TRANSIENT STABILITY EVALUATION OF GRID-CONNECTED PHOTOVOLTAIC ENERGY STORAGE SYSTEM BASED ON gcForest-AdaBoost

Wang Yuchen, Li Zhongyan, Su Han

Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (6) : 58-67.

PDF(1101 KB)
Welcome to visit Acta Energiae Solaris Sinica, Today is
PDF(1101 KB)
Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (6) : 58-67. DOI: 10.19912/j.0254-0096.tynxb.2025-0134

TRANSIENT STABILITY EVALUATION OF GRID-CONNECTED PHOTOVOLTAIC ENERGY STORAGE SYSTEM BASED ON gcForest-AdaBoost

  • Wang Yuchen1, Li Zhongyan1, Su Han2
Author information +
History +

Abstract

It is of great significance to realize transient stability evaluation accurately and quickly for stable and safe operation of power system. Aiming at the problem of low prediction accuracy in the current transient stability evaluation of grid-connected photovoltaic energy storage systems, a transient stability evaluation method based on integrated deep Random forest algorithm (gcForest-AdaBoost) is proposed by combining deep learning and ensemble learning techniques. Firstly, the initial input features are constructed according to the steady-state components of the grid-connected photovoltaic energy storage system during operation. Secondly, in order to reduce the overfit of the model and ensure that the model still has strong learning ability after the number of cascades increases, a transient stability evaluation method based on gcForest-AdaBoost is proposed by combining deep random forest algorithm and AdaBoost algorithm. Thirdly, the gcForest-AdaBoost model is trained with input feature set, and the transient stability evaluation model of grid-connected PV energy storage system is established. Finally, the IEEE 39-node system is connected to the photovoltaic energy storage unit to build a case system for simulation analysis and data acquisition, and the evaluation results are obtained. Numerical examples show that the proposed model can effectively analyze the transient stability of grid-connected photovoltaic energy storage systems, and it is found that the proposed model has good robustness and generalization ability.

Key words

solar power generation / energy storage / transient stability / random forests / gcForest-AdaBoost / input characteristics

Cite this article

Download Citations
Wang Yuchen, Li Zhongyan, Su Han. TRANSIENT STABILITY EVALUATION OF GRID-CONNECTED PHOTOVOLTAIC ENERGY STORAGE SYSTEM BASED ON gcForest-AdaBoost[J]. Acta Energiae Solaris Sinica. 2026, 47(6): 58-67 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0134

