摘要
为进一步提高电力设备异常检测方法对设备信息的利用率,发现更多潜在的设备故障,结合大数据分析技术和设备评估技术,提出了一种基于时间序列和神经网络的状态数据异常检测方法。通过时间序列自回归模型和自组织映射神经网络将连续的电力设备数据离散为单个序列,计算状态变量在时间轴上的转移概率,通过状态转移概率和聚类算法快速检测数据异常。通过实验对该方法的有效性进行验证。结果表明,该方法可以快速、有效地检测电力设备异常状态。
Due to the low utilization of equipment information,the existing abnormal detection methods for power equipment are difficult to find potential equipment faults.Combined with big data analysis technology and equipment evaluation technology,a state data anomaly detection method based on time series and neural network is proposed.The auto-regressive time series model and self-organized mapping neural network are used to discretize the continuous power equipment data into a single sequence,and the transition probability of the state variable on the time axis is calculated,which can quickly detect data anomalies through state transition probability and clustering algorithms.The effectiveness of the proposed method is verified by experiments.The results show that this method can detect the abnormal state of power equipment quickly and effectively.
作者
丁江桥
文屹
吕黔苏
张迅
范强
黄军凯
DING Jiangqiao;WEN Yi;LV Qiansu;ZHANG Xun;FAN Qiang;HUANG Junkai(Electric Power Research Institute of Guizhou Power Grid Co.,Ltd.,Guiyang 550002,China)
出处
《电测与仪表》
北大核心
2024年第2期185-190,共6页
Electrical Measurement & Instrumentation
基金
南方电网有限公司科技项目(066600GS62180038)。
关键词
电力设备
时间序列自回归模型
自组织映射神经网络
转移概率
异常检测
power equipment
auto-regressive time series model
self-organized mapping neural network
transfer probability
anomaly detection
