A Hybrid Method for Optimal Allocation of Shunt Compensators to Mitigate Fault Induced Delayed Voltage Recovery (FIDVR)

Subject Areas : Generation, transmission and distribution

Maryam Bahramgiri ¹ , Mehdi Ehsan ² , Seyed Babak Mozafari ³

1 - Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 - Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
3 - Faculty of Mechanics, Electrical Power and Computer- Science and Research Branch, Islamic Azad University, Tehran, Iran

Received: 2024-02-03 Accepted : 2024-05-28 Published : 2024-09-08

Keywords: Composite load model, fault-induced delayed voltage recovery, multi-objective particle swarm optimization, residential air conditioners, volt/ampere reactive.,

Abstract :

The widespread use of residential air conditioning (RAC) systems in modern power systems has resulted in an increase in the phenomenon of fault-induced delayed voltage recovery (FIDVR). This phenomenon leads to short-term voltage instability and sometimes even voltage collapse. To address this issue, parallel FACTS devices such as SVC and STATCOM can be used. In this paper, a data-driven hybrid approach based on volt-ampere reactive (VAR) placement is proposed to reduce FIDVR events. This approach uses a new and efficient index for voltage evaluation after faults and determines the optimal location and size of VAR resources considering economic and technical constraints. A multi-layer perceptron (MLP) neural network is used to solve the multi-dimensional mapping problem considering reactive power injections into buses. Then, a multi-objective optimization is proposed to identify the optimal size of VAR resources to address short-term voltage instability and prevent FIDVR events using intelligent optimization methods. First, optimization is performed for the single-objective function with predefined weights by the PSO algorithm, and then the results are compared with the artificial bee colony (ABC) algorithm, ant colony optimization for continuous domains (ACO_R), and differential evolution (DE) algorithms. Additionally, this paper focuses on identifying a Pareto front of non-dominated solutions using multi-objective particle swarm optimization (MOPSO). The proposed approach is tested on the 39-bus IEEE system considering a time-varying dynamic model for residential air conditioning loads. The results show that this approach is highly effective in solving reactive power optimization problems and reducing FIDVR effects.

References:

