Data Characteristics, Key Technologies and Prevention and Control Challenges of Security Risk Identification for New Grid-Connected Entities

Bo WANG, Fuqi MA, Hongxia WANG

South Power Sys Technol ›› 2025, Vol. 19 ›› Issue (7) : 15-29.

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South Power Sys Technol ›› 2025, Vol. 19 ›› Issue (7) : 15-29. DOI: 10.13648/j.cnki.issn1674-0629.2025.07.002
Special Column on Boao New Power System International Forum

Data Characteristics, Key Technologies and Prevention and Control Challenges of Security Risk Identification for New Grid-Connected Entities

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Abstract

Driven by the "dual carbon" goals, the user side of the new power system faces multidimensional security risks due to the large-scale integration of new grid-connected entities such as distributed photovoltaic systems, electric vehicle charging stations, and energy storage equipment.Firstly, the challenges to equipment safety, grid security, and societal safety posed by the massive integration of these new entities are systematically analyzed, core bottlenecks at the technical and managerial levels, such as uneven equipment quality, difficulties in cross-modal data fusion, and insufficient safety protections, are highlighted. To address the heterogeneity, semantic differences, and correlation characteristics of multimodal data, a multimodal large model construction technology is proposed, incorporating data augmentation generation, cross-modal semantic alignment, and parameter-efficient fine-tuning methods to enhance risk perception accuracy. Furthermore, integrated security prevention and control strategies are introduced from three perspectives: grid optimization, intelligent operation and maintenance, and resilience enhancement. These measures provide theoretical support and practical pathways for the security governance of the new power system, facilitating the coordinated development of energy transition and security under the "dual carbon" objectives.

Key words

double carbon / new power system / user side security / data characteristics / risk prevention and control

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Bo WANG , Fuqi MA , Hongxia WANG. Data Characteristics, Key Technologies and Prevention and Control Challenges of Security Risk Identification for New Grid-Connected Entities[J]. Southern Power System Technology. 2025, 19(7): 15-29 https://doi.org/10.13648/j.cnki.issn1674-0629.2025.07.002

