Power Systems Dynamic Security Assessment using machine learning.

包括英国在内的大多数国家都在促进可再生能源（RES）的普及，以减少碳排放。目前正在为今后几年制定雄心勃勃的目标，主要是因为气候变化，但也有经济和技术原因。发电组合正在发生变化，许多大型火力发电站关闭或以较低的输出运行，更多的电力来自通过电力电子转换器连接的电源，如风力发电机和光伏发电机，引发了有关系统安全的问题。这些变化导致电力系统的显著不同的动态行为，由于许多电力电子接口源的间歇性质，这些动态行为可以在时间和空间上都变化。可能的扰动前的操作条件和电力系统的扰动后的行为可以显着影响。这些发展可能导致运行接近稳定极限，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。随着现代电力系统中测量设备的广泛安装，可以获得大量数据，这些数据包含有关潜在不稳定模式的有价值信息。测量数据还可以帮助快速预测即将发生的级联事件，并实现自动化控制操作，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），重点关注从几秒到几秒的各种时间尺度的系统动态安全。DSA通常需要执行计算密集型时域仿真（特别是对于大型电力系统）。这一事实加上RES带来的时间和空间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连锁故障的情况变得非常复杂，并需要新的工具和方法。数据分析和人工智能（AI）和深度学习的进步近年来取得了巨大进展，提供了强大的工具，可以模拟复杂的行为，高度非线性系统，如电力系统，具有非常高的精度。

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.