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Microgrid Fault Diagnosis Based on Deep Transfer Learning and LSTM Network |
WU Zhong-qiang,LU Xue-qin |
Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, China |
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Abstract A microgrid fault diagnosis method based on the combination of deep transfer learning and long-short-term memory network is proposed, which can be used to diagnose microgrids fault with different structures. First, wavelet packet transform is used to extract the feature vectors of fault as the input of the network; second, source domain data samples is used to pre-train the fault diagnosis model built by long short-term memory network, and relevant parameters is saved; Then transfer learning is used to migrate the parameters in the pre-training model to the domain adaptive network to obtain the combination model of deep transfer learning and long-short-term memory network; finally, the model is fine-tuned and migrated according to the labeled data (source domain data) and target domain data, and the single microgrid fault diagnosis model is transferred to other microgrids of different structures. The test results show that the proposed method can detect and identify any type of faults in microgrids of different structures. The mean square error of the identification result is 8.5905×10-5, smaller two orders of magnitude compared with the long-short-term memory network model before adaptive adjustment, the recognition effect is better and the diagnosis accuracy is obviously improved.
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Received: 26 April 2021
Published: 18 April 2023
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[3] |
刘畅, 卓建坤, 赵东明, 等. 利用储能系统实现可再生能源微电网灵活安全运行的研究综述 [J]. 中国电机工程学报, 2020, 40(1): 1-18.
|
[17] |
杨少波, 刘道伟, 安军, 等. 基于长短期记忆网络的电网动态轨迹趋势预测方法 [J]. 中国电机工程学报, 2020, 40(9): 2854-2866.
|
[1] |
吴忠强, 戚松岐, 尚梦瑶, 等. 基于优化回声状态网络的微电网等效建模 [J]. 计量学报, 2021, 42(7): 923-929.
|
[6] |
张伟亮, 张辉, 支娜, 等. 环形直流微电网故障分析与保护 [J]. 电力系统自动化, 2020, 44(24): 105-110.
|
|
Wu Z Q, Liu C Y. Parameter identification of photovoltaic cell engineering model based on IHHO algorithm [J]. Acta Metrologica Sinica, 2021, 42(2): 221-227.
|
|
Liu C, Zhuo J K, Zhao D M, et al. A review on the utilization of energy storage system for the flexible and safe operation of renewable energy microgrids [J]. Proceedings of the CSEE, 2020, 40(1): 1-18.
|
|
Wu Z Q, Wang G Y, Xie Z K, et al. Parameter identification of battery based on IALO algorithm[J]. Acta Metrologica Sinica, 2021, 42(9): 1206-1213.
|
[5] |
吴忠强, 王国勇, 谢宗奎, 等. 基于WOA-UKF 算法的锂电池容量与SOC 联合估计 [J]. 计量学报, 2022, 43(5): 649-656.
|
[8] |
Li X, Dys′ko A, Burt G M. Traveling wave-based protection scheme for inverter-dominated microgrid using mathematical morphology [J]. IEEE Transactions on Smart Grid, 2014, 5(5): 2211-2218.
|
[9] |
Li Y, Gong Y, Jiang B. A novel traveling-wave-based directional protection scheme for MTDC grid with inductive DC terminal [J]. Electric Power Systems Research, 2018, 157: 83-92.
|
[10] |
Aftab M A, Hussain S M S, Ali I, et al. Dynamic protection of power systems with high penetration of renewables: A review of the traveling wave based fault location techniques [J]. International Journal of Electrical Power & Energy Systems, 2020, 114: 105410.
|
[12] |
Hong Y Y, Cabatac M T A M. Fault detection, classification, and location by static switch in microgrids using wavelet transform and taguchi-based artificial neural network [J]. IEEE Systems Journal, 2019, 14(2): 2725-2735.
|
[15] |
和敬涵, 罗国敏, 程梦晓, 等. 新一代人工智能在电力系统故障分析及定位中的研究综述 [J]. 中国电机工程学报, 2020, 40(17): 5506-5516.
|
[2] |
吴忠强, 刘重阳. 基于IHHO算法的光伏电池工程模型的参数辨识 [J]. 计量学报, 2021, 42(2): 221-227.
|
[4] |
吴忠强, 王国勇, 谢宗奎, 等. 基于IALO算法的蓄电池参数辨识 [J]. 计量学报, 2021, 42(9): 1206-1213.
|
[16] |
Sherstinsky A. Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network [J]. Physica D: Nonlinear Phenomena, 2020, 404: 132306.
|
[19] |
张雪松, 庄严, 闫飞, 等. 基于迁移学习的类别级物体识别与检测研究与进展 [J]. 自动化学报, 2019, 45(7): 1224-1243.
|
|
Wu Z Q, Qi S Q, Shang M Y, et al. Equivalent modeling of micro-grid using optimized ESN [J]. Acta Metrologica Sinica, 2021, 42(7): 923-929.
|
|
Zhang W L, Zhang H, Zhi N, et al. Fault analysis and protection of ring DC microgrid [J]. Automation of Electric Power Systems, 2020, 44(24): 105-110.
|
[7] |
Gush T, Bukhari S B A, Haider R, et al. Fault detection and location in a microgrid using mathematical morphology and recursive least square methods [J]. International Journal of Electrical Power & Energy Systems, 2018, 102: 324-331.
|
[11] |
Abdali A, Mazlumi K, Noroozian R. High-speed fault detection and location in DC microgrids systems using Multi-Criterion System and neural network [J]. Applied Soft Computing, 2019, 79: 341-353.
|
[13] |
Gush T, Bukhari S B A, Mehmood K K, et al. Intelligent Fault Classification and Location Identification Method for Microgrids Using Discrete Orthonormal Stockwell Transform-Based Optimized Multi-Kernel Extreme Learning Machine [J]. Energies, 2019, 12(23): 4504.
|
|
He J H, Luo G M, Cheng M X, et al. A research review on application of artificial intelligence in power system fault analysis and location [J]. Proceedings of the CSEE, 2020, 40(17): 5506-5516.
|
[20] |
Li Y, Sheng H, Cheng Y, et al. State-of-health estimation of lithium-ion batteries based on semi-supervised transfer component analysis [J]. Applied Energy, 2020, 277: 115504.
|
|
Wu Z Q, Wang G Y, Xie Z K, et al. Joint estimation of the capacity and SOC of lithium battery based on WOA-UKF algorithm[J].Acta Metrologica Sinica, 2022, 43(5): 649-656.
|
[14] |
James J Q, Hou Y, Lam A Y S, et al. Intelligent fault detection scheme for microgrids with wavelet-based deep neural networks [J]. IEEE Transactions on Smart Grid, 2017, 10(2): 1694-1703.
|
|
Yang S B, Liu D W, An J, et al. Trend prediction method of power network dynamic trajectory based on long short term memory neural networks [J]. Proceedings of the CSEE, 2020, 40(9): 2854-2866.
|
[18] |
Weiss K, Khoshgoftaar T M, Wang D D. A survey of transfer learning [J]. Journal of Big data, 2016, 3(1): 1-40.
|
|
Zhang X S, Zhuang Y, Yan F, et al. Status and development of transfer learning based category-level object recognition and detection [J]. Acta Automatica Sinica, 2019, 45(7): 1224-1243.
|
|
|
|