Voltage Security Assessment by Using PFDT and CBR Methods in Emerging Power System

Nandanwar, Sonali R.; Kolhe, Mohan Lal; Warkad, S. B.; Patidar, N. P.; Singh, V. K.

doi:10.1016/j.egypro.2018.06.023

Cited by 8 publications

(5 citation statements)

References 18 publications

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“…Small test systems used in the literature are the IEEE 4-bus test system (Abbass et al, 2023), the IEEE 9-bus test system (Bahbah & Girgis, 2004;Goh et al, 2015;Hossain & Kumar, 2023;C. Xu et al, 2005), the IEEE 14bus power system (Adewuyi, Shigenobu, Ooya, Senjyu, & Howlader, 2019;Ali et al, 2015;Dharmapala et al, 2020;Goh et al, 2015;Innah & Hiyama, 2011;Jayasankar et al, 2010;Ortiz-Villalba et al, 2020;Pannell et al, 2018;Pérez-Londoño et al, 2017;, the IEEE 24-bus power system (Y. , the IEEE 30-bus power system (Ali et al, 2015;Jain et al, 2003;Mukherjee & De, 2020;Nandanwar et al, 2018;Pérez-Londoño et al, 2017;Suganyadevi et al, 2016;Suliman & Rahman, 2010;, the IEEE 39-bus New England (Adewole & Tzoneva, 2017;Azman et al, 2020;Bahmanyar & Karami, 2014;Bo et al, 2014;Cepeda et al, 2014;Chen et al, 2019;Echeverría et al, 2017;Frimpong et al, 2017;Gomez et al, 2011;Gu & Li, 2013;Hashiesh et al, 2012;Hossain & Kumar, 2023;Hu et al, 2019;F. Li et al, 2021;Y.…”

Section: Test Systemsmentioning

confidence: 99%

“…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%

“…IS for evaluating long-term (small-disturbance) voltage stability are proposed in (Adewole & Tzoneva, 2017;Diao et al, 2009;Innah & Hiyama, 2011;Jain et al, 2003;Malbasa et al, 2017;H. Mohammadi et al, 2016;Nandanwar et al, 2018;Nandanwar & Warkad, 2016;Nie et al, 2017;Shah & Verma, 2016;Suganyadevi et al, 2016;C. Xu et al, 2005;Zheng et al, 2013;Zhu & Luo, 2021).…”

Section: Voltage Stabilitymentioning

confidence: 99%

“…Mohammadi et al, , 2016M. Mohammadi et al, 2010;Mukherjee & De, 2020;M.V & C.K., 2014;Naderi et al, 2023;Nakas et al, 2023;Nandanwar et al, 2018;Nie et al, 2017;Nuqui et al, 2001;Pannell et al, 2018;Pérez-Londoño et al, 2017;Rahmatian et al, 2017;Ren, Wang, et al, 2023;Ren & Xu, 2019Ren, Yuan, et al, 2023;Rovnyak et al, 1994Rovnyak et al, , 2000Sajan et al, 2015aSajan et al, , 2015bSajan et al, , 2014Senroy et al, 2006;Shah & Verma, 2016;Siddiqui et al, 2018;Song et al, 2020;Suganyadevi et al, 2016;Suliman & Rahman, 2010;Sulistiawati et al, 2016;Sun et al, 2008;Suprême et al, 2018;Tan et al, 2017;Tang et al, 2017;Teeuwsen et al, 2005;Tian et al, 2019;Velayati et al, 2015;B. Wang et al, 2016;C.…”

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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Section: Test Systemsmentioning

confidence: 99%

Section: Types Of Techniquementioning

confidence: 99%

Section: Voltage Stabilitymentioning

confidence: 99%

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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“…e GTEP should be done to meet the high energy demand and electricity consumption surges at peak-load circumstances such as holidays when voltage collapses are highly likely. One solution is to advise and take action beforehand [7]. By utilizing suitable models for GTEP, the abovementioned objectives can be satisfied.…”

Section: Introductionmentioning

confidence: 99%

Robust Allocation of FACTS Devices in Coordinated Transmission and Generation Expansion Planning considering Renewable Resources and Demand Response Programs

Rashid

Akbari

Khalafian

et al. 2022

International Transactions on Electrical Energy Systems

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Nowadays, the use of demand response programs (DRPs) in a variety of long-term and short-term planning problems has been explored. In this paper, a generation and transmission expansion planning (GTEP) model along with FACTS device allocation is presented. Furthermore, demand response programs are taken into account for more load flexibility. The proposed model is presented as a multi-objective minimizing problem considering emission, cost, and voltage security index. Furthermore, the conventional Pareto optimization is adopted using fuzzy weighted sum method (FWSM) to achieve a single-objective model. The final problem is constrained by equations of alternative current power flow, operation and voltage security limits, planning model of shunt FACTS devices, and operation of the DRP. Adaptive robust optimization (ARO) is used to reach suitable models for the active power of renewable resources and power consumption. As a main search algorithm, a hybrid combination of water cycle algorithm (WCA) and ant lion optimization (ALO) is proposed to find the optimum solution with a small standard deviation. The problem is tested on different standard IEEE systems, and the results validate the operation and network security improvement due to optimal location of FACTS devices. According to the results, the economic and environmental status of the network has also improved.

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