Load shedding strategies to mitigate fault induced delayed voltage recovery: trends and development
DOI:
https://doi.org/10.13140/RG.2.2.24721.10081Keywords:
fault-induced delayed voltage recovery, load shedding, short term voltage instability, induction motorsAbstract
Fault-induced delayed voltage recovery (FIDVR) is a major threat to the voltage stability of electrical power systems with load dominated by induction motors. Over the past decade, load rejection strategies have been proposed to mitigate FIDVR and avoid voltage instability. This paper analyzes the load shedding strategies published in the scientific literature to mitigate FIDVR. A classification of scientific publications based on a systematic method of evaluation of characteristics is applied, for which the characteristics of the strategies are identified according to their methodological and technological aspects of design and functionality; further the characteristics are evaluated according to their degree of belonging and with this the analyzed publications are classified. The result of the analysis of the load shedding strategies, based on empirical rules and analytical methods, revealed that they are not yet fully efficient in determining the minimum amount of load to be disconnected, nor fast enough to effectively reduce the voltage recovery time; and it is also found that design methods based on artificial intelligence represent a great opportunity to overcome these challenges.
Downloads
References
P. Irminger; D. T. Rizy; H. Li; T. Smith; K. Rice; F. Li; S. Adhikari. Air conditioning stall phenomenon - testing, model development, and simulation. In PES T & D 2012, pages 1–8. Orlando, FL, USA, IEEE, 2012. https://doi.org/10.1109/TDC.2012.6281531.
H. Saber; M. R. Karimi; E. Hajipour; N. Farzin; S. M. Hashemi; A. Agheli; H. Ayoubzadeh; M. Ehsan. Investigating the effect of ambient temperature on fault-induced delayed voltage recovery events. IET Generation, Transmission & Distribution, 14(9):1781–1790, 2020. https: //doi.org/10.1049/iet-gtd.2019.1025.
N. Hatziargyriou; J. Milanovic; C. Rahmann; V. Ajjarapu; C. Canizares; I. Erlich; D. Hill; I. Hiskens; I. Kamwa; B. Pal; P. Pourbeik; J. Sanchez-Gasca; A. Stankovic; T. Van Cutsem; V. Vittal; C. Vournas. Definition and classification of power system stability – revisited & extended. IEEE Transactions on Power Systems, 36(4):3271–3281, 2021. https://ieeexplore.ieee.org/document/9286772.
S. Adhikari; J. Schoene; N. Gurung; A. Mogilevsky. Fault induced delayed voltage recovery (FIDVR): Modeling and guidelines. In 2019 IEEE Power & Energy Society General Meeting (PESGM), Atlanta, GA, USA, 2019. IEEE. https://ieeexplore.ieee.org/document/8973440.
Institute of Electrical and Electronics Engineers, IEEE. IEEEXplore Digital Library. https://ieeexplore.ieee.org/Xplore/home.jsp.
Elsevier. ScienceDirect. https://www.sciencedirect.com/.
A. R. R. Matavalam; V. Ajjarapu. PMU-based monitoring and mitigation of delayed voltage recovery using admittances. IEEE Transactions on Power Systems, 34(6):4451–4463, 2019. https://ieeexplore.ieee.org/document/8701501.
M. Paramasivam; A. Salloum; V. Ajjarapu; V. Vittal; N. Bhatt; S. Liu. Dynamic optimization based reactive power planning to mitigate slow voltage recovery and short term voltage instability. IEEE Transactions on Power Systems, 28(4):3865–3873, 2013. https://ieeexplore.ieee.org/document/6558852.
W. Wang; F. de Leon. Quantitative evaluation of DER smart inverters for the mitigation of FIDVR in distribution systems. IEEE Transactions on Power Delivery, 35(1):420–429, 2020. https://ieeexplore.ieee.org/document/8770152.
R. Verayiah; A. Mohamed; H. Shareef. Review of under-voltage load shedding schemes in power system operation. Przeglad Elektrotechniczny, 90(7):99–103, 2014. https://www.researchgate.net/publication/288094761_Review_of_under-voltage_load_shedding_schemes_in_power_system_operation.
R.B. Sharma; G.M. Dhole. Wide area measurement technology in power systems. Procedia Technology, 25:718–725, 2016. https://www.sciencedirect.com/science/article/pii/S2212017316305126.
S. M. Miraftabzadeh; F. Foiadelli; M. Longo; M. Pasetti. A survey of machine learning applications for power system analytics. In 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), Genova, Italy, 2019. IEEE. https://ieeexplore.ieee.org/document/8783340.
K. Mollah; M. Bahadornejad; N. K. C. Nair; G. Ancell. Automatic under-voltage load shedding: A systematic review. In 2012 IEEE Power and Energy Society General Meeting, pages 1–7, San Diego, CA, USA, 2012. IEEE. https://ieeexplore.ieee.org/document/6345547.
R. M. Larik; M. W. Mustafa; M. N. Aman. A critical review of the state-of-art schemes for under voltage load shedding. International Transactions on Electrical Energy Systems, 29(5):1–26, 2019. https://onlinelibrary.wiley.com/doi/pdf/10.1002/2050-7038.2828.
