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A review of machine learning approaches to power system security and stability

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dc.contributor.author Alimi, OA
dc.contributor.author Ouahada, K
dc.contributor.author Abu-Mahfouz, Adnan MI
dc.date.accessioned 2020-09-21T12:29:23Z
dc.date.available 2020-09-21T12:29:23Z
dc.date.issued 2020-06
dc.identifier.citation Alimi, O.A., Ouahada, K. & Abu-Mahfouz, A.M.I. 2020. A review of machine learning approaches to power system security and stability. IEEE Access, vol. 8, pp. 113512-113531 en_US
dc.identifier.issn 2169-3536
dc.identifier.uri DOI: 10.1109/ACCESS.2020.3003568
dc.identifier.uri https://ieeexplore.ieee.org/document/9121208/
dc.identifier.uri https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9121208
dc.identifier.uri http://hdl.handle.net/10204/11584
dc.description This work is licensed under a Creative Commons Attribution 4.0 License en_US
dc.description.abstract Increasing use of renewable energy sources, liberalized energy markets and most importantly, the integrations of various monitoring, measuring and communication infrastructures into modern power system network offer the opportunity to build a resilient and efficient grid. However, it also brings about various threats of instabilities and security concerns in form of cyberattack, voltage instability, power quality (PQ) disturbance among others to the complex network. The need for efficient methodologies for quicker identification and detection of these problems have always been a priority to energy stakeholders over the years. In recent times, machine learning techniques (MLTs) have proven to be effective in numerous applications including power system studies. In the literature, various MLTs such as artificial neural networks (ANN), Decision Tree (DT), support vector machines (SVM) have been proposed, resulting in effective decision making and control actions in the secured and stable operations of the power system. Given this growing trend, this paper presents a comprehensive review on the most recent studies whereby MLTs were developed for power system security and stability especially in cyberattack detections, PQ disturbance studies and dynamic security assessment studies. The aim is to highlight the methodologies, achievements and more importantly the limitations in the classifier(s) design, dataset and test systems employed in the reviewed publications. A brief review of reinforcement learning (RL) and deep reinforcement learning (DRL) approaches to transient stability assessment is also presented. Finally, we highlighted some challenges and directions for future studies. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.relation.ispartofseries Workflow;23713
dc.subject Classifiers en_US
dc.subject Cyberattacks en_US
dc.subject Deep reinforcement learning en_US
dc.subject Intruder detection systems en_US
dc.subject Machine learning techniques en_US
dc.subject Power quality disturbance en_US
dc.subject Power systems en_US
dc.subject Reinforcement learning en_US
dc.subject Test systems en_US
dc.subject Transient stability assessment en_US
dc.subject Voltage stability en_US
dc.title A review of machine learning approaches to power system security and stability en_US
dc.type Article en_US
dc.identifier.apacitation Alimi, O., Ouahada, K., & Abu-Mahfouz, A. M. (2020). A review of machine learning approaches to power system security and stability. http://hdl.handle.net/10204/11584 en_ZA
dc.identifier.chicagocitation Alimi, OA, K Ouahada, and Adnan MI Abu-Mahfouz "A review of machine learning approaches to power system security and stability." (2020) http://hdl.handle.net/10204/11584 en_ZA
dc.identifier.vancouvercitation Alimi O, Ouahada K, Abu-Mahfouz AM. A review of machine learning approaches to power system security and stability. 2020; http://hdl.handle.net/10204/11584. en_ZA
dc.identifier.ris TY - Article AU - Alimi, OA AU - Ouahada, K AU - Abu-Mahfouz, Adnan MI AB - Increasing use of renewable energy sources, liberalized energy markets and most importantly, the integrations of various monitoring, measuring and communication infrastructures into modern power system network offer the opportunity to build a resilient and efficient grid. However, it also brings about various threats of instabilities and security concerns in form of cyberattack, voltage instability, power quality (PQ) disturbance among others to the complex network. The need for efficient methodologies for quicker identification and detection of these problems have always been a priority to energy stakeholders over the years. In recent times, machine learning techniques (MLTs) have proven to be effective in numerous applications including power system studies. In the literature, various MLTs such as artificial neural networks (ANN), Decision Tree (DT), support vector machines (SVM) have been proposed, resulting in effective decision making and control actions in the secured and stable operations of the power system. Given this growing trend, this paper presents a comprehensive review on the most recent studies whereby MLTs were developed for power system security and stability especially in cyberattack detections, PQ disturbance studies and dynamic security assessment studies. The aim is to highlight the methodologies, achievements and more importantly the limitations in the classifier(s) design, dataset and test systems employed in the reviewed publications. A brief review of reinforcement learning (RL) and deep reinforcement learning (DRL) approaches to transient stability assessment is also presented. Finally, we highlighted some challenges and directions for future studies. DA - 2020-06 DB - ResearchSpace DP - CSIR KW - Classifiers KW - Cyberattacks KW - Deep reinforcement learning KW - Intruder detection systems KW - Machine learning techniques KW - Power quality disturbance KW - Power systems KW - Reinforcement learning KW - Test systems KW - Transient stability assessment KW - Voltage stability LK - https://researchspace.csir.co.za PY - 2020 SM - 2169-3536 T1 - A review of machine learning approaches to power system security and stability TI - A review of machine learning approaches to power system security and stability UR - http://hdl.handle.net/10204/11584 ER - en_ZA


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