Abstract：In DC microgrids, the negative impedance characteristics of constant power loads (CPL) can reduce system damping, leading to system collapse. To enhance the stability of DC microgrid systems, researchers propose various active damping addition methods, including virtual active resistance compensation, synchronous buck circuit, and output current feedforward, which require complex control algorithms and signal processing. In addition, these methods may reduce load performance and affect the system's dynamic response. Compared with active dampers, passive damping only requires simple passive components without complex control algorithms and signal processing technology. Accordingly, the implementation is simple, and the cost is low. To analyze the stability of the DC microgrid after adding dampers, traditional methods based on the mechanism model ignore the structure influence, and the model error gradually increases with the increase of system dimension and complexity. Thus, this paper proposes a machine learning-based method to detect the stability of DC microgrids with passive damping. This method can effectively improve the error problem of the mechanism model, comprehensively consider the relationship between local and global stability, and realize the rapid and accurate online detection of the passive damped DC microgrid stable state. Firstly, this paper establishes an impedance model for the DC microgrid system with passive damping, using the Routh-Hurwitz criterion to determine the parameters that influence system stability. Secondly, simulation scenarios are conducted based on the selected system parameters to obtain sample data for training the machine learning algorithm. A stability detection model based on the light gradient boosting machine (LGBM) is introduced for DC microgrids. The influence of the selected parameters on the LGBM prediction and the stability of the DC microgrid system is further explored using Shapley additive explanations (SHAP). Finally, the effectiveness and superiority of the proposed method are validated through simulations and hardware-in-the-loop experiments. The results show that the CPL critical value is increased by 31 kW, 15 kW, and 22 kW, respectively, when RC parallel, RL parallel, and RLC series dampers are added to the single energy storage DC microgrid. The RL series damper only improves the CPL critical value by 3kW. After establishing six simulation scenarios with RC parallel, RL parallel, and RLC series dampers, the proposed LGBM achieves prediction accuracy of more than 96.5% and a false alarm rate of less than 2.5% when there is uncertainty in source and load. Compared with traditional mathematical, machine learning, and deep learning methods, the proposed method has advantages in prediction accuracy and computational speed. Among the selected system parameter features, CPL, line inductance, and damper inductance are negatively correlated with the stability of the DC microgrid. In contrast, line capacitance and damper capacitance are positively correlated with the stability of DC microgrids. The following conclusions can be drawn. (1) RC parallel dampers, RL parallel dampers, and RLC series dampers significantly enhance the stability of the DC microgrid system with CPL. However, the RL series damper does not show an ideal improvement effect. (2) The proposed LGBM model consistently achieves the highest prediction accuracy across multiple scenarios and reduces computational time. (3) The SHAP method effectively explains the impact of each input feature on the stability of the LGBM detection DC microgrid, providing interpretability to the LGBM model.
刘笑, 杨建, 李力, 董密, 宋冬然. 基于机器学习的带被动阻尼直流微电网系统的稳定性检测[J]. 电工技术学报, 2024, 39(8): 2281-2293.
Liu Xiao, Yang Jian, Li Li, Dong Mi, Song Dongran. Stability Detection of DC Microgrid Systems with Passive Damping Based on Machine Learning. Transactions of China Electrotechnical Society, 2024, 39(8): 2281-2293.