References

List( Publishing order | Descend order by publishing year | Descend order by cited within ) Chart analysis
[1]
韩富佳， 王晓辉， 乔骥， 等. 基于人工智能技术的新型电力系统负荷预测研究综述［J］. 中国电机工程学报， 2023， 43（22）：8569 - 8592.
HAN Fujia， WANG Xiaohui， QIAO Ji， et al. Review on artificial intelligence based load forecasting research for the new-type power system［J］. Proceedings of the CSEE， 2023， 43（22）：8569 - 8592.
Cited in this article [1]
[2]
李响， 牛赛. 双碳目标下源⁃网⁃荷多层评价体系研究［J］. 中国电机工程学报， 2021， 41（S1）：178 - 184.
LI Xiang， NIU Sai. Study on multi-layer evaluation system of source-grid-load under carbon-neutral goal［J］. Proceedings of the CSEE， 2021， 41（S1）：178 - 184.
Cited in this article [1]
[3]
陈胜， 卫志农， 顾伟， 等. 碳中和目标下的能源系统转型与变革：多能流协同技术［J］. 电力自动化设备， 2021， 41（9）：3 - 12.
CHEN Sheng， WEI Zhinong， GU Wei， et al. Carbon neutral oriented transition and revolution of energy systems：multi-energy flow coordination technology［J］. Electric Power Automation Equipment， 2021， 41（9）：3 - 12.
Cited in this article [1]
[4]
邱伟强， 王茂春， 林振智， 等. “双碳”目标下面向新能源消纳场景的共享储能综合评价［J］. 电力自动化设备， 2021， 41（10）：244 - 255.
QIU Weiqiang， WANG Maochun， LIN Zhenzhi， et al. Comprehensive evaluation of shared energy storage towards new energy accommodation scenario under targets of carbon emission peak and carbon neutrality［J］. Electric Power Automation Equipment， 2021， 41（10）：244 - 255.
Cited in this article [1]
[5]
国务院. 国务院关于印发2030年前碳达峰行动方案的通知［EB/OL］. （2021 - 10 - 24）［2023 - 03 - 29］. http：//www. gov. cn/zhengce/content/2021 - 10/26/content_5644984. htm.
Cited in this article [1]
[6]
国家能源局. 国家能源局综合司关于公开征求《新型电力系统发展蓝皮书（征求意见稿）》意见的通知［EB/OL］. （2023 - 01 - 04）［2023 - 03 - 29］. http：//www. nea. gov. cn/2023 - 01/06/c_1310688702. htm.
Cited in this article [1]
[7]
康重庆， 杜尔顺， 李姚旺， 等. 新型电力系统的“碳视角”：科学问题与研究框架［J］. 电网技术， 2022， 46（3）：821 - 833.
KANG Chongqing， DU Ershun， LI Yaowang， et al. Key scientific problems and research framework for carbon perspective research of new power systems［J］. Power System Technology， 2022， 46（3）：821 - 833.
Cited in this article [1]
[8]
李浩， 曹华珍， 吴亚雄， 等. 含光伏低压配电网边端功率-电压控制方法［J］. 南方电网技术， 2022， 16（11）：139 - 148.
LI Hao， CAO Huazhen， WU Yaxiong， et al. Edge-side power-voltage control method of low voltage distribution network with PV systems［J］. Southern Power System Technology， 2022， 16（11）：139 - 148.
Cited in this article [1]
[9]
周劼英， 张晓， 邵立嵩， 等. 新型电力系统网络安全防护挑战与展望［J］. 电力系统自动化， 2023， 47（8）：15 - 24.
ZHOU Jieying， ZHANG Xiao， SHAO Lisong， et al. Challenges and prospects of cyber security protection for new power system［J］. Automation of Electric Power Systems， 2023， 47（8）：15 - 24.
Cited in this article [1]
[10]
高辉， 彭成薇， 李炜卓， 等. 电动汽车与充电设备充电安全预警研究综述［J］. 电力系统自动化， 2024， 48（7）：47 - 61.
GAO Hui， PENG Chengwei， LI Weizhuo， et al. Review on early warning of charging safety for electric vehicles and charging equipment［J］. Automation of Electric Power Systems， 2024， 48（7）：47 - 61.
Cited in this article [1]
[11]
陈雷， 刘波， 孙凯， 等. 基于改进DeepLabV3+网络的光伏组件热斑故障识别及状态量化评估方法研究［J］. 太阳能学报， 2025， 46（3）：445 - 453.
CHEN Lei， LIU Bo， SUN Kai， et al. Research on hot spot fault identification method and state quantitative evaluation of photovoltaic module based on improved DeepLabV3+ network［J］. Acta Energiae Solaris Sinica， 2025， 46（3）：445 - 453.
Cited in this article [1]
[12]
肖先勇， 陈智凡， 汪颖， 等. 基于累积和事件段识别与改进谱聚类的锂离子电池储能系统内短路故障检测方法［J］. 电网技术， 2024， 48（2）：658 - 670.
XIAO Xianyong， CHEN Zhifan， WANG Ying， et al. Internal short circuit fault detection in li-ion battery storage system based on cusum event segment identification and improved spectral clustering algorithm［J］. Power System Technology， 2024， 48（2）：658 - 670.
Cited in this article [1]
[13]
何华锦， 任洲洋， 冯健冰， 等. 考虑三相负荷时空特性和配变动态安全负载域的配变周前分相重过载预警方法［J/OL］. 电工技术学报， 1 - 17［2025 - 07 - 17］. https：//doi. org/10. 19595/j. cnki. 1000 - 6753. tces. 241943.