[1] K. Zhang, H. Zhu, S. Guo, "Dependency analysis and improved parameter estimation for dynamic composite load modeling", IEEE Transaction on Power System, vol. 32, no. 4, pp. 3287–3297, July 2017 (doi: 10.1109/PESGM.2017.8273973).
[2] E. Hajipour, H. Saber, N. Farzin, M. R. Karimi, S.M. Hashemi, A. Agheli, H. Ayoubzadeh, M. Ehsan, "An improved aggregated model of residential air conditioners for FIDVR studies", IEEE Transaction on Power System, vol. 35, no. 2, pp. 909-919, March 2020 (doi: 10.1109/TPWRS.2019.2940596).
[3] Z. Zhong, L. Guan, Y. Su, J. Yu, J. Huang, M. Guo, "A method of multivariate short-term voltage stability assessment based on heterogeneous graph attention deep network", International Journal of Electrical Power and Energy Systems, vol. 136, p. 107648, March 2022 (doi: 10.1016/j.ijepes.2021.107648).
[4] R. Bekhradian, M. Davarpanah, M. Sanaye-Pasand, "Current-based blocking scheme to stabilize distribution network relays against FIDVR", International Journal of Electrical Power and Energy Systems, vol. 132, p. 107205, Nov. 2021 (doi: 10.1016/j.ijepes.2021.107205).
[5] S. Sundarajoo, D. M. Soomro, "Under voltage load shedding and penetration of renewable energy sources in distribution systems: a review", International Journal of Modelling and Simulation, vol. 42, no. 4, pp. 653-679, Nov. 2022 (doi: 10.1080/02286203.2022.2143191).
[6] H. Yang , N. Li, Z. Sun, D. Huang, D. Yang, G. Cai, C. Liu, T. Zhang, W. Zhang, "Real-Time Adaptive UVLS by Optimized Fuzzy Controllers for Short-Term Voltage Stability Control", IEEE Transaction on Power System, vol. 37, no. 2, pp. 1449-1460, March 2022 (doi: 10.1109/TPWRS.2021.3105090).
[7] 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, vol. 17, no. 1, pp. 699-709, Jan. 2021 (doi: 10.1109/TII.2020.2993807).
[8] Q. Huang, R. Huang, W. Hao, J. Tan, R. Fan, Z. Huang, "Adaptive power system emergency control using deep reinforcement learning", IEEE Transaction on Smart Grid, vol. 11, no. 2, pp. 1171-1182, March 2020 (doi: 10.1109/TSG.2019.2933191).
[9] S. Nourollah, F. Aminifar, G. B. Gharehpetian, "A hierarchical regionalization-based load shedding plan to recover frequency and voltage in microgrid", IEEE Transactions on Smart Grid, vol. 10, no. 4, pp. 3818-3827, July 2019 (doi: 10.1109/TSG.2018.2837160).
[10] M. Ghotbi-Maleki, R. Mohammadi-Chabanloo, H. Javadi, "Load shedding strategy using online voltage estimation process for mitigating fault-induced delayed voltage recovery in smart networks", Electric Power Systems Research, vol. 214, p. 108899, Jan. 2023 (doi: 10.1016/j.epsr.2022.108899).
[11] A. Haji-Mohammadi, M. Abedini, M. Sanaye-Pasand, "Novel relative slip based under-voltage load shedding protection scheme to mitigate FIDVR", IEEE Transactions on Power Delivery, vol. 38, no. 1, pp. 277-286, Feb. 2023 (doi: 10.1109/TPWRD.2022.3184356).
[12] M. Ghotbi-Maleki, R. Mohammadi-Chabanloo, H. Javadi, "MILP-based load shedding strategy for mitigating FIDVR phenomenon in smart networks", International Journal of Electrical Power and Energy Systems, vol. 146, pp. 108736, March 2023 (doi: 10.1016/j.ijepes.2022.108736).
[13] M. Taheri, M. Abedini, F. Aminifar, "A novel centralized load shedding approach to assess short-term voltage stability: a model-free using time series forecasting", IEEE Transactions on Power Delivery, vol. 38, no. 5, Oct. 2023 (doi: 10.1109/TPWRD.2023.3266265).
[14] M. Ghotbi-Maleki, R. Mohammadi, H. Javadi, "Load shedding method aimed fast voltage recovery to prevent interference of FIDVR with UV relays", IET Generation, Transmission and Distribution, vol. 17, no. 11, pp. 2667-2686, June 2023 (doi: 10.1049/gtd2.12846).