[1] Xia Yanghong, Wei Wei, Long Teng, et al.New analysis framework for transient stability evaluation of DC microgrids[J]. IEEE Transactions on Smart Grid, 2020, 11(4): 2794-2804. [2] 陶珑, 王萍, 王议锋, 等. 微电网低压接口变换器 的参数寻优自抗扰控制[J]. 电工技术学报, 2022, 37(16): 4202-4211. 3 Tao Long, Wang Ping, Wang Yifeng, et al.Active disturbance rejection control with automatic optimi- zation for low-voltage interface converter in micro- grid[J]. Transactions of China Electrotechnical Society, 2022, 37(16): 4202-4211. [3] 张伟亮, 张辉, 支娜, 等. 基于节点源荷电流差分的直流微电网储能变换器控制策略[J]. 电工技术学报, 2022, 37(9): 2199-2210. Zhang Weiliang, Zhang Hui, Zhi Na, et al.Control strategy of DC microgrid energy storage converter based on node differential current[J]. Transactions of China Electrotechnical Society, 2022, 37(9): 2199-2210. [4] 王晴, 刘增, 韩鹏程, 等. 基于变流器输出阻抗的直流微电网下垂并联系统振荡机理与稳定边界分析[J]. 电工技术学报, 2023, 38(8): 2148-2161. Wang Qing, Liu Zeng, Han Pengcheng, et al.Analysis of oscillation mechanism and stability boundary of droop-controlled parallel converters based on output impedances of individual converters in DC micro- grids[J]. Transactions of China Electrotechnical Society, 2023, 38(8): 2148-2161. [5] 张辉, 梁誉馨, 孙凯, 等. 直流微电网中多端口隔离型DC-DC变换器的改进虚拟电容控制策略[J]. 电工技术学报, 2021, 36(2): 292-304. Zhang Hui, Liang Yuxin, Sun Kai, et al.Improved virtual capacitor control strategy of multi-port isolated DC-DC converter in DC microgrid[J]. Transactions of China Electrotechnical Society, 2021, 36(2): 292-304. [6] 张泽华, 宋桂英, 张晓璐, 等. 考虑恒功率负载的直流微电网稳定性与鲁棒性控制策略[J]. 电工技术学报, 2023, 38(16): 4391-4405. Zhang Zehua, Song Guiying, Zhang Xiaolu, et al.Stability and robustness control strategy of DC microgrid considering constant power load[J]. Transa- ctions of China Electrotechnical Society, 2023, 38(16): 4391-4405. [7] 谢文强, 韩民晓, 严稳利, 等. 考虑恒功率负荷特性的直流微电网分级稳定控制策略[J]. 电工技术学报, 2019, 34(16): 3430-3443. Xie Wenqiang, Han Minxiao, Yan Wenli, et al.Hierarchical stability control strategy of DC micro- grid considering constant power load performance[J]. Transactions of China Electrotechnical Society, 2019, 34(16): 3430-3443. [8] 朱晓荣, 李铮, 孟凡奇. 基于不同网架结构的直流微电网稳定性分析[J]. 电工技术学报, 2021, 36(1): 166-178. Zhu Xiaorong, Li Zheng, Meng Fanqi.Stability analysis of DC microgrid based on different grid structures[J]. Transactions of China Electrotechnical Society, 2021, 36(1): 166-178. [9] Liu Haiyuan, Guo Wenzhong, Cheng Dong, et al.Stability and bifurcation analysis of DC microgrid with multiple droop control sources and loads[J]. IEEE Transactions on Power Electronics, 2021, 36(2): 2361-2372. [10] 吴琦, 邓卫, 谭建鑫, 等. 基于下垂控制的多端直流系统稳定性分析[J]. 电工技术学报, 2021, 36(增刊2): 507-516. Wu Qi, Deng Wei, Tan Jianxin, et al.Stability analysis of multi-terminal DC system based on droop control[J]. Transactions of China Electrotechnical Society, 2021, 36(S2): 507-516. [11] Cespedes M, Xing Lei, Sun Jian.Constant-power load system stabilization by passive damping[J]. IEEE Transactions on Power Electronics, 2011, 26(7): 1832-1836. [12] Du Yuhua, Men Yuxi, Ding Lizhi, et al.Large-signal stability analysis for inverter-based dynamic micro- grids reconfiguration[J]. IEEE Transactions on Smart Grid, 2023, 14(2): 836-852. [13] Chang Fangyuan, Cui Xiaofan, Wang Mengqi, et al.Large-signal stability criteria in DC power grids with distributed-controlled converters and constant power loads[J]. IEEE Transactions on Smart Grid, 2020, 11(6): 5273-5287. [14] 刘宿城, 李响, 秦强栋, 等. 直流微电网集群的大信号稳定性分析[J]. 电工技术学报, 2022, 37(12): 3132-3147. Liu Sucheng, Li Xiang, Qin Qiangdong, et al.Large signal stability analysis for DC microgrid clusters[J]. Transactions of China Electrotechnical Society, 2022, 37(12): 3132-3147. [15] Jiang Jianbo, Liu Fei, Pan Shangzhi, et al.A conservatism-free large signal stability analysis method for DC microgrid based on mixed potential theory[J]. IEEE Transactions on Power Electronics, 2019, 34(11): 11342-11351. [16] 明佳, 王玉斌, 王璠, 等. 