Scalable Non-invasive Radiometric Wireless Sensor Network for Partial Discharge Monitoring in the Future Smart Grid

用于未来智能电网局部放电监测的可扩展非侵入式辐射无线传感器网络

• 批准号: EP/J015873/1
• 负责人: Ian Glover
• 金额: $ 71.38万
• 依托单位: University of Huddersfield
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FJ015873%2F1
• 关键词: Scalable; Non; invasive; Radiometric; Wireless

Partial discharge (PD) refers to an electrical spark that does not completely bridge the space between the conductors causing it. It occurs in degraded electrical insulation and its occurrence is known to be characteristic of insulation defects in the high-voltage (HV) components (transformers, switchgear, cables, etc) of the electrical grid. Such PD results in the radiation of short pulses of electromagnetic energy extending over a wide band of radio frequencies. The detection and location of such radiated signals and, in particular, the careful tracking of changes in their intensity can thus be used to monitor the health of HV equipment. There is an immediate economic case for ubiquitous and continuous monitoring of PD intensity throughout the power system to realise an early warning system for equipment failure. This case rests on the fact that by monitoring radiated PD signals: (i) a compromised item of plant item can be de-rated or replaced to avoid catastrophic failure by rerouting network energy flows, (ii) routine maintenance can be replaced with condition- or risk-based maintenance, and (iii) de-rating or replacement of aging plant can be postponed until demonstrably necessary. One component of this project is to develop and deploy a network of free-standing, non-invasive, wireless sensors that will use these signals to cooperatively locate sources of PD and monitor their evolution. The resulting space-time map of changing PD intensity will provide system operators with a real-time picture of equipment health.The radiometer network described above will map gross PD intensity and provide a simple, but robust, early warning of plant failure. The detailed character of a PD signal (its time waveform, frequency spectrum and statistical behaviour) carries more detailed information about the nature of the insulation defect producing it than PD intensity alone. This has been demonstrated in the context of invasive sensors requiring contact with plant items. The relationship between the nature of insulation defects and the character of the resulting PD signal received by free-standing sensors is, however, currently obscure. A second component of this project is to investigate this relationship by developing and deploying a smaller number of specially designed radio receivers capable of extracting a broad range of signal characteristics along with the radiometers (intensity sensors) described above. Over the course of the project, as insulation defects are diagnosed by the power system operator in the normal way (including forensic examination of failed items of plant), the nature of specific insulation defects will be correlated with the characteristics of the observed PD signal. A programme of opportunistic measurements of PD signals obtained by transporting a free-standing portable PD receiver to any substation identified as having a significant insulation defect or PD source will accelerate the collection of fault-specific PD data. Automatic signal processing and data analysis routines will be developed and used to identify those signal characteristics providing the best discrimination between insulation defect types. Spatially resolved, real-time, information about the health status of the grid, including information about the nature and severity of incipient faults, raises the possibility of the self-diagnosing (and ultimately self-healing) grid.As an integrated part of the future 'smart grid', ubiquitous and continuous PD monitoring will allow routing of energy to be dynamically optimised to minimise any selected cost metric. Such a cost metric might, for example, include the monetary and environmental cost of transmission losses (including CO2 emissions), the cost of maintenance and/or replacement of plant, and the cost (economic and social) of supply interruptions. This project is thus an important component in the vision of the self-optimising grid.

