. Then, a one-dimensional dilated convolutional neural network (1D-DCNN) model is introduced to realize fault diagnosis. The 1D-DCNN establishes a mapping relationship from the input (excitation current waveforms) to the output (fault mode). Meanwhile, the difference between the largest and second-largest probability of the output is defined as the confidence based on the conditional probability of samples belonging to different fault modes given by the model, which is used to measure the reliability of the diagnosis results. The process of fault diagnosis based on the 1D-DCNN model includes three stages. At stage 1, raw data at several voltage levels are acquired to ensure the model has applicability and generalizability under different voltage levels. Then, the data are preprocessed regarding sliding window segmentation, filtering, and mean normalization. At stage 2, the 1D-DCNN model is trained to diagnose rotating rectifier faults online. At stage 3, the Score-CAM method is introduced to clarify the interpretability of the 1D-DCNN model. Finally, the prototype experiment verifies the effectiveness of the proposed 1D-DCNN model. Diode open-circuit faults in an 11-phase brushless excitation system are mainly studied, which includes eight fault types. The processed samples are divided into training, verification, and test sets. The verification set is utilized to determine the hyperparameters of the model. The variable control method is used to optimize the hyperparameters of each layer sequentially. Next, the Score-CAM method provides interpretability for the 1D-DCNN model and explains the hyperparameter influence on fault diagnosis performance. The diagnostic accuracy and confidence of different models are compared by the original test set and the test sets with noise. The 1D-DCNN model shows better anti-noise ability, which indicates that selecting the appropriate confidence threshold improves the classification accuracy of the model. In conclusion, the feasibility of fault diagnosis of rotating rectifiers based on a convolutional neural network is verified, providing a new data-driven idea for the fault diagnosis of rotating rectifiers.
[1] 张蕴. 双碳目标下我国核电发展趋势分析[J]. 核科学与工程, 2021, 41(6): 1347-1351. Zhang Yun.Analysis on the development trend of nuclear energy in China under the dual carbon target[J]. Nuclear Science and Engineering, 2021, 41(6): 1347-1351. [2] 郝亮亮, 常江, 李洪学, 等. 核电多相角形无刷励磁机定子匝间短路故障特征分析[J]. 中国电机工程学报, 2019, 39(22): 6726-6736. Hao Liangliang, Chang Jiang, Li Hongxue, et al.Characteristics analysis of stator inter-turn short circuits in nuclear power multi-phase angular brush- less exciter[J]. Proceedings of the CSEE, 2019, 39(22): 6726-6736. [3] 郝亮亮, 李佳慧, 段贤稳, 等. 核电多相环形无刷励磁机转子绕组短路故障特征分析[J]. 电工技术学报, 2020, 35(6): 1251-1261. Hao Liangliang, Li Jiahui, Duan Xianwen, et al.Characteristic analysis of short-circuit fault in rotor winding of nuclear power multi-phase annular brushless exciter[J]. Transactions of China Electro- technical Society, 2020, 35(6): 1251-1261. [4] 吴新振, 张伟玲, 董海涛, 等. 十二相整流发电机二极管开路的故障运行分析[J]. 电工技术学报, 2017, 32(3): 157-163. Wu Xinzhen, Zhang Weiling, Dong Haitao, et al.Analysis of diode-open fault operation for 12-phase rectifier generators[J]. Transactions of China Elec- trotechnical Society, 2017, 32(3): 157-163. [5] Bui H K, Bracikowski N, Hecquet M, et al.Simulation of a large power brushless synchronous generator (BLSG) with a rotating rectifier by a reluctance network for fault analysis and diagnosis[J]. IEEE Transactions on Industry Applications, 2017, 53(5): 4327-4337. [6] Wu Yucai, Cai Bochong, Ma Qianqian.An online diagnostic method for rotary diode open-circuit faults in brushless exciters[J]. IEEE Transactions on Energy Conversion, 2018, 33(4): 1677-1685. [7] 张伟玲, 吴新振, 魏锟, 等. 十二相整流发电机缺桥故障运行分析[J]. 中国电机工程学报, 2016, 36(6): 1724-1730. Zhang Weiling, Wu Xinzhen, Wei Kun, et al.Analysis of bridge-loss fault operation of 12-phase generators with rectifier-load[J]. Proceedings of the CSEE, 2016, 36(6): 1724-1730. [8] 蔡波冲, 武玉才, 赵艳军. 基于定子电流谐波法的无刷励磁机旋转二极管开路故障检测[J]. 大电机技术, 2018(4): 61-65. Cai Bochong, Wu Yucai, Zhao Yanjun.The detection of open-circuit fault of rotary diode in brushless exciter using stator current harmonic method[J]. Large Electric Machine and Hydraulic Turbine, 2018(4): 61-65. [9] 郝亮亮, 李佳慧, 李洪学, 等. 核电多相角形无刷励磁系统旋转二极管开路故障特征分析[J]. 电力系统自动化, 2019, 43(11): 112-120. Hao Liangliang, Li Jiahui, Li Hongxue, et al.Characteristic analysis of open-circuit fault of rotating diode in nuclear multi-phase angular brush- less excitation system[J]. Automation of Electric Power Systems, 2019, 43(11): 112-120. [10] 朱继忠, 骆腾燕, 吴皖莉, 等. 综合能源系统运行可靠性评估评述Ⅱ: 数据驱动法与模型-数据混合驱动法[J]. 