References

[1] 文继锋, 刘子俊, 周专, 等. 新型电力系统下高渗透新能源接入的次同步振荡问题研究[J]. 太阳能学报, 2024, 45(11): 50-60.
WEN J F, LIU Z J, ZHOU Z, et al.Research on sub-synchronous oscillation problem with high permeability new energy access in new power system[J]. Acta energiae solaris sinica, 2024, 45(11): 50-60.
[2] 李汐, 高阳, 马斌, 等. 基于PEF-PinSVM的含可再生能源并网电力系统电压暂态稳定裕度评估[J]. 太阳能学报, 2024, 45(8): 240-248.
LI X, GAO Y, MA B, et al.Voltage transient stability margin assessment of grid connected power systems containing renewable energy based on PEF-PinSVM[J]. Acta energiae solaris sinica, 2024, 45(8): 240-248.
[3] REN C, XU Y.A fully data-driven method based on generative adversarial networks for power system dynamic security assessment with missing data[J]. IEEE transactions on power systems, 2019, 34(6): 5044-5052.
[4] 田春胜, 任永峰, 胡志帅, 等. 基于自恢复型下垂控制的微电网运行控制策略研究[J]. 太阳能学报, 2024, 45(8): 71-77.
TIAN C S, REN Y F, HU Z S, et al.Research on microgrid operation control strategy based on self-recovery droop control[J]. Acta energiae solaris sinica, 2024, 45(8): 71-77.
[5] 林伟芳, 任晓钰, 张桂红, 等. 考虑功角稳定和暂态过电压的新能源电压穿越控制参数优化[J]. 电网技术, 2023, 47(4): 1323-1330.
LIN W F, REN X Y, ZHANG G H, et al.Optimization of voltage ride-through control parameters of renewable energy considering power angle stability and transient overvoltage[J]. Power system technology, 2023, 47(4): 1323-1330.
[6] 张玉冰, 申彦波, 姚鑫, 等. 青海高原光伏适宜性评价的不同决策树算法的比较研究[J]. 太阳能学报, 2024, 45(12): 30-39.
ZHANG Y B, SHEN Y B, YAO X, et al.Comparative study of different decision tree algorithms for PV suitability evaluation in Qinghai plateau[J]. Acta energiae solaris sinica, 2024, 45(12): 30-39.
[7] 朱乔木, 陈金富, 李弘毅, 等. 基于堆叠自动编码器的电力系统暂态稳定评估[J]. 中国电机工程学报, 2018, 38(10): 2937-2946.
ZHU Q M, CHEN J F, LI H Y, et al.Transient stability assessment based on stacked autoencoder[J]. Proceedings of the CSEE, 2018, 38(10): 2937-2946.
[8] MOVAHEDI A, NIASAR A H, GHAREHPETIAN G B. Designing SSSC, TCSC, and STATCOM controllers using AVURPSO, GSA, and GA for transient stability improvement of a multi-machine power system with PV and wind farms[J]. International journal of electrical power & energy systems, 2019, 106: 455-466.
[9] 谈赢杰, 向真, 杨昆, 等. 时序数据驱动的微电网暂态稳定运行评估[J]. 南方电网技术, 2023, 17(7): 125-134.
TAN Y J, XIANG Z, YANG K, et al.Time series data-driven transient stability assessment for microgrid[J]. Southern power system technology, 2023, 17(7): 125-134.
[10] 李志兵, 肖健梅, 王锡淮. 基于多粒度NRS和改进Bi-LSTM的电力系统暂态稳定评估[J]. 电气工程学报, 2023, 18(3): 232-241.
LI Z B, XIAO J M, WANG X H.Transient stability assessment of power system based on multi-granularity neighborhood rough set and improved Bi-directional long-short-term memory network[J]. Journal of electrical engineering, 2023, 18(3): 232-241.
[11] 李淼, 雷鸣, 周挺, 等. 基于深度森林的电力系统暂态稳定评估方法[J]. 电测与仪表, 2021, 58(2): 53-58.
LI M, LEI M, ZHOU T, et al.Transient stability assessment method for power system based on deep forest[J]. Electrical measurement & instrumentation, 2021, 58(2): 53-58.
[12] 朱瑞金, 董亚丽, 唐波. 基于改进深度森林的暂态电压稳定快速评估[J]. 电网与清洁能源, 2022, 38(6): 24-30, 43.
ZHU R J, DONG Y L, TANG B.Fast assessment of transient voltage stability based on improved deep forest[J]. Advances of power system & hydroelectric engineering, 2022, 38(6): 24-30, 43.
[13] 陈康, 王泽, 郭永吉. 基于grcForest模型的风电并网系统暂态电压稳定评估[J]. 智慧电力, 2023, 51(1): 31-37.
CHEN K, WANG Z, GUO Y J.Transient voltage stability assessment of wind power grid-connected system based on grcForest model[J]. Smart power, 2023, 51(1): 31-37.
[14] 叶进, 卢泉, 王钰淞, 等. 基于级联随机森林的光伏故障诊断模型研究[J]. 太阳能学报, 2021, 42(3): 358-362.
YE J, LU Q, WANG Y S, et al.Research on PV fault diagnosis model based on cascaded random forest[J]. Acta energiae solaris sinica, 2021, 42(3): 358-362.
[15] ZHOU Z H, FENG J.Deep forest: towards an alternative to deep neural networks[C]//Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. Melbourne, Australia, 2017: 3553-3559.
[16] 庄保乾, 韩路, 李晓虎, 等. 基于集成深度随机森林算法的智能电厂设备健康评估方法[J]. 计算机测量与控制, 2024, 32(8): 322-328.
ZHUANG B Q, HAN L, LI X H, et al.Health status assessment method of intelligent power plant equipment based on integrated deep random forest algorithm[J]. Computer measurement & control, 2024, 32(8): 322-328.
[17] 史法顺, 吴俊勇, 吴昊衍, 等. 基于深度学习的电力系统暂态功角与暂态电压稳定裕度一体化评估[J]. 电网技术, 2023, 47(2): 731-740.
SHI F S, WU J Y, WU H Y, et al.Integrated evaluation of power system transient power angle and transient voltage stability margin based on deep learning[J]. Power system technology, 2023, 47(2): 731-740.
[18] 李鹏, 董鑫剑, 孟庆伟, 等. 基于Fisher Score特征选择的电力系统暂态稳定评估方法[J]. 电力自动化设备, 2023, 43(7): 117-123.
LI P, DONG X J, MENG Q W, et al.Transient stability assessment method for power system based on Fisher Score feature selection[J]. Electric power automation equipment, 2023, 43(7): 117-123.
[19] 刘聪, 刘颂凯, 刘礼煌, 等. 基于TCN-CCRELMS的电力系统暂态稳定评估[J]. 电力系统及其自动化学报, 2023, 35(7): 36-44, 82.
LIU C, LIU S K, LIU L H, et al.Transient stability assessment of power system based on TCN-CCRELMS[J]. Proceedings of the CSU-EPSA, 2023, 35(7): 36-44, 82.
[20] 谢瀚阳, 彭泽武, 唐重阳, 等. 基于数据挖掘技术的电网时序数据质量维护研究[J]. 电测与仪表, 2022, 59(2): 38-44.
XIE H Y, PENG Z W, TANG C Y, et al.Research on power grid time-sequence data quality maintenance based on data mining technology[J]. Electrical measurement & instrumentation, 2022, 59(2): 38-44.
[21] 刘颂凯, 胡竞哲, 杨超, 等. 基于改进CDBN的电力系统暂态稳定评估[J]. 智慧电力, 2023, 51(6): 8-14, 92.
LIU S K, HU J Z, YANG C, et al.Transient stability assessment of power systems based on improved CDBN[J]. Smart power, 2023, 51(6): 8-14, 92.
[22] 张玉敏, 孙鹏凯, 叶平峰, 等. 基于CNN的配电网快速重构方法[J]. 智慧电力, 2022, 50(11): 112-118.
ZHANG Y M, SUN P K, YE P F, et al.Fast reconfiguration method of distribution network of based on convolutional neural network[J]. Smart power, 2022, 50(11): 112-118.
PDF(1101 KB)

Accesses

Citation

Detail

Sections
Recommended

/