HE Huajin， REN Zhouyang， FENG Jianbing， et al. Week-ahead phase-separated overload warning method for distribution transformers considering the spatiotemporal characteristics of three-phase loads and the dynamic safety loading region［J/OL］. Transactions of China Electrotechnical Society， 1 - 17［2025 - 07 - 17］. https：//doi. org/10. 19595/j. cnki. 1000 - 6753. tces. 241943.
Cited in this article [1]
[14]
张海峰， 任洲洋， 冯健冰， 等. 基于内驱进化预测模型和载荷能力动态评估的变压器周前重过载预警方法［J］. 电网技术， 2024， 48（10）：4349 - 4358.
ZHANG Haifeng， REN Zhouyang， FENG Jianbing， et al. A week-ahead heavy and overload warning strategy based on an endogenous evolutionary forecasting model and a transformer rating dynamic evaluation method［J］. Power System Technology， 2024， 48（10）：4349 - 4358.
Cited in this article [1]
[15]
CHEN T， ZHANG C， JING W， et al. State of health estimation for lithium-ion batteries using separable logsparse self-attention transformer［J］. IEEE Transactions on Instrumentation and Measurement， 2025.
Cited in this article [1]
[16]
LEE S， CHOI D H. Multilevel deep reinforcement learning for secure reservation-based electric vehicle charging via differential privacy and energy storage system［J］. IEEE Transactions on Vehicular Technology， 2024， 73（8）： 11097 - 11109.
Cited in this article [1]
[17]
LIU X， ZHAO X， XIA Z， et al. Secure outsourced SIFT： Accurate and efficient privacy-preserving image SIFT feature extraction［J］. IEEE Transactions on Image Processing， 2023（32）： 4635 - 4648.
Cited in this article [1]
[18]
PAL K， NAMRATA K， AKELLA A K， et al. Machine learning based image pattern recognition using histogram of oriented gradient for islanding detection［J］. IEEE Access， 2025（356）：41 - 45.
Cited in this article [1]
[19]
程志友， 汪德胜. 基于机器学习与疫情关联特征的短期负荷预测［J］. 电力系统保护与控制， 2022， 50（23）：1 - 8.
CHENG Zhiyou， WANG Desheng. Short-term load forecasting based on machine learning and epidemic association features［J］. Power System Protection and Control， 2022， 50（23）：1 - 8.
Cited in this article [1]
[20]
彭宇文， 林镇宏， 黄京苑， 等. 基于EEMD和BiGRU-Attention的短期风电功率预测方法［J/OL］. 南方电网技术， 1 - 10［2025 - 07 - 19］. http：//kns. cnki. net/kcms/detail/44. 1643. TK. 20250506. 1419. 006. html.
PENG Yuwen， LIN Zhenhong， HUANG Jingyuan， et al. Short-term wind power prediction method based on eemd and bigru-attention［J/OL］. Southern Power System Technology， 1 - 10［2025 - 07 - 19］. http：//kns. cnki. net/kcms/detail/44. 1643. TK. 20250506. 1419. 006. html.
Cited in this article [1]
[21]
ZHU Qiankun， LI Wenqi， WANG Xianyu， et al. Anomaly detection in bridge structural health monitoring via 1D-LBP and statistical feature fusion［J］. Structures， 2024（70）：107734. 1 - 107734. 12.
Cited in this article [1]
[22]
ITO N， HASHIMOTO M， OTSUKA A. Feature extraction methods for binary code similarity detection using neural machine translation models［J］. IEEE Access， 2023（11）： 102796 - 102805.
Cited in this article [1]
[23]
马恒瑞， 袁傲添， 王波， 等. 基于深度学习的负荷预测研究综述与展望［J］. 高电压技术， 2025， 51（3）：1233 - 1250.
MA Hengrui， YUAN Aotian， WANG Bo， et al. Review and prospect of load forecasting based on deep learning［J］. High Voltage Engineering， 2025， 51（3）：1233 - 1250.
Cited in this article [1]
[24]
AHN J， LEE Y， HAN B， et al. A highly effective and robust structure-based LSTM with feature-vector tuning framework for high-accuracy SOC estimation in EV［J］. Energy， 2025（325）： 136134. 1 - 136134. 15.
Cited in this article [1]
[25]
王妮妮， 马萍， 张宏立， 等. 基于多尺度深度卷积网络特征融合的滚动轴承故障诊断［J］. 太阳能学报， 2022， 43（4）：351 - 358.
WANG Nini， MA Ping， ZHANG Hongli， et al. Fault diagnosis of rolling bearing based on feature fusion of multi-scale deep convolutional network［J］. Acta Energiae Solaris Sinica， 2022， 43（4）：351 - 358.
Cited in this article [1]
[26]
DRIR N， CHEKIRED F， MELLIT A， et al. Hybrid CNN-EML model for fault diagnosis in Electroluminescence images of photovoltaic cells［J］. Renewable Energy， 2025（250）： 123343. 1 - 123343. 11.
