基于稀疏自编码器技术的电网气象灾害预测预警在新型电力系统中的研究

方圆圻; 任茂鑫; 葛乃成; 李昊雨; 杨秀

方圆圻,任茂鑫,葛乃成,李昊雨,杨秀.基于稀疏自编码器技术的电网气象灾害预测预警在新型电力系统中的研究[J].电测与仪表,2026,63(7):2-14.
FANG Yuanqi,REN Maoxin,GE Naicheng,LI Haoyu,YANG Xiu.Research on meteorological disaster prediction and early warning model based on sparse autoencoder technology in novel power system[J].Electrical Measurement & Instrumentation,2026,63(7):2-14.

基于稀疏自编码器技术的电网气象灾害预测预警在新型电力系统中的研究

Research on meteorological disaster prediction and early warning model based on sparse autoencoder technology in novel power system

DOI：10.19753/ j.issn1001-1390.2026.07.001

中文关键词: 新型电力系统电网故障特征提取堆叠自动编码器支持向量机预测预警模型

英文关键词:novel power system, power grid fault, feature extraction, stacked autoencoder, support vector machine, predicting and warning model

基金项目:国家自然科学基金资助项目（52177098）；国家电网有限公司华东分部科技项目（529924240017）

作者中文名	作者英文名	单位
方圆圻	FANG Yuanqi	国家电网有限公司华东分部, 上海 200120
任茂鑫	REN Maoxin	国家电网有限公司华东分部, 上海 200120
葛乃成	GE Naicheng	国家电网有限公司华东分部, 上海 200120
李昊雨	LI Haoyu	上海电力大学电气工程学院, 上海 200090
杨秀	YANG Xiu	上海电力大学电气工程学院, 上海 200090

摘要点击次数: 187

中文摘要:

自然灾害和气候因素是造成新型电力系统电网非计划停运的主要责任原因。随着高比例可再生能源接入和电网数字化升级,新型电力系统对气象扰动的敏感性显著增强,电网防灾减灾更需重点关注气象致灾。针对气象因素与电网故障的关联关系和规律,文中提出了一种基于气象稀疏自编码器(sparse autoencoder, SAE)和支持向量机(support vector machine, SVM)的电网故障预警方法。通过稀疏自编码器和 SVM的结合,显著提高了含高比例电力电子设备(power electronic devices, PED)的电网故障预警的灵敏度和抗干扰能力。基于新型电力系统典型场景下的气象历史数据和电网运维检修数据,通过应用合成少数类样本过采样技术(synthetic minority over-sampling technique, SMOTE)缓解数据集的不平衡问题,并利用最小信息熵原理确定气象因子的初始权重。通过反向传播算法微调 SAE 提取气象特征,预测气象灾害类型,结合SVM建立适应新型电力系统柔性运行需求的电网故障预警模型,完成气象场景与新型电网架构故障类型之间的关联预测。算例分析表明,该方法可准确实现气象灾害和故障类型的预测预警,为新型电力系统韧性提升提供技术支撑。

英文摘要:

Natural disasters and climatic factors are the main reasons for the unplanned outages of the novel power system. With the high proportion of renewable energy access and the digital upgrade of power grids, the sensitivity of novel power system to meteorological disturbances has increased. Greater attention should be paid to meteorological disasters disaster of prevention and mitigation in power grid. In view of the correlation and patterns between meteorological factors and power grid faults, this study introduces a power grid fault early warning approach utilizing a meteorological sparse autoencoder (SAE) combined with support vector machine (SVM) technology. The method is developed by analyzing the relationship and patterns between meteorological conditions and power grid failures through the combination of sparse autoencoder and SVM, the sensitivity and anti-interference ability of power grid fault warning with a high proportion of power electronic devices (PED) are significantly improved. To address the dataset imbalance in historical meteorological and grid operation data under typical scenarios of modern power systems, this study employs the synthetic minority over-sampling technique (SMOTE) for data balancing. The initial weights of meteorological factors are optimized based on minimum information entropy principles. A stacked SAE, fine-tuned via backpropagation, is utilized to extract critical meteorological features and predict disaster types. By integrating SVM, an adaptive fault early warning model is developed to align with the flexible operational demands of contemporary power grids, enabling accurate correlation analysis between meteorological conditions and fault patterns in advanced grid architectures. Case studies demonstrate the effectiveness of the proposed model in disaster prediction and fault-type identification, offering actionable insights for enhancing the resilience of novel power systems.

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