摘要
由于配电网具有拓扑结构复杂、线路分支较多、空间分布密集等特性,潜在运行扰动及故障难以完全避免,故所配备的保护系统势必确保较高水平的可靠性及稳定性。因此,针对配网保护系统潜在异常运行状态的监测与识别面临新的挑战。为此,提出一种基于数据驱动的运行异常状态实时检测模型。首先,采用核函数主成分分析(kernel principal components analysis,KPCA)流程,针对原始数据实施维度压缩,能够在高维数据环境下降低后续模型的运算复杂度;其次,应用孤立森林(isolated forest,IF)模型,依据各正常运行状态取值范围,挖掘潜在离群样本点,能够在数据呈偏置或稀疏分布环境下保持较高的检测性能,针对异常状况进行快速反应;最后,以某地区配电网继保系统运行数据作为仿真实例,实验结果验证所提出模型在实际应用中较高的异常检测水平,能够助力配网安全风险的自动识别和应对。
Due to the complex topology,multiple line branches,and dense spatial distributions of distribution networks,the potential disturbances and failures cannot be eliminated.Thus,a protection system is required to ensure a high level of both reliability and stability.In that case,new challenges in the monitoring and identification of these potential abnormal operation statues must be worked out.To this end,a data-driven-based real-time anomaly detection model is proposed in this paper.To start with,the kernel principal components analysis(KPCA)process is deployed to compress the dimensionality of input data,which can reduce the computational complexity within such high-dimensional data environments.Next,the isolated forest(IF)model is applied to excavate potential outliers according to the numeric range of normal operating states of each feature.Thus,the IF can maintain a high detection performance in the biased or sparse distributions,and react swiftly to those outliers.Finally,the operation data of a relay system in one regional distribution network are utilized in the case study.The results verify the better performance of the proposed model in practical applications,and therefore can be utilized to assist in the automatic identification and response of the risks of distribution networks.
作者
徐军
齐蓬勃
李凡
王国荣
弋富国
XU Jun;QI Pengbo;LI Fan;WANG Guorong;YI Fuguo(Guyuan power supply company,State Grid Ningxia Electric Power Co.,Ltd.,Guyuan 750003,China)
出处
《电力科学与技术学报》
CAS
CSCD
北大核心
2024年第3期31-37,共7页
Journal of Electric Power Science And Technology
基金
国网宁夏电力有限公司科技研发项目(5229GY2000FL)。