[15] Q. Li; Y. Xu; C. Ren; R. Zhang, "A probabilistic data-driven method for response- based load shedding against fault-induced delayed voltage recovery in power system", IEEE Transactions on Power Systems, vol. 38, no. 4, pp. 3491-3503, July 2023 (doi: 10.1109/TPWRS.2022.3206839).
[16] S.M. Hashemi, M. Sanaye-Pasand, M. Abedini, "Under‐impedance load shedding a new preventive action against voltage instability", IET Generation, Transmission and Distribution, vol. 13, no. 2, pp. 201-208, Jan. 2019 (doi: 10.1049/iet-gtd.2018.5851).
[17] A. Gargoom, M. Elmusrati, A. Gaouda, "Enhancing the operation of smart inverters with PMU and data concentrators", International Journal of Electrical Power and Energy Systems, vol. 140, p. 108077, Sep. 2022 (doi: 10.1016/j.ijepes.2022.108077).
[18] M. Ahmed, N. Al-Masood, T. Aziz, "Optimal selection of single tuned passive filters to enhance post‐fault voltage", IET Renewable Power Generation, vol. 17, no. 7, p. 1747-1767, May 2023 (doi: 10.1049/rpg2.12710).
[19] A. Boricic, J.L.R. Torres, M. Popov, "Fundamental study on the influence of dynamic load and distributed energy resources on power system short-term voltage stability", International Journal of Electrical Power and Energy Systems, vol. 131, p. 107141, Oct. 2021 (doi: 10.1016/j.ijepes.2021.107141).
[20] A. Dalirian, A. Solat, S.M.J. Rastegar-Fatemi, "Smart control of photovoltaic static compensator system based on fuzzy logic control to improve voltage stability", Journal of Intelligent Procedures in Electrical Technology, vol. 13, no. 49, pp. 119-134, March 2023 (in Persian) (dor: 20.1001.1.23223871.1403.15.60.7.2).
[21] M. Abbasi, M Nafar, M Simab, "Management and control of microgrids connected to three-phase network with the approach of activating current limitation under unbalanced errors using fuzzy iIntelligent method with the presence of battery, wind, photovoltaic and diesel sources", Journal of Intelligent Procedures in Electrical Technology, vol. 15, no. 60, pp. 55-66, June 2021 (in Persian) (dor: 20.1001.1.23223871.1401.13.49.4.3).
[22] H.A. Villarroel-Gutierrez, J. Morales, M. Molina, "A novel methodology for dynamic voltage support with adaptive schemes in photovoltaic generators", Electrical Engineering, vol. 104, pp. 4103-4123, July 2022 (doi: 10.1007/s00202-022-01600-w).
[23] A. Alzahrani, R. Shah, N. Mithulananthan, "Examination of effective VAr with respect to dynamic voltage stability in renewable rich power grids", IEEE Access, vol. 9, pp. 75494-75508, May 2021 (doi: 10.1109/ACCESS.2021.3079292).
[24] H. Sun, Q. Guo, J. Qi, V. Ajjarapu, R. Bravo, J. Chow, Z. Li, R. Moghe, E. Nasr-Azadani, U. Tamrakar, G.N. Taranto, R. Tonkoski, G. Valverde, Q. Wu, G.Yang, "Review of challenges and research opportunities for voltage control in smart grids", IEEE IEEE Transactions on Power Systems, vol. 34, no. 4, pp. 2790-2801, July 2019 (doi: 10.1109/TPWRS.2019.2897948).
[25] S.R. Ghatak, S. Sannigrahi, P. Acharjee, "Comparative performance analysis of DG and DSTATCOM using improved PSO based on success rate for deregulated environment", IEEE Systems Journal, vol. 12, no. 3, pp. 2791-2802, Sep. 2018 (doi: 10.1109/JSYST.2017.2691759).
[26] S. Devi, M. Geethanjali, "Optimal location and sizing determination of distributed generation and DSTATCOM using particle swarm optimization algorithm", International Journal of Electrical Power and Energy Systems, vol. 62, pp. 562–570, Nov. 2014 (doi: 10.1016/j.ijepes.2014.05.015).
[27] J. Qi, W. Huang, K. Sun, W. Kang, "Optimal placement of dynamic var sources by using empirical controllability covariance", IEEE Transactions on Power Systems, vol. 32, no. 1, p. 240-249, Jan. 2017 (doi: 10.1109/TPWRS.2016.2552481).