直流微网的大信号稳定性分析及有源阻尼补偿方法[J]. 电工技术学报, 2021, 36(增刊2): 517-529. Ming Jia, Wang Yubin, Wang Fan, et al.Large-signal stability analysis and active damping compensation methods for DC microgrid[J]. Transactions of China Electrotechnical Society, 2021, 36(S2): 517-529. [17] Wu Mingfei, Lu D D C. A novel stabilization method of LC input filter with constant power loads without load performance compromise in DC microgrids[J]. IEEE Transactions on Industrial Electronics, 2015, 62(7): 4552-4562. [18] 季宇, 王东旭, 吴红斌, 等. 提高直流微电网稳定性的有源阻尼方法[J]. 电工技术学报, 2018, 33(2): 370-379. Ji Yu, Wang Dongxu, Wu Hongbin, et al.The active damping method for improving the stability of DC microgrid[J]. Transactions of China Electrotechnical Society, 2018, 33(2): 370-379. [19] Li Xiangke, Zhang Xinan, Jiang Wentao, et al.A novel assorted nonlinear stabilizer for DC-DC multilevel Boost converter with constant power load in DC microgrid[J]. IEEE Transactions on Power Electronics, 2020, 35(10): 11181-11192. [20] Aldhaheri A, Etemadi A H.Stabilization and per- formance preservation of DC-DC cascaded systems by diminishing output impedance magnitude[J]. IEEE Transactions on Industry Applications, 2018, 54(2): 1481-1489. [21] 杜镇宇, 阳岳希, 季柯, 等. 张北柔直工程高频谐波振荡机理与抑制方法研究[J]. 电网技术, 2022, 46(8): 3066-3075. Du Zhenyu, Yang Yuexi, Ji Ke, et al.High frequency harmonic resonance and suppression in Zhangbei project[J]. Power System Technology, 2022, 46(8): 3066-3075. [22] 黄明增, 胡雅涵, 文云峰, 等. 融合JMIM和NGBoost的电力系统暂态稳定评估方法[J]. 电力系统自动化, 2021, 45(8): 155-165. Huang Mingzeng, Hu Yahan, Wen Yunfeng, et al.Assessment method of transient stability for power system based on joint mutual information maximi- zation and natural gradient Boosting[J]. Automation of Electric Power Systems, 2021, 45(8): 155-165. [23] Zhang Yuchen, Xu Yan, Zhang Rui, et al.A missing-data tolerant method for data-driven short- term voltage stability assessment of power systems[J]. IEEE Transactions on Smart Grid, 2019, 10(5): 5663-5674. [24] Chen Yangfan, Zhang Yu, Qu Erya, et al.Active damping method for LC-DAB system based on a power-based impedance model[J]. IEEE Transactions on Power Electronics, 2023, 38(4): 4405-4418. [25] Beres R N, Wang Xiongfei, Blaabjerg F, et al.Optimal design of high-order passive-damped filters for grid-connected applications[J]. IEEE Transactions on Power Electronics, 2015, 31(3): 2083-2098. [26] 滕昌鹏, 王玉斌, 周博恺, 等. 含恒功率负载的直流微网大信号稳定性分析[J]. 电工技术学报, 2019, 34(5): 973-982. Teng Changpeng, Wang Yubin, Zhou Bokai, et al.Large-signal stability analysis of DC microgrid with constant power loads[J]. Transactions of China Electrotechnical Society, 2019, 34(5): 973-982. [27] 肖迁, 穆云飞, 焦志鹏, 等. 基于改进LightGBM的电动汽车电池剩余使用寿命在线预测[J]. 电工技术学报, 2022, 37(17): 4517-4527. Xiao Qian, Mu Yunfei, Jiao Zhipeng, et al.Improved LightGBM based remaining useful life prediction of lithium-ion battery under driving conditions[J]. Transactions of China Electrotechnical Society, 2022, 37(17): 4517-4527. [28] Ye Lin, Dai Binhua, Li Zhuo, et al.An ensemble method for short-term wind power prediction considering error correction strategy[J]. Applied Energy, 2022, 322: 119475. [29] 郑心仕, 梁寿愚, 苏晓, 等. 基于贝叶斯方法与可解释机器学习的负荷特性分析与预测[J]. 电力系统自动化, 2023, 47(13): 56-68. Zheng Xinshi, Liang Shouyu, Su Xiao, et al.Characteristic analysis and load forecasting based on Bayesian method and interpretable machine learning[J]. Automation of Electric Power Systems, 2023, 47(13): 56-68. [30] 顾菊平, 蒋凌, 张新松, 等. 基于特征提取的锂离子电池健康状态评估及影响因素分析[J]. 电工技术学报, 2023, 38(19): 5330-5342. Gu Juping, Jiang Ling, Zhang Xinsong, et al.Estimation and influencing factor analysis of lithium- ion batteries state of health based on features extraction[J]. Transactions of China Electrotechnical Society, 2023, 38(19): 5330-5342.
2024, Vol. 39 
