引用本文: | 刘 畅,郑 涛,王志华,等.基于DBSCAN的智能变电站交流采样异常实时识别算法[J].电力系统保护与控制,2024,52(24):140-148.[点击复制] |
LIU Chang,ZHENG Tao,WANG Zhihua,et al.Algorithm for real-time identification of sampling abnormalities in smart substations based on DBSCAN[J].Power System Protection and Control,2024,52(24):140-148[点击复制] |
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摘要: |
新一代智能变电站使用采集执行单元统一采样,后续设备都将依赖此装置数据,因此该装置采样环节产生的异常数据会影响变电站保护、测控等多个系统的正常运行。如何高效地识别这些异常数据是电力系统采样中至关重要的部分,也是智能变电站安全性和稳定性的基础。传统的异常数据检测方法主要设计用于处理低采样率场景下偶尔出现的异常采样点。随着智能变电站采样率的提高和电磁干扰问题的加剧,采样时连续多个采样点同时异常成为普遍现象。这使得原有识别算法准确率降低,而未被识别的异常数据可能会造成后续测控装置精度降低甚至保护装置误动。针对传统检测方法的不足,提出基于密度的噪声空间聚类(density-based spatial clustering of applications with noise, DBSCAN)算法的交流采样异常数据识别实时算法。该算法利用异常数据与正常数据的空间密度差异,有效区分出密度较低的异常采样点,从而实现智能变电站异常采样数据的识别。相比于传统方法,所提方法在采样异常数据识别上具有更准确的结果。 |
关键词: 智能变电站 采样 异常数据 DBSCAN算法 |
DOI:10.19783/j.cnki.pspc.240203 |
投稿时间:2024-02-26修订日期:2024-04-16 |
基金项目:国家重点研发计划项目资助(2021YFB2401000) |
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Algorithm for real-time identification of sampling abnormalities in smart substations based on DBSCAN |
LIU Chang1,ZHENG Tao1,WANG Zhihua2,YANG Qianqian1 |
(1.School of Electrical & Electronic Engineering, North China Electric Power University, Beijing 102206, China;
2. Beijing SiFang Jibao Engineering Technology Co., Ltd., Beijing 100085, China) |
Abstract: |
The new generation of smart substations uses the acquisition and execution unit for unified sampling. Subsequent equipment will rely on the data from this device. Therefore, the abnormal data generated during the sampling process of the device will affect the normal operation of multiple systems such as substation protection and measurement and control. How to efficiently identify these abnormal data is a crucial part of power system sampling and the basis for the safety and stability of smart substations. Traditional anomaly data detection methods are mainly designed to deal with occasional anomalous sampling points in low sampling rate scenarios. As the sampling rate of intelligent substations increases and the electromagnetic interference problem intensifies, it has become a common phenomenon that multiple sampling points are abnormal at the same time during sampling. This makes the original recognition algorithm less accurate, and the unrecognized anomalous data may cause the accuracy of the subsequent measurement and control devices to be reduced or even the protection devices to be inadvertently activated. Given the shortcomings of traditional detection methods, a real-time algorithm for identifying abnormal data of AC sampling based on density-based spatial clustering of applications with noise (DBSCAN) algorithm is proposed. The algorithm makes use of the spatial density difference between abnormal and normal data to effectively distinguish the abnormal sampling points with lower density, so as to realize the identification of abnormal sampling data in intelligent substations. Compared with the traditional method, the method proposed has more accurate results in sampling abnormal data identification. |
Key words: smart substation sampling abnormal data DBSCAN algorithm |