J. Hurtado. Metodología de la investigación. Guía para la comprensión holística de la ciencia. Quirón Ediciones, Caracas, Venezuela, 2012.
M. Barrera. Análisis en investigación. Ediciones Quirón, Caracas, Venezuela, 2009.
H. Bai; V. Ajjarapu. A novel online load shedding strategy for mitigating fault-induced delayed voltage recovery. IEEE Transactions on Power Systems, 26(1):294–304, 2011. https://ieeexplore.ieee.org/document/5454321.
Mahari; H. Seyedi. A fast online load shedding method for mitigating FIDVR based on novel stability index. In 2013 21st Iranian Conference on Electrical Engineering (ICEE), pages 1–6, Mashhad, Iran, 2013. IEEE. https://ieeexplore.ieee.org/document/6599887.
B. Otomega; T. Van Cutsem. Distributed load interruption and shedding against voltage delayed recovery or instability. In 2013 IEEE Grenoble Conference, pages 1–6, Grenoble, France, 2013. IEEE. https://ieeexplore.ieee.org/document/6652479.
S. V. Kolluri; J. R. Ramamurthy; S. M. Wong; M. Peterson; P. Yu; M. R. Chander. Relay-based undervoltage load shedding scheme for entergy's western region. In 2015 IEEE Power & Energy Society General Meeting, pages 1–5, Denver, CO, USA, 2015. IEEE. https://ieeexplore.ieee.org/document/7285651.
Y. Dong; X. Xie; K. Wang; B. Zhou; Q. Jiang. An emergency-demand-response based under speed load shedding scheme to improve short-term voltage stability. IEEE Transactions on Power Systems, 32(5):3726–3735, 2017. https://ieeexplore.ieee.org/document/7822924.
Y. Lee; H. Song. Multi-phase under voltage load shedding scheme for preventing delayed voltage recovery by induction motor power consumption characteristics. Applied Sciences, 8(7):1115, 2018. https://www.mdpi.com/2076-3417/8/7/1115.
Joseph; M. Cvetkovi´c; P. Palensky. Predictive mitigation of short term voltage instability using a faster than real-time digital replica. In 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), Sarajevo, Bosnia and Herzegovina, 2018. IEEE. https: //ieeexplore.ieee.org/document/8571803.
S. M. Hashemi; M. Sanaye-Pasand; M. Abedini. Under-impedance load shedding: a new preventive action against voltage instability. IET Generation, Transmission & Distribution, 13(2):201–208, 2018. https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2018.5851.
S. R. Moghadam; E. Hajipour; N. Farzin; M. Vakilian; M. Ehsan. Improvement in voltage recovery delay phenomenon caused by air conditioners specific performance. In 2019 International Power System Conference (PSC), pages 523–529, Tehran, Iran, 2019. IEEE. https://ieeexplore.ieee.org/document/9081481.
E. A. Tapia; D. G. Colome. Mitigación de la recuperación retardada de tensión inducida por falla mediante desconexión de carga basada en el comportamiento dinámico de la carga. Revista Técnica “Energía”, 16(1):23–31, 2019. https://revistaenergia.cenace.gob.ec/index.php/cenace/article/view/332.
C. X. Jiang; Z. Li; J. H. Zheng; Q. H. Wu. Power system emergency control to improve short-term voltage stability using deep reinforcement learning algorithm. In 2019 IEEE 3rd International Electrical and Energy Conference (CIEEC), pages 1872–1877, Beijing, China, 2019. IEEE. https://ieeexplore.ieee.org/document/9077322.
S. Chen; Y. Bai; Z. Jun. Dynamic load shedding strategy using distributional deep reinforcement learning in power system emergency control. In 2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2), pages 248–253, Wuhan, China, 2020. IEEE. https://ieeexplore.ieee.org/document/9346749.
Q. Huang; R. Huang; W. Hao; J. Tan; R. Fan; Z. Huang. Adaptive power system emergency control using deep reinforcement learning. IEEE Transactions on Smart Grid, 11(2):1171–1182, 2020. https://ieeexplore.ieee.org/document/8787888.
Q. Li; Y. Xu; C. Ren. A hierarchical data-driven method for event-based load shedding against fault-induced delayed voltage recovery in power systems. IEEE Transactions on Industrial Informatics, 17(1):699–709, 2021. https://ieeexplore.ieee.org/document/9091249.
L. Zhu; Y. Luo. Deep feedback learning based predictive control for power system undervoltage load shedding. IEEE Transactions on Power Systems, 36(4):3349–3361, 2021. https://ieeexplore.ieee.org/document/9312447.
Published
How to Cite
Issue
Section
Copyright (c) 2022 Gustavo Araujo-Suárez, Carmen Luisa Vásquez Stanescu
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)
The opinions expressed by the authors do not necessarily reflect the position of the publisher of the publication or of UCLA. The total or partial reproduction of the texts published here is authorized, as long as the complete source and the electronic address of this journal are cited.
The authors fully retain the rights to their works, giving the journal the right to be the first publication where the article is presented. The authors have the right to use their articles for any purpose as long as it is done for non-profit. Authors are recommended to disseminate their articles in the final version, after publication in this journal, in the electronic media of the institutions to which they are affiliated or personal digital media.
