| 引用本文: | 肖 飞,叶 康,邓祥力,魏聪聪,柯 杨.基于最优编码集及智能状态估计的电网故障诊断方法[J].电力系统保护与控制,2021,49(2):89-97.[点击复制] |
| XIAO Fei,YE Kang,DENG Xiangli,WEI Congcong,KE Yang.A fault diagnosis method of a power grid based on an optimal coding set and intelligent state estimation[J].Power System Protection and Control,2021,49(2):89-97[点击复制] |
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| 摘要: |
| 当电网发生故障时，大量的遥信告警和变位信息上传到调度端，使得调度人员很难在短时间内对故障设备及故障类型做出准确的判断。因此提出了利用分组遥信数据识别故障类型，利用人工智能方法纠正差错遥信的电网故障诊断方法。对于此，将各种设备的标准遥信数据映射到故障诊断空间中，求取最优编码集，把故障遥信的故障空间编码值和故障空间最优编码值进行比较归类，查找故障类型，实现电网的故障诊断。通过不同故障模式的远程变位信号数据，利用站内丢失遥信事件的历史数据样本，训练智能状态估计模型。对遥信误变位或漏传数据进行纠正，实现遥信数据的前端数据纠错，提高故障诊断正确率，最终形成具有纠错能力、适用于大数据平台应用的电网故障智能诊断方法。通过案例仿真验证和实际大数据平台挂网运行，验证了智能状态估计模型和故障诊断模型对电网故障元件诊断的有效性。 |
| 关键词: 故障诊断 大数据 人工智能 设备监控 |
| DOI：DOI: 10.19783/j.cnki.pspc.200079 |
| 投稿时间：2020-01-17修订日期：2020-03-17 |
| 基金项目:国家自然科学基金项目资助(51777119)；国网上海市电力公司科技项目资助(520900180030) |
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| A fault diagnosis method of a power grid based on an optimal coding set and intelligent state estimation |
| XIAO Fei,YE Kang,DENG Xiangli,WEI Congcong,KE Yang |
| (1. State Grid Shanghai Municipal Electric Power Company, Shanghai 200122, China;
2. School of Electric Engineering, Shanghai University of Electric Power, Shanghai 200090, China) |
| Abstract: |
| When a fault occurs in the power grid, a large number of remote signaling alarms and amount of displacement information are uploaded to the dispatching terminal. This makes it difficult for dispatchers to make accurate judgments on the faulty equipment and the type of the fault in a short time. Therefore, this paper proposes a power grid fault diagnosis method that combines classification with fault space optimal coding and intelligent state estimation and error correction of remote signal data. It proposes mapping the remote signal data to the fault diagnosis space, and compares and classifies it with the optimal coding set of the fault space to realize fault diagnosis of the power grid. Through the remote signal displacement data of different failure modes, the intelligent state estimation model is trained to correct the misplaced or missed data of the remote signal to realize the front-end data error correction of the remote signal data, and thus improve the accuracy of fault diagnosis. Finally, an intelligent fault diagnosis method for power grid faults suitable for big data platform applications is formed. The validity of the intelligent state estimation model and fault diagnosis model for fault diagnosis of power grids is verified by case simulation verification and actual big data platform network operation.
This work is supported by the National Natural Science Foundation of China (No. 51777119) and the Science and Technology Project of State Grid Shanghai Electric Power Company (No. 520900180030). |
| Key words: fault diagnosis big data artificial intelligence equipment monitoring |
