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Multi-objective dynamic VAR planning against fault-induced delayed voltage recovery using heuristic optimization

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Abstract

The fault-induced delayed voltage recovery (FIDVR) and short-term voltage instability are increasing, especially due to the widespread implementation of residential air conditioners (RACs) in modern power systems. Single-phase induction motors in RACs have a high potential to stall in less than two to three cycles following a voltage dip in transmission or distribution systems. Using Shunt-FACTS devices, such as SVC and STATCOM, is a suitable solution for mitigating FIDVR events. In this paper, the Bayesian regularized artificial neural networks technique is employed to solve multidimensional mapping problems, taking into account the reactive powers injected into Busses. Following this, a multi-objective dynamic VAR programming is proposed to identify the optimal size of STATCOM for short-term voltage instability using trajectory sensitivities and heuristic optimization. This method is subject to complying with the criteria for dynamic and transient performance during FIDVR events. Dynamic VAR planning is carried out with assistance of the non-dominated sorting genetic algorithm II (NSGA-ӀӀ). The proposed multi