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In Heliyon

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.

Hailu Engidaw Abel, Nyakoe George Nyauma, Muriithi Christopher Maina


FACTS device, Machine learning, Optimal allocation, Performance index, Power system uncertainties, Static security assessment, Static security improvement