Abstract This paper proposes an online monitoring method of metal-oxide arrester (MOA) based on passive RFID sensor tag and mind evolutionary computation (MEC). Firstly, an RFID sensor tag is designed to collect the leakage current of MOA, so as to realize the rapid fault location and equipment life cycle management. The proposed RFID sensor incorporates the sensor data into its ID information. The proposed power management block adopts a new architecture of a low-voltage DC-DC charge pump after a single-stage rectifier circuit. A method of MOA condition monitoring based on MEC is proposed. According to the measured operating voltage and leakage current, the parameters k and α which can reflect the aging condition of MOA are solved by using MEC's better optimization calculation ability, so as to monitor the MOA condition. In addition, the influence of harmonic voltage on the algorithm is analyzed through MATLAB simulation. The results show that the designed sensor tag can effectively measure the leakage current, and the proposed monitoring algorithm can accurately calculate the relevant parameters reflecting the state of MOA. Moreover, under the influence of harmonic voltage, the maximum errors of parameters k and α are 1.8% and 2.0%. Compared with the existing monitoring technology, it has the advantages of low cost, high precision and rapid fault location, which provides a new method and idea for on-line monitoring of MOA.

中文翻译：

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基于RFID传感器和思维进化计算的新型金属氧化物避雷器监测技术

摘要 提出一种基于无源RFID传感器标签和思维进化计算(MEC)的金属氧化物避雷器(MOA)在线监测方法。首先设计了RFID传感器标签，采集MOA的漏电流，实现快速故障定位和设备生命周期管理。提议的 RFID 传感器将传感器数据合并到其 ID 信息中。所提出的电源管理模块在单级整流电路之后采用了低压 DC-DC 电荷泵的新架构。提出了一种基于MEC的MOA状态监测方法。根据测得的工作电压和漏电流，利用MEC较好的优化计算能力求解反映MOA老化状况的参数k和α，从而对MOA状况进行监测。此外，通过MATLAB仿真分析了谐波电压对算法的影响。结果表明，所设计的传感器标签能够有效地测量泄漏电流，所提出的监测算法能够准确计算反映MOA状态的相关参数。而且，在谐波电压的影响下，参数k和α的最大误差分别为1.8%和2.0%。与现有的监测技术相比，它具有成本低、精度高、故障定位快速等优点，为MOA的在线监测提供了一种新的方法和思路。提出的监测算法能够准确计算反映MOA状态的相关参数。而且，在谐波电压的影响下，参数k和α的最大误差分别为1.8%和2.0%。与现有的监测技术相比，它具有成本低、精度高、故障定位快速等优点，为MOA的在线监测提供了一种新的方法和思路。提出的监测算法能够准确计算反映MOA状态的相关参数。而且，在谐波电压的影响下，参数k和α的最大误差分别为1.8%和2.0%。与现有的监测技术相比，它具有成本低、精度高、故障定位快速等优点，为MOA的在线监测提供了一种新的方法和思路。