Online Electromagnetic Condition Monitoring Techniques for High Voltage Systems

高压系统在线电磁状态监测技术

基本信息

批准号：
RGPIN-2017-05488
负责人：
Kordi, Behzad
金额：
$ 5.39万
依托单位：
University of Manitoba
依托单位国家：
加拿大
项目类别：
Discovery Grants Program - Individual
财政年份：
2022
资助国家：
加拿大
起止时间：
2022-01-01 至 2023-12-31
项目状态：
已结题

来源：
https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752155
关键词：
Online Electromagnetic Condition Monitoring Techniques

项目摘要

Reliable operation of electric power systems is highly dependent on an insulation system that can safely isolate energized electrical components from ground and from each other. Insulation degradation and breakdown is a major root cause of the failure of electric power system equipment. The aged insulation of existing electric power systems operate at high voltages and are now under higher levels of stress than they were designed to tolerate. In addition, emerging renewable electric power sources, e.g. wind and solar energies, use power electronics that introduce high frequency voltages that accelerate the aging of the electric insulation. These stressed and aging electric insulators increase the risk of sudden equipment failure, outage, and disturbance.Condition monitoring and diagnostics of power systems insulation are required to minimize failures and outages. A key to condition monitoring is the detection of partial discharges (PD): small, localized electrical breakdowns that occur within imperfections and voids in solid and liquid insulation materials and in insulating gases. The presence of PDs indicates that material degradation is accelerating towards a catastrophic failure, which can be avoided if PD activities are monitored. The existing electric power grid does not have sufficient or effective condition monitoring and diagnostics for its electrical insulation system.The proposed research program is focused on the development of online, autonomous, and smart, PD collection and analysis systems. Together with my HQP, we will develop novel PD sensing hardware and establish time-domain techniques for their characterization. Accurate simulation models for various power system components (such as transformers, transmission lines and cables, and gas-insulated switch gears) will be developed that are capable of simulating the propagation of PD. We will develop PD analysis algorithms based on machine learning techniques for online condition monitoring and diagnostics of high voltage insulation systems that will form the core of a smart PD analysis system. The developed techniques will be capable of identifying the cause of discharge activities that will be an important factor in risk management and decision-making for asset managers with regards to condition-based maintenance.The development of new and improved techniques for online condition monitoring of high voltage power systems will improve Canadian power security by contributing to the minimization of power outages and disruptions due to sudden equipment failure. These techniques will be essential to the development of a smart grid for future transmission and distribution of electric power, and will support this rapidly growing segment of the economy. The team of 10 HQP trainees of this research will have advanced multidisciplinary training and will be a key support for the future Canadian power system industry.

电力系统的可靠运行高度依赖于能够安全地将带电电气元件与地面以及彼此之间隔离的绝缘系统。绝缘劣化和击穿是电力系统设备发生故障的一个重要根源。现有电力系统老化的绝缘体在高电压下运行，现在承受的应力水平高于其设计承受的水平。此外，新兴的可再生电力来源，例如风能和太阳能，使用电力电子设备引入高频电压，加速电绝缘体的老化。这些受压和老化的电绝缘体增加了设备突然故障、断电和干扰的风险。需要对电力系统绝缘进行状态监测和诊断，以最大限度地减少故障和断电。状态监测的关键是检测局部放电 (PD)：在固体和液体绝缘材料以及绝缘气体的缺陷和空隙内发生的小型局部电气击穿。局放的存在表明材料退化正在加速，导致灾难性故障，如果监测局放活动，则可以避免这种情况。现有的电网对其电气绝缘系统没有足够或有效的状态监测和诊断。所提出的研究计划重点是开发在线、自主、智能的局部放电采集和分析系统。我们将与我的总部一起开发新型局部放电传感硬件，并建立用于表征的时域技术。将开发各种电力系统组件（例如变压器、输电线路和电缆以及气体绝缘开关设备）的精确仿真模型，能够模拟局部放电的传播。我们将开发基于机器学习技术的局部放电分析算法，用于高压绝缘系统的在线状态监测和诊断，这将构成智能局部放电分析系统的核心。所开发的技术将能够识别排放活动的原因，这将成为资产管理者基于状态的维护进行风险管理和决策的重要因素。开发用于高压在线状态监测的新技术和改进技术电压电力系统将有助于最大限度地减少设备突然故障造成的停电和中断，从而提高加拿大的电力安全。这些技术对于未来电力传输和分配的智能电网的发展至关重要，并将支持这一快速增长的经济领域。该研究的10名HQP学员团队将接受先进的多学科培训，并将成为未来加拿大电力系统行业的关键支持。