Machine Learning Techniques Applied to On-Line Voltage Stability Assessment: A Review
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ORIGINAL PAPER
Machine Learning Techniques Applied to On‑Line Voltage Stability Assessment: A Review Mohammed Amroune1 Received: 24 March 2019 / Accepted: 30 September 2019 © CIMNE, Barcelona, Spain 2019
Abstract Electric power systems have become larger, more complex and found to be operating close to their stability limits with small security margin. In such situation, fast and accurate assessment of voltage stability is necessary in order to prevent large-scale blackouts. Due to its ability to learn off-line and produce accurate results on-line, machine learning (ML) techniques i.e., artificial neural networks, decision trees, support vector machines, fuzzy logic and adaptive neuro-fuzzy inference system are widely applied for on-line voltage stability assessment. This paper focuses on providing a clear review of the latest ML techniques employed in on-line voltage stability assessment. For each technique, a brief description is first presented and then a detailed review of the finding published research papers discussed the application of this technique in on-line voltage stability assessment is presented. Based on the conducted review, some discussions and limitations of ML techniques are finally presented.
1 Introduction In the last decade, serious power grid blackouts have occurred throughout the world bringing with them important economic losses and affecting the lives of local residents. Voltage instability incidents have been identified as contributing factors in several recent worldwide blackouts such as the large-scale power failure occurred in the Tokyo metropolitan area in 1987 [1]. The blackout incident affected Egypt in April 24, 1990 where 50 million people were affected for 6 h [2]. This blackout was characterized by a fast local voltage collapse, followed by sudden total voltage collapse. Another incident is the blackout affected around 50 million people in eight U.S. states and two Canadian provinces on 14th August 2003. Estimates show that this blackout interrupted around 63 GW of load resulting in an economic loss of approximately 4–6 billion USD. Recently, on 2012 India suffered a severe and large blackout following a voltage collapse due to the overloading of transmission lines. This blackout affected around 670 million people in 22 Indian states [3]. These blackouts have large impacts that are both direct such as the interruption of an activity, function, * Mohammed Amroune mohammed_amroune@univ‑setif.dz 1
Department of Electrical Engineering, University of Ferhat Abbas Setif 1, 19000 Sétif, Algeria
or service that requires electricity and indirect due to the interrupted activities or services. Voltage instability phenomenon is generally associated with a gradual or uncontrollable drop in voltage magnitude after disturbances in the system, increase in load demand or incapacity to cover the demand for reactive power. The voltage collapse is the process by which voltage instability leads to loss of voltage in an important part of the system. When the power system is operating with ins
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