Cited in this article [1]
[27]
陈鹏， 邰彬， 石英， 等. 融合BERT、双向长短记忆网络和条件随机场的电力设备缺陷文本实体抽取［J］. 电网技术， 2023， 47（10）：4367 - 4376.
CHEN Peng， TAI Bin， SHI Ying， et al. Text entity extraction of power equipment defects based on BERT-BI-LSTM-CRF algorithm［J］. Power System Technology， 2023， 47（10）：4367 - 4376.
Cited in this article [1]
[28]
LIU Y， LI K， YAN D， et al. A network-based CNN model to identify the hidden information in text data［J］. Physica A： Statistical Mechanics and its Applications， 2022（590）： 126744. 1 - 126744. 11.
Cited in this article [1]
[29]
王红霞， 王波， 董旭柱， 等. 面向电力多模态融合的语义差异性和感知能力差异性分析方法［J］. 高电压技术， 2024， 50（9）：4037 - 4047.
WANG Hongxia， WANG Bo， DONG Xuzhu， et al. Semantic difference and performance difference analysis method for power multimodal data fusion［J］. High Voltage Engineering， 2024， 50（9）：4037 - 4047.
Cited in this article [1]
[30]
CUI S， CHEN W， XIONG W， et al. SiMultiF： a remote sensing multimodal semantic segmentation network with adaptive allocation of modal weights for siamese structures in multi-scene［J］. IEEE Transactions on Geoscience and Remote Sensing， 2025（63）：1107 - 1118.
Cited in this article [1]
[31]
黄光攀， 张林鍹， 任峰， 等. 基于单应性评估的低压电气设备红外与可见光图像配准［J］. 红外与激光工程， 2025， 54（6）：347 - 360.
HUANG Guangpan， ZHANG Linxuan， REN Feng， et al. Infrared and visible image registration of low-voltage electrical equipment based on homography evaluation［J］. Infrared and Laser Engineering， 2025， 54（6）：347 - 360.
Cited in this article [1]
[32]
贾景龙， 张沈习， 李珂， 等. 基于ARIMA和HO-BiLSTM的变压器监测数据清洗方法［J/OL］. 高电压技术， 1 - 11［2025 - 06 - 30］. https：//doi. org/10. 13336/j. 1003 - 6520. hve. 20241594.
JIA Jinglong， ZHANG Shenxi， LI Ke， et al. Transformer monitoring data cleaning method based on ARIMA and HO-BiLSTM［J/OL］. High Voltage Engineering， 1 - 11［2025 - 06 - 30］. https：//dod. org/ 10. 13336/j. 1003 - 6520. hve. 20241594.
Cited in this article [1]
[33]
BASUMATARY H， KHATUA M， NATH S. False data injection attack detection in EV charging network using NARX neural network［J］. IEEE Transactions on Transportation Electrification， 2025.
Cited in this article [1]
[34]
XU H， PANG G， WANG Y， et al. Deep isolation forest for anomaly detection［J］. IEEE Transactions on Knowledge and Data Engineering， 2023， 35（12）： 12591 - 12604.
Cited in this article [1]
[35]
CHEN L， XU Y， ZHU Q X， et al. Adaptive multi-head self-attention based supervised VAE for industrial soft sensing with missing data［J］. IEEE Transactions on Automation Science and Engineering， 2023， 21（3）： 3564 - 3575.
Cited in this article [1]
[36]
李海燕， 乔仁超， 李海江， 等. 基于全局残差注意力和门控特征融合的CNN-Transformer去雾算法［J］. 东北大学学报（自然科学版）， 2025， 46（1）：26 - 34.
LI Haiyan， QIAO Renchao， LI Haijiang， et al. CNN-transformer dehazing algorithm based on global residual attention and gated feature fusion［J］. Journal of Northeastern University（Natural Science）， 2025， 46（1）：26 - 34.
Cited in this article [1]
[37]
WU T， WU W， YANG Y， et al. Retinex image enhancement based on sequential decomposition with a plug-and-play framework［J］. IEEE Transactions on Neural Networks and Learning Systems， 2023， 35（10）： 14559 - 14572.
Cited in this article [1]
[38]
牛海清， 吴炬卓， 许佳， 等. 考虑尺度间相关性的电缆瓷套终端红外图像去噪［J］. 华南理工大学学报（自然科学版）， 2017， 45（4）：15 - 21.
NIU Haiqing， WU Juzhuo， XU Jia， et al. Denoising of infrared images of porcelain sleeve cable terminal considering inter-scale correlation［J］. Journal of South China University of Technology （ Natural Science Edition）， 2017， 45（4）：15 - 21.
Cited in this article [1]
[39]
吴兴扬， 戴剑丰. 基于多尺度自适应时空图卷积网络与BERT模型的多节点短期负荷预测［J/OL］. 电网技术， 1 - 17［2025 - 06 - 30］. https：//doi. org/10. 13335/j. 1000 - 3673. pst. 2024. 1895.
