Power Systems Dynamic Security Assessment using machine learning.

使用机器学习的电力系统动态安全评估。

• 批准号: 2118623
• 金额: --
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2118623
• 关键词: Power; Systems; Dynamic; Security; Assessment

Most countries, including the UK, are promoting the increased penetration of renewable energy sources (RES) to reduce carbon emissions. Ambitious targets are being set for the years to come, mainly because of climate change but also for economic and technical reasons. The generation mix is changing with many large, thermal power stations closing or operating with lower output and more power coming from sources connected via power electronic converters such as wind generators and Photo-Voltaics, raising questions about system security. These changes lead to significantly different dynamic behaviour of the power system that may vary in a both temporal and spatial manner due to the intermittent nature of many of the power electronic interfaced sources. Both the possible pre-disturbance operating conditions and the post-disturbance behaviour of power systems can be significantly affected. These developments, may lead to operation closer to the stability limit and increase the risk of widespread events, that might even lead to blackouts if not acted upon.With the extensive installation of measurement devices in modern power systems, abundant data are available that hold valuable information about potential patterns of instability. Measurement data can also help in the fast prediction of imminent cascading events and enable automated control actions, all faster than with human operators.This PhD project will deal with Dynamic Security Assessment (DSA) which focuses on the security of system dynamics in various timescales, from a few up to several seconds. DSA usually requires performing computationally intensive time domain simulations (especially for large power systems). This fact coupled with the increasing temporal and spatial variation introduced by RES as well as the huge number of possible combinations of equipment failure renders the challenge of identifying and predicting situations that might lead to cascading failures highly complex, and calls for the need of novel tools and methodologies.Data analytics and advances in artificial intelligence (AI) and deep learning have made huge steps in recent years, providing powerful tools that can model the behaviour of complex, highly non-linear systems, such as power systems, with very high accuracy.

包括英国在内的大多数国家都在推动可再生能源（RES）的普及，以减少碳排放。各国正在为未来几年设定雄心勃勃的目标，这主要是由于气候变化，但也有经济和技术方面的原因。发电结构正在发生变化，许多大型火力发电站关闭或运行时输出较低，更多的电力来自通过电力电子转换器（如风力发电机和光伏）连接的电源，这引发了对系统安全性的质疑。这些变化导致电力系统的动态行为显著不同，由于许多电力电子接口源的间歇性，这些动态行为可能在时间和空间上都有所不同。电力系统可能的扰动前运行状态和扰动后的行为都会受到显著影响。这些发展可能导致运行更接近稳定极限，并增加大范围事件的风险，如果不采取行动，甚至可能导致停电。随着测量设备在现代电力系统中的广泛安装，大量的数据可用于提供潜在不稳定模式的有价值的信息。测量数据还可以帮助快速预测即将发生的级联事件，并实现自动控制操作，所有这些都比人工操作更快。这个博士项目将处理动态安全评估（DSA），侧重于不同时间尺度（从几秒到几秒）的系统动力学安全性。DSA通常需要执行计算密集的时域模拟（特别是对于大型电力系统）。这一事实，再加上RES带来的越来越多的时空变化，以及大量可能的设备故障组合，使得识别和预测可能导致级联故障的情况变得非常复杂，需要新的工具和方法。近年来，数据分析和人工智能（AI）和深度学习的进步取得了巨大进展，提供了强大的工具，可以以非常高的精度对复杂、高度非线性系统（如电力系统）的行为进行建模。