局部放电（PD）指的是没有完全桥接导体之间空间的电火花。它发生在电气绝缘退化的情况下，并且已知其发生是电网高压（HV）部件（变压器、开关设备、电缆等）绝缘缺陷的特征。这种PD导致电磁能量的短脉冲辐射在宽的无线电频率带上延伸。因此，这种辐射信号的检测和定位，特别是对其强度变化的仔细跟踪，可以用于监测高压设备的健康状况。有一个直接的经济情况下，无处不在的和连续的监测PD强度在整个电力系统，以实现设备故障的早期预警系统。这种情况取决于这样的事实，即通过监测辐射的PD信号：（i）可以通过重新路由网络能量流来降低或更换受损的设备项目，以避免灾难性故障，（ii）可以用基于条件或风险的维护来取代日常维护，以及（iii）可以推迟降低或更换老化设备，直到证明有必要。该项目的一个组成部分是开发和部署一个独立的、非侵入性的无线传感器网络，该网络将使用这些信号来合作定位PD源并监测其演变。由此产生的局部放电强度变化的时空图将为系统操作员提供设备健康的实时画面。上述辐射计网络将绘制总局部放电强度，并提供简单但可靠的电厂故障早期预警。局部放电信号的详细特征（其时间波形、频谱和统计行为）比单独的局部放电强度携带了关于产生局部放电信号的绝缘缺陷的性质的更详细的信息。这已经在需要与植物物品接触的侵入式传感器的背景下得到证明。然而，绝缘缺陷的性质和由独立式传感器接收的所得PD信号的特性之间的关系目前是模糊的。该项目的第二个组成部分是通过开发和部署少量专门设计的无线电接收器来研究这种关系，这些接收器能够与上述辐射计（强度传感器）一起沿着提取广泛的信号特征。在项目过程中，由于电力系统运营商以正常方式（包括对设备故障项目的法医检查）诊断绝缘缺陷，因此特定绝缘缺陷的性质将与观察到的PD信号的特征相关。通过将独立便携式PD接收器运送到被识别为具有显著绝缘缺陷或PD源的任何变电站获得的PD信号的机会性测量的方案将加速故障特定PD数据的收集。将开发自动信号处理和数据分析程序，并用于识别那些提供绝缘缺陷类型之间最佳区分的信号特征。空间分辨的，实时的，关于电网的健康状态的信息，包括关于初始故障的性质和严重性的信息，提高了自我诊断（并最终自我修复）grid.As未来的“智能电网”的一个组成部分，无处不在的和连续的局部放电监测的可能性将允许能量的路由被动态优化，以最大限度地减少任何选定的成本指标。例如，这种成本度量可以包括传输损失（包括CO2排放）的货币和环境成本、设备的维护和/或更换成本以及供应中断的成本（经济和社会）。因此，该项目是自我优化电网愿景的重要组成部分。

项目成果

Comparative study of Partial Discharge emulators for the calibration of Free-Space radiometric measurements

用于校准自由空间辐射测量的局部放电模拟器的比较研究

• DOI: 10.1109/iconac.2016.7604938
• 发表时间: 2016
• 作者: Jaber A

Radio location of partial discharge sources: a support vector regression approach

• DOI: 10.1049/iet-smt.2017.0175
• 发表时间: 2018-03-01
• 期刊: IET SCIENCE MEASUREMENT & TECHNOLOGY
• 影响因子: 1.4
• 作者: Iorkyase, Ephraim T.;Tachtatzis, Christos;Atkinson, Robert C.
• 通讯作者: Atkinson, Robert C.

Handbook of Mems for Wireless and Mobile Applications

无线和移动应用MEMS手册

• DOI: 10.1533/9780857098610.1.30
• 发表时间: 2013
• 作者: Glover I

Improving RF-Based Partial Discharge Localization via Machine Learning Ensemble Method

• DOI: 10.1109/tpwrd.2019.2907154
• 发表时间: 2019-08
• 期刊: IEEE Transactions on Power Delivery
• 影响因子: 4.4
• 作者: E. Iorkyase;C. Tachtatzis;I. Glover;P. Lazaridis;D. Upton;B. Saeed;R. Atkinson

An envelope detector as a trading cost technique for radiometric partial discharge detection

包络检测器作为辐射局部放电检测的交易成本技术

• DOI: 10.1109/i2mtc.2014.6861013
• 发表时间: 2014
• 作者: De Souza Neto J