WU Xingyang， DAI Jianfeng. Multi-node short-term load forecasting based on multi-scale adaptive spatio-temporal graph convolutional network and BERT model［J/OL］. Power System Technology， 1 - 17［2025 - 06 - 30］. https：//doi. org/10. 13335/j. 1000 - 3673. pst. 2024. 1895.
Cited in this article [1]
[40]
张钊光， 蒋庆磊， 詹瑜滨， 等. 基于VAE-GAN数据增强算法的小样本滚动轴承故障分类方法［J］. 原子能科学技术， 2023， 57（S1）：228 - 237.
ZHANG Zhaoguang， JIANG Qinglei， ZHAN Yubin， et al. VAE-GAN data enhancement networks-based model for rolling bearing few-shot fault classification［J］. Atomic Energy Science and Technology， 2023， 57（S1）：228 - 237.
Cited in this article [1]
[41]
LIU Q， MENG X， SHAO F， et al. PSTAF⁃GAN： progressive spatio-temporal attention fusion method based on generative adversarial network［J］. IEEE Transactions on Geoscience and Remote Sensing， 2022（60）： 1 - 13.
Cited in this article [1]
[42]
何志甘， 范彦琨， 付胜宪. 面向柔性直流阀厅多模态感知数据的风险预测模型［J］. 南方电网技术， 2023， 17（7）：19 - 26.
HE Zhigan， FAN Yankun， FU Shengxian. Risk prediction model for multi⁃modal perception data of flexible HVDC valve hall［J］. Southern Power System Technology， 2023， 17（7）：19 - 26.
Cited in this article [1]
[43]
LEE S， LIU L， CHOI W. Iterative translation-based data augmentation method for text classification tasks［J］. IEEE Access， 2021（9）： 160437 - 160445.
Cited in this article [1]
[44]
左建辉， 石访， 宋雪萌， 等. 主动学习与自训练相结合的电网暂态电压稳定评估方法［J/OL］. 电力系统自动化， 1 - 13［2025 - 06 - 30］. http：//kns. cnki. net/kcms/detail/32. 1180. TP. 20250616. 1005. 004. html.
ZUO Jianhui， SHI Fang， SONG Xuemeng， et al. Transient voltage stability assessment method for power grids combining active learning and self-training［J/OL］. Automation of Electric Power Systems， 1 - 13［2025 - 06 - 30］. http：//kns. cnki. net/kcms/detail/32. 1180. TP. 20250616. 1005. 004. html.
Cited in this article [1]
[45]
赵宏， 王贺， 李文改. 对比学习改进文本生成图像方法的研究［J/OL］. 计算机工程与应用， 1 - 13［2025 - 06 - 30］. http：//kns. cnki. net/kcms/ detail/11. 2127. TP. 20240909. 1436. 016. html.
ZHAO Hong， WANG He， LI Wengai. Research on improving text-to-image generation method through contrastive learning［J/OL］. Computer Engineering and Applications， 1 - 13［2025 - 06 - 30］. http：// kns. cnki. net/kcms /detail/11. 2127. TP. 20240909. 1436. 016. html.
Cited in this article [1]
[46]
张珂， 郑朝烨， 石超君， 等. 基于多模态对比学习的输电线路螺栓缺陷分类［J］. 高电压技术， 2025， 51（2）：630 - 641.
ZHANG Ke， ZHENG Zhaoye， SHI Chaojun， et al. Transmissionline bolt defects classification based on multi-modal contrastve learning［J］. High Voltage Engineering， 2025， 51（2）： 630 - 641.
Cited in this article [1]
[47]
李浩， 邢志远， 李琳利， 等. 基于多智能体的工业数字孪生系统云边端架构与关键技术［J］. 计算机集成制造系统， 2024， 30（11）：3755 - 3770.
LI Hao， XING Zhiyuan， LI Linli， et al. Cloud-edge-device system architecture and key technologies of industrial digital twin system based on multi-agent［J］. Computer Integrated Manufacturing Systems， 2024， 30（11）：3755 - 3770.
Cited in this article [1]
[48]
LIU J， LIAO Y， XU H， et al. Adaptive parameter-efficient federated fine-tuning on heterogeneous devices［J］. IEEE Transactionson Mobile Computing， 2025（109）：3586644. 1 - 3586644. 18.
Cited in this article [1]
[49]
LU C， CUI J， WANG H， et al. Privacy preserving user energyconsumption profiling： from theory to application［J］. IEEE Transactions on Smart Grid， 2023， 15（2）： 2332 - 2347.
Cited in this article [1]
[50]
廖家齐， 于若英， 刘瑜俊， 等. 基于自适应高斯混合模型的含高渗透率分布式光伏电力系统风险评估［J］. 电力系统保护与控制， 2024， 52（19）：144 - 156.
LIAO Jiaqi， YU Ruoying， LIU Yujun， et al. Risk assessment of a power system with a high penetration of distributed photo-voltaic based on self-adaptive Gaussian mixture model［J］. Power System Protection and Control， 2024， 52（19）：144 - 156.
Cited in this article [1]

Funding

the National Natural Science Foundation Youth Project(52407143)
the Headquarters Science and Technology Project of State Grid Corporation(5400-20241918A-1-1-ZN)
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