A review on application of machine learning-based methods for power system inertia monitoring
Mahdi Heidari, Lei Ding, Mostafa Kheshti, Weiyu Bao, Xiaowei Zhao, Marjan Popov, Vladimir Terzija
Abstract
• Presentation of system inertia definitions and approaches for its monitoring in both traditional and modern power systems. • Investigation of ML-based methods used for inertia estimation. • Comprehensive and comparative review of system inertia monitoring using ML-based methods. • Review of academic and industrial projects related to system inertia monitoring using ML-based methods. • Review of the literature on applications involving ML-based methods and associated inertia monitoring. The modernization of electrical power systems is reflected through the integration of renewable energy resources, with the ultimate aim of creating a carbon–neutral world. However, this goal has brought new and complex challenges for the power system, with one of the most crucial issues which is the reduction of system inertia. The decrease in system inertia has led to severe difficulties in maintaining frequency stability. As a result, power system operators must continuously monitor the system inertia and when necessary to activate appropriate preventive measures, ensuring a reliable and secure operation of the power system. Fortunately, wide-area monitoring systems can provide the necessary measurements to monitor and analyze system behavior, assisting system operators in undertaking optimal actions. This paper provides a review of recent publications that apply machine learning (ML)-based methods for monitoring power system inertia. It also provides an overview of academic and industrial projects related to ML-based methods for inertia monitoring. Furthermore, the paper explores applications based on ML-based methods and inertia. Lastly, the paper briefly discusses future directions for the development of this research field. © 2017 Elsevier Inc. All rights reserved.