Prediction of Sub-synchronous Resonance Oscillations - A Machine Learning Approach

Krishnan, Karthik; Thampatty, K. C. Sindhu

doi:10.1109/sces50439.2020.9236775

Cited by 6 publications

(8 citation statements)

References 15 publications

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“…Zhang, Xu, & Dong, 2018;Zheng, Malbasa, & Kezunovic, 2013;Zhu, Lu, Dong, & Hong, 2017). Another commonly used technique is Random Forest (RF) (Kaci, Kamwa, Dessaint, & Guillon, 2014;Kamwa, Samantaray, & Joos, 2010;Krishnan & Thampatty, 2020;S. Liu et al, 2020;Malbasa, Zheng, Chen, Popovic, & Kezunovic, 2017;Ortiz-Villalba, Rahmann, Alvarez, Canizares, & Strunck, 2020;Suprême, Dessaint, Kamwa, & Heniche-Oussédik, 2018;R.…”

Section: Types Of Techniquementioning

confidence: 99%

“…Wang, Fang, Wang, Liu, & Liu, 2016), and Ball Vector Machines (M. Mohammadi et al, 2010). Other algorithms are K-Nearest Neighbor (KNN) (C. He, Guan, & Mo, 2016;Krishnan & Thampatty, 2020;R. Liu, Verbič, & Ma, 2019;Y.…”

Section: Types Of Techniquementioning

confidence: 99%

“…Using DL enabled it to extract spatial and temporal features from the input data and combine local features with global ones, which ML algorithms such as SVM or simpler ANN structures could not. MPL applications with more than one hidden layer only started to become prevalent in recent years, as seen in (Abbass, Lis, & Mushtaq, 2023;Ali, El-Amary, Ibrahim, & Mekhamer, 2015;Anderson et al, 2023;Bahmanyar & Karami, 2014;Gurung, Naetiladdanon, & Sangswang, 2021;Hashiesh, Mostafa, Khatib, Helal, & Mansour, 2012;Hossain & Kumar, 2023;Krishnan & Thampatty, 2020;Naderi et al, 2023;Ren & Xu, 2019;Sepehr, Gomis-Bellmunt, & Pouresmaeil, 2022;Shah & Verma, 2016;Tang et al, 2017;Y. Wang & Pal, 2023;R.…”

Section: Types Of Techniquementioning

confidence: 99%

“…Hence, resonance stability can be torsional resonance (effect of HVDC links and FACTs) or electrical resonance (effect of DFIG) (Hatziargyriou et al, 2021). Because resonance stability is a relatively new phenomenon, only one work addresses this type of stability using IS (Krishnan & Thampatty, 2020), in which the authors trained different techniques to predict unstable sub-synchronous resonance (SSR) oscillations.…”

Section: Resonance Stabilitymentioning

confidence: 99%

“…The main idea is to support energy regulators and system operators in validating the results of these studies from a stability perspective, which confirms the validity of different operation schedules or expansion plans from a stability viewpoint. Another example of an off-line application is to design a specific control strategy (see, for example, (Adewuyi et al, 2019;Arteaga et al, 2019;Chang et al, 2005;Gurung et al, 2021;Krishnan & Thampatty, 2020;Lin, 2013;Ortiz-Villalba et al, 2020;Yuanhang et al, 2015;R. Zhang et al, 2012)).…”

Section: Online and Off-line Applicationsmentioning

confidence: 99%

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Artificial Intelligence Techniques for Dynamic Security Assessments – A Survey

Cuevas,

Alvarez-Malebrán,

Rahmann

et al. 2024

Preprint

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The rise of converter interface generation (CIGs) technologies with fast response times has introduced new challenges for the stability of electrical power systems. Traditional dynamic security assessment (DSA) methods of power systems are based on dynamic simulations within a time domain. Because of the high computational efforts and human resources required to carry out these kinds of simulations, they are performed for a limited number of adverse (critical) operational conditions in terms of their stability. However, further incorporation of CIGs in power systems not only results in a shift of the critical operating conditions that may threaten the system’s stability, but also the number of critical conditions increases. Consequently, there is an urgent need to develop new methods for evaluating system stability in a wide range of operating conditions with a reasonable amount of human and computational effort. A complementary 1 strategy to the traditional methods for DSA is to use artificial intelligence (AI) techniques. Unlike traditional methods, AI techniques use a data set that captures the non-linear relationships between the system’s operational conditions and their stability, without needing to solve the algebraic-differential equations modeling the power system. Once these relationships are established, the system’s stability can be evaluated for other operational conditions much faster (within the order of the hundreds of milliseconds) and accurately. This, in turn, allows for the consideration of an extended range of operational conditions for DSA studies. This article surveys the state of the art regarding the use of AI techniques for DSA in electrical power systems. Focus is made on algorithms, the kind of stability being addressed, data processing and applications. Finally, limitations, challenges, and future trends are discussed.

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