[28] W. Huang, K. Sun, J. Qi, J. Ning, "Optimal allocation of dynamic var sources using the voronoi diagram method integrating linear programing", IEEE Transactions on Power Systems, vol. 32, no. 6, pp.4644-4655, Nov. 2017 (doi: 10.1109/TPWRS.2017.2681459).
[29] W. Wang, M.D. Aguilo, K.B. Mak F.D. Leon, D. Czarkowski, Member, R. E. Uosef, "Time series power flow framework for the analysis of FIDVR using linear regression", IEEE Transactions on Power Delivery, vol. 33, no. 6, pp. 2946-2955, Dec. 2018 (doi: 10.1109/TPWRD.2018.2832852).
[30] R. Bravo, Y. Xu, D. P. Chassin, "Fault induced delayed voltage recovery (FIDVR) model validation", IEEE Transmission and Distribution Conference and Exposition (T&D), Dallas, TX, USA, May 2016 (doi: 10.1109/TDC.2016.7520045).
[31] Y. Liu, L. Wu, J. Li, "D-PMU based applications for emerging active distribution systems: A review", Electric Power Systems Research, vol. 179, p. 106063, Feb. 2020 (doi: 10.1016/j.epsr.2019.106063).
[32] 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 and Distribution, vol. 14, no. 9, p. 1781-1790, May 2020 (doi: 10.1049/iet-gtd.2019.1025).
[33] S. Nekkalappu, V. Vittal, J. Undrill, B. Keel, B. Gong, K. Brown, "Synthesis of load and feeder models using point on wave measurement data", IEEE Open Access Journal of Power and Energy, vol. 8, pp. 198-210, May 2021 (doi: 10.1109/OAJPE.2021.3079724).
[34] WECC dynamic composite load model (CMPLDW) specifications, Western Electricity Coordination Council, Jan. 2015.
[35] H. Bai, V. Ajjarapu, "A novel online load shedding strategy for mitigating fault-induced delayed voltage recovery", IEEE Trans. on Power Systems, vol. 26, no. 1, pp. 294-304, Feb. 2011 (doi: 10.1109/TPWRS.2010.2047279).
[36] ‘WECC dynamic composite load model (CMPLDW) specifications’, https://home.engineering.iastate.edu/~jdm/ee554/WECC%20Composite%20Load%20Model%20Specifications%2001-27-2015.pdf, accessed 11 June 2019.
[37] Y. Xu, Z. Y. Dong, K. Meng, W. F. Yao, R. Zhang, K. P. Wong, "Multi-objective dynamic VAR planning against short-term voltage instability using a decomposition based evolutionary algorithm", IEEE Trans. on Power Systems, vol. 29, no. 6, pp. 2813-2822, Nov. 2014 (doi: 10.1109/TPWRS.2014.2310733).
[38] Y. Dong, X. Xie, B. Zhou, W. Shi, and Q. Jiang, "An integrated high side var-voltage control strategy to improve short-term voltage stability of receiving-end power systems", IEEE Trans. on Power System, vol. 31, no. 3, pp. 2105-2115, May 2016 (doi: 10.1109/PESGM.2016.7741250).
[39] Y. Zhang, Y. Xu, Z. Y. Dong, R. Zhang, "A hierarchical self-adaptive data-Analytics method for real-time power system short-term voltage stability assessment", IEEE Trans. on Industrial Informatics, vol. 15, no. 1, pp. 74-84, Jan. 2019 (doi: 10.1109/TII.2018.2829818).
[40] R. Sellami, F. Sher, R. Neji, "An improved MOPSO algorithm for optimal sizing & placement of distributed generation: A case study of the Tunisian offshore distribution network (ASHTART)", Energy Reports, vol. 8, pp. 6960-6975, Nov. 2022 (doi: 10.1016/j.egyr.2022.05.049).
[41] C. Mokhtara, B. Negrou, N. Settou, B. Settou, M. MahmoudSamy, "Design optimization of off-grid hybrid renewable energy systems considering the effects of building energy performance and climate change: Case study of Algeria", Energy, vol. 219, p. 119605, March 2021 (doi: 10.1016/j.energy.2020.119605).
[42] D. Kosterev, "Composite load model development and implementation", NERC-DOE FIDVR Conference, Alexandria, VA, USA, pp.121-128, 2015, https://eta-publications.lbl.gov/sites/default/files/3-composite-load-model-development.pdf.
[43] A. G. Gad, "Particle swarm optimization algorithm and its applications: a systematic review", Arch. Comput. Methods Eng., vol. 29, p. 2531–2561, Apr. 2022 (doi: 0.1007/s11831-021-09694-4).