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.
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.
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.
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.
2023, Vol. 44 
