Voltage Security Assessment with Application of PMUs Using Decision Tree

Nandanwar, Sonali R.; Warkad, S. B.

doi:10.1109/cicn.2016.78

Cited by 6 publications

(6 citation statements)

References 13 publications

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“…Mohammadi, Khademi, Simon, & Dehghani, 2016;M. Mohammadi, Gharehpetian, & Niknam, 2010;Mukherjee & De, 2020;Naderi, Javadi, Mazhari, & Chung, 2023;Nandanwar, Kolhe, Warkad, Patidar, & Singh, 2018;Nandanwar & Warkad, 2016;Nie, Yang, Centeno, & Jones, 2017;Nuqui, Phadke, Schulz, & Bhatt, 2001;Pannell, Ramachandran, & Snider, 2018;Rahmatian, Chen, Palizban, Moshref, & Dunford, 2017;Ren, Wang, Yu, Xu, & Dong, 2023;Ren, Yuan, Li, Zhang, & Xu, 2023;Rovnyak, Kretsinger, Thorp, & Brown, 1994;Rovnyak, Taylor, & Sheng, 2000;Senroy, Heydt, & Vittal, 2006;Sun et al, 2008;Teeuwsen, Erlich, & El-Sharkawi, 2005;Y. Yang et al, 2017;R.…”

Section: Types Of Techniquementioning

confidence: 99%

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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Section: Types Of Techniquementioning

confidence: 99%

Artificial Intelligence Techniques for Dynamic Security Assessments – A Survey

Cuevas,

Alvarez-Malebrán,

Rahmann

et al. 2024

Preprint

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show abstract

“…The database generation is then usually done offline, given the extensive simulation cost to build it, while the application of the resulting model trained on the dataset can be done offline or online, depending on the application and the context. [15], [16], [19], [22], [27]- [29], [32], [34], [35], [37], [38], [41], [43], [45], [47], [49]- [52], [54]- [56], [63], [64], [67]- [73], [75], [76], [81]- [83], [85]- [88], [90], [92], [93], [98], [102], [105], [107], [113], [115]- [117] Voltage stability [26], [30], [39], [40], [42], [44], [46], [48], [53],…”

Section: A Database Buildingmentioning

confidence: 99%

“…Furthermore, feature selection can help to find the most useful PMUs in a network, e.g. the least number and best locations of PMUs for a given DSA application [37], [42].…”

Section: B Data Pre-processingmentioning

confidence: 99%

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Recent Developments in Machine Learning for Energy Systems Reliability Management

2020

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This paper reviews recent works applying machine learning techniques in the context of energy systems reliability assessment and control. We showcase both the progress achieved to date as well as the important future directions for further research, while providing an adequate background in the fields of reliability management and of machine learning. The objective is to foster the synergy between these two fields and speed up the practical adoption of machine learning techniques for energy systems reliability management. We focus on bulk electric power systems and use them as an example, but we argue that the methods, tools, etc. can be extended to other similar systems, such as distribution systems, micro-grids, and multi-energy systems.

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“…As Redes Neurais Artificiais foram utilizadas para monitorar a estabilidade de tensão em tempo real, por Diao et al (2009), Zhou et al (2010), Innah and Hiyama (2011), Khoshkhoo and Shahrtash (2013), Mohammadi and Dehghani (2015), Nandanwar and Warkad (2016) e Shah and Verma (2016). As redes neurais estão sendo usadas em diferentesáreas de estudos,é uma ferramenta que detecta padrões e informam para os operadores qual a situação do sistema.…”

Section: Introductionunclassified

Avaliação de Índices de Estabilidade de Tensão para Utilização em Sistemas de Medições Fasoriais

Lentz

Alberto

2020

Anais Do Simpósio Brasileiro De Sistemas Elétricos 2020

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A busca por metodologias para avaliar a estabilidade da tensão em tempo real é atualmente um dos maiores desafios para os pesquisadores. Alguns autores usaram medições da \emph{Phasor Measurement Unit} (PMU) para calcular índices e monitorar o quão perto o sistema está do Ponto de Máximo Carregamento (PMC). No entanto, para poder aplicar corretamente esses índices, deve funcionar para todos os tipos de aumento de carga. Este artigo investiga o desempenho de alguns índices existentes para avaliar a margem de estabilidade. Apenas PMC devido a Bifurcação do tipo Sela-Nó (BSN) foi considerado neste artigo. Os resultados indicam que esses índices são fortemente dependentes da direção do aumento da carga e não conseguem detectar corretamente o ínicio de um colapso de tensão.

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Voltage Security Assessment with Application of PMUs Using Decision Tree

Cited by 6 publications

References 13 publications

Artificial Intelligence Techniques for Dynamic Security Assessments – A Survey

Artificial Intelligence Techniques for Dynamic Security Assessments – A Survey

Recent Developments in Machine Learning for Energy Systems Reliability Management

Avaliação de Índices de Estabilidade de Tensão para Utilização em Sistemas de Medições Fasoriais

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