电工技术学报, 2022, 37(13): 3227-3240. Zhu Jizhong, Luo Tengyan, Wu Wanli, et al.A review of operational reliability assessment of integrated energy systems Ⅱ: data-driven method and model- data hybrid driven method[J]. Transactions of China Electrotechnical Society, 2022, 37(13): 3227-3240. [11] 马伟明. 关于电工学科前沿技术发展的若干思考[J]. 电工技术学报, 2021, 36(22): 4627-4636. Ma Weiming.Thoughts on the development of frontier technology in electrical engineering[J]. Transactions of China Electrotechnical Society, 2021, 36(22): 4627-4636. [12] 崔江, 冯赛, 张卓然, 等. 基于BLS的无刷发电机旋转整流器特征提取技术研究[J]. 中国电机工程学报, 2020, 40(12): 4004-4013. Cui Jiang, Feng Sai, Zhang Zhuoran, et al.Research on feature extraction technology of brushless gener- ator rotating rectifier based on BLS[J]. Proceedings of the CSEE, 2020, 40(12): 4004-4013. [13] 崔江, 郭瑞东, 张卓然, 等. 基于改进DBN的发电机旋转整流器故障特征提取技术[J]. 中国电机工程学报, 2020, 40(7): 2369-2376, 2415. Cui Jiang, Guo Ruidong, Zhang Zhuoran, et al.Generator rotating rectifier fault feature extraction technique based on improved DBN[J]. Proceedings of the CSEE, 2020, 40(7): 2369-2376, 2415. [14] Rahnama M, Vahedi A, Alikhani A M, et al.Diode open-circuit fault detection in rectifier bridge of the brushless synchronous generator[C]//2018 XIII Inter- national Conference on Electrical Machines (ICEM), Alexandroupoli, Greece, 2018: 1821-1826. [15] Mohammad-Alikhani A, Rahnama M, Vahedi A.Neighbors class solidarity feature selection for fault diagnosis of brushless generator using thermal imaging[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(9): 6221-6227. [16] 纪守领, 李进锋, 杜天宇, 等. 机器学习模型可解释性方法、应用与安全研究综述[J]. 计算机研究与发展, 2019, 56(10): 2071-2096. Ji Shouling, Li Jinfeng, Du Tianyu, et al.Survey on techniques, applications and security of machine learning interpretability[J]. Journal of Computer Research and Development, 2019, 56(10): 2071-2096. [17] 任守纲, 张景旭, 顾兴健, 等. 时间序列特征提取方法研究综述[J]. 小型微型计算机系统, 2021, 42(2): 271-278. Ren Shougang, Zhang Jingxu, Gu Xingjian, et al.Overview of feature extraction algorithms for time series[J]. Journal of Chinese Computer Systems, 2021, 42(2): 271-278. [18] Zhang Quanshi, Wu Ying Nian, Zhu Songchun.Interpretable convolutional neural networks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018: 8827-8836. [19] Selvaraju R R, Cogswell M, Das A, et al.Grad-CAM: visual explanations from deep networks via gradient- based localization[C]//2017 IEEE International Con- ference on Computer Vision (ICCV), Venice, Italy, 2017: 618-626. [20] Chattopadhay A, Sarkar A, Howlader P, et al.Grad- CAM: generalized gradient-based visual explanations for deep convolutional networks[C]//2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA, 2018: 839-847. [21] Wang Haofan, Wang Zifan, Du Mengnan, et al.Score-CAM: score-weighted visual explanations for convolutional neural networks[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recog- nition Workshops (CVPRW), Seattle, WA, USA, 2020: 111-119. [22] Zeiler M D, Fergus R.Visualizing and understanding convolutional networks[C]//Proceedings of the 13th European Conference on Computer Vision, Zurich, Switzerland, 2014: 818-833. [23] Fong R C, Vedaldi A.Interpretable explanations of black boxes by meaningful perturbation[C]//2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017: 3449-3457. [24] Chen Jianlin, Hao Liangliang, Li Huazhong, et al.Time-frequency characteristics analysis and diagnosis of rotating rectifier faults in multiphase annular brushless system[J]. IEEE Transactions on Industrial Electronics, 2023, 70(4): 3233-3244. [25] 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 Dahai, Zhang Xiaowei, Sun Hao, 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. [26] 王艳新, 闫静, 王建华, 等. 基于域对抗迁移卷积神经网络的小样本GIS绝缘缺陷智能诊断方法[J]. 电工技术学报, 2022, 37(9): 2150-2160. Wang Yanxin, Yan Jing, Wang Jianhua, et al.Intelligent diagnosis for GIS with small samples using a novel adversarial transfer learning in convolutional neural network[J]. Transactions of China Electro- technical Society, 2022, 37(9): 2150-2160. [27] 蔡智超, 孙翼虎, 赵振勇, 等. 基于时频分析和深度学习的表面粗糙度超声模式识别方法[J]. 电工技术学报, 2022, 37(15): 3743-3752. Cai Zhichao, Sun Yihu, Zhao Zhenyong, et al.A deep learning-based electromagnetic ultrasonic recognition method for surface roughness of workpeice[J]. Transactions of China Electrotechnical Society, 2022, 37(15): 3743-3752. [28] Yu F, Koltun V. Multi-scale context aggregation by dilated convolutions[EB/OL].2015, arXiv: 1511.07122. https://arxiv.org/abs/1511.07122. [29] Bridle J S.Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition[J]. Neurocomputing: Algorithms, Architectures and Applications, 1989, 68: 227-236. [30] Rubinstein R.The cross-entropy method for combi- natorial and continuous optimization[J]. Methodology and Computing in Applied Probability, 1999, 1(2): 127-190. [31] Maaten L, Hinton G.Visualizing data using t-SNE[J]. Journal of Machine Learning Research, 2008, 9(86): 2579-2605.
2023, Vol. 38 
