Online Electromagnetic Condition Monitoring Techniques for High Voltage Systems

电力系统的可靠运行高度依赖于一个绝缘系统，该系统可以安全地将带电的电气部件与地面和彼此隔离。绝缘劣化和击穿是电力系统设备故障的主要根源。现有电力系统老化的绝缘在高压下运行，现在承受的压力水平高于设计承受的水平。此外，新兴的可再生能源，如风能和太阳能，使用引入高频电压的电力电子设备，加速电气绝缘的老化。这些紧张和老化的绝缘子增加了设备突然故障、停电和干扰的风险。*电力系统绝缘的状态监测和诊断是必要的，以最大限度地减少故障和停电。状态监测的一个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洞中发生的微小的局部电气故障。PD的存在表明材料正在加速降解，走向灾难性的故障，如果监测PD的活动，这是可以避免的。现有电网对其电气绝缘系统缺乏充分或有效的状态监测和诊断。*拟议的研究计划侧重于开发在线、自主和智能的局部放电采集和分析系统。与我的HQP一起，我们将开发新的局部放电检测硬件，并建立表征它们的时域技术。将开发能够模拟局部放电传播的各种电力系统元件(如变压器、输电线路和电缆以及气体绝缘开关装置)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。开发的技术将能够识别放电活动的原因，这将是资产管理公司在基于状态的维护方面进行风险管理和决策的重要因素。*高压电力系统在线状态监测的新技术和改进技术的开发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，从而提高加拿大的电力安全。这些技术将对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分。本次研究的10名HQP学员团队将接受多学科的高级培训，将成为未来加拿大电力系统行业的关键支撑。

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.