Online Electromagnetic Condition Monitoring Techniques for High Voltage Systems

高压系统在线电磁状态监测技术

• 批准号: RGPIN-2017-05488
• 负责人: Kordi, Behzad
• 金额: $ 2.7万
• 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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=655897
• 关键词: Online; Electromagnetic; Condition; Monitoring; Techniques

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在很大程度上取决于绝缘系统，该绝缘系统可以安全地将通电的电气部件与地面以及彼此隔离。绝缘劣化和击穿是电力系统设备故障的主要根本原因。现有电力系统的老化绝缘在高电压下运行，并且现在处于比它们设计承受的更高水平的应力下。此外，新兴的可再生电力源，例如风能和太阳能，使用引入高频电压的电力电子设备，这加速了电绝缘的老化。这些受压和老化的电绝缘子增加了设备突然故障、中断和干扰的风险。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停电。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中的缺陷和空隙内发生的小的局部电击穿。PD的存在表明，材料降解正在加速走向灾难性故障，如果监测PD活动，则可以避免这种情况。现有的电网没有足够或有效的状态监测和诊断其电气绝缘系统。拟议的研究计划的重点是开发在线，自主和智能，PD收集和分析系统。与我的HQP一起，我们将开发新的PD传感硬件，并建立其表征的时域技术。将开发能够模拟局部放电传播的各种电力系统部件（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。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确定放电活动的原因，这将是资产管理人员在基于状态的维护方面进行风险管理和决策的一个重要因素。开发新的和改进的高压电力系统在线状态监测技术将有助于最大限度地减少因设备突然故障而造成的停电和中断，从而改善加拿大的电力安全。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部门。本研究的10名HQP学员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。