Hailu Engidaw Abel, Nyakoe George Nyauma, Muriithi Christopher Maina
Pan African University Institute for Basic Sciences, Technology, and Innovation, Juja, Kenya.
Jomo Kenyatta University of Agriculture and Technology (JKUAT), Juja, Kenya.
Heliyon. 2023 Mar 14;9(3):e14524. doi: 10.1016/j.heliyon.2023.e14524. eCollection 2023 Mar.
The secure operation of a power system depends on the available security evaluation tools and improvement techniques to tackle the disturbances or contingencies. The main objective of the survey presented in this paper is to provide a comprehensive review to the researchers, academicians, and utility engineers on the available techniques of static security assessment and improvement in modern power systems. Various performance indices are used to express the severity of limit violations from security margins typically in transmission line loading and buses voltage magnitude under a given disturbance or contingency. The accuracy and speed of computation considering uncertainties in renewable energy generation and load demand scenarios are the fundamental requirements of any security assessment tool. Conventional power flow and machine learning approaches are explored and compared for static security assessment. Although, conventional AC power flow provides accurate result, it is computationally demanding and slow process to assess the security of a power system with uncertainties and changing future operating scenarios considering simultaneous component failures. Several machine learning techniques have been studied to make fast and sufficiently accurate assessment. The application of FACTS devices to improve static security of a power system has been reviewed. To ensure the effectiveness of FACTS devices, various sensitivity and optimization approaches have been suggested for proper placement and sizing. The increasing complexity and uncertainty in power systems due to increased penetration of renewable energy resources and the introduction of new type of loads such as electric vehicles and heating loads suggests the development and application of more robust and portable security assessment tools such as deep learning algorithms and fast responding flexible security improvement mechanisms like FACTS devices.
电力系统的安全运行取决于可用的安全评估工具和改进技术，以应对干扰或突发事件。本文所呈现调查的主要目的是，就现代电力系统中静态安全评估和改进的可用技术，向研究人员、学者和电力公司工程师提供全面的综述。各种性能指标用于表示在给定干扰或突发事件下，通常从输电线路负载和母线电压幅值的安全裕度方面违反限值的严重程度。考虑可再生能源发电和负荷需求场景中的不确定性时计算的准确性和速度，是任何安全评估工具的基本要求。本文对传统潮流和机器学习方法进行了探索和比较，以用于静态安全评估。尽管传统交流潮流能提供准确结果，但对于评估具有不确定性以及考虑同时发生元件故障的未来变化运行场景的电力系统安全性而言，其计算要求高且过程缓慢。已研究了多种机器学习技术以进行快速且足够准确的评估。本文还综述了灵活交流输电系统（FACTS）装置在提高电力系统静态安全性方面的应用。为确保FACTS装置的有效性，已提出各种灵敏度和优化方法以进行合理的布置和选型。由于可再生能源资源渗透率的提高以及诸如电动汽车和供热负荷等新型负荷的引入，电力系统中的复杂性和不确定性不断增加，这表明需要开发和应用更强大且便携的安全评估工具，如深度学习算法，以及快速响应的灵活安全改进机制，如FACTS装置。
