| 引用本文: | 闫梦秋,杨轶俊,赵 舫.基于改进OCSVM的智能变电站数据流异常检测方法研究[J].电力系统保护与控制,2022,50(6):100-106.[点击复制] |
| YAN Mengqiu,YANG Yijun,ZHAO Fang.A data stream anomaly detection method based on an improved OCSVM smart substation[J].Power System Protection and Control,2022,50(6):100-106[点击复制] |
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| 摘要: |
| 目前智能变电站的数据流异常检测对准确性和实时性要求较高，采用简单阈值的检测方法已无法满足要求。针对这一问题，基于智能变电站体系架构，提出了一种将改进的密度聚类算法和改进的单类支持向量机算法相结合用于智能变电站异常数据流检测的方法。使用k-dist图优化密度聚类算法对正常数据流样本进行聚类，形成样本簇。使用改进的粒子群算法优化单类支持向量机算法建立相应的检测模型，对异常数据流进行检测。通过仿真与传统检测方法进行对比分析，验证了所提方法的有效性。结果表明，与传统OCSVM方法相比，所提异常检测方法将常规数据流样本拆分为多个OCSVM模型，可以更紧密地包裹正常样本，检测效果较为理想，检测准确率高于99%，可以满足异常数据检测对准确性和实时性的要求。 |
| 关键词: 智能变电站 通信网络异常 数据流 密度聚类算法 单类支持向量机算法 |
| DOI：DOI: 10.19783/j.cnki.pspc.210946 |
| 投稿时间：2021-07-22修订日期：2021-11-11 |
| 基金项目:南方电网公司科技项目资助(0002200000072652)；国家重点研发计划资助(2017YFB0903100) |
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| A data stream anomaly detection method based on an improved OCSVM smart substation |
| YAN Mengqiu,YANG Yijun,ZHAO Fang |
| (1. Shenzhen Power Supply Co., Ltd., Shenzhen 518000, China; 2. China Southern Power Grid Shenzhen Digital Grid
Research Institute Co., Ltd., Shenzhen 518000, China; 3. College of Electrical Engineering,
Zhejiang University, Hangzhou 310027, China) |
| Abstract: |
| At present, data flow anomaly detection for an intelligent substation requires high accuracy and real-time, and the detection method of a simple threshold cannot meet the requirements. To solve this problem, based on the architecture of an intelligent substation, a method combining an improved density clustering algorithm and an improved single class support vector machine algorithm for abnormal data flow detection in intelligent substation is proposed. The k-dist graph optimized density clustering algorithm is used to cluster normal data stream samples to form sample clusters. An improved particle swarm optimization algorithm is used to optimize the single class support vector machine algorithm, and the corresponding detection model is established to detect abnormal data flow. The effectiveness of the proposed method is verified by comparing the simulation with the traditional detection method. The results show that compared with the traditional OCSVM method, the proposed anomaly detection method divides the conventional data stream samples into multiple OCSVM models, which can wrap the normal samples more closely. The detection effect is ideal, and the detection accuracy is higher than 99%, which can meet the requirements of accuracy and real-time for anomaly data detection.
This work is supported by the Science and Technology Project of China Southern Power Grid Co., Ltd. (No. 0002200000072652). |
| Key words: intelligent substation abnormal communication network data flow density clustering algorithm one-class support vector machine algorithm |
