Online Electromagnetic Condition Monitoring Techniques for High Voltage Systems

电力系统的可靠运行高度依赖于绝缘系统，该绝缘系统能够安全地将带电的电气元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。现有电力系统的老化绝缘在高电压下运行，现在承受的应力水平高于它们的设计承受能力。此外，新兴的可再生电力来源，如风能和太阳能，使用电力电子，引入高频电压，加速电绝缘的老化。这些应力和老化的电绝缘子增加了设备突然故障，停电和干扰的风险。******需要对电力系统绝缘进行状态监测和诊断，以尽量减少故障和中断。状态监测的关键是局部放电（PD）的检测：在固体和液体绝缘材料以及绝缘气体的缺陷和空隙中发生的小的局部电气故障。PD的存在表明材料的降解正在加速走向灾难性的失败，如果监测PD的活动，就可以避免这种失败。现有电网对电绝缘系统的状态监测和诊断不够充分和有效。******拟议的研究计划侧重于开发在线、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，并建立表征它们的时域技术。将开发能够模拟PD传播的各种电力系统组件（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。开发的技术将能够识别排放活动的原因，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。******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。这项研究的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工业的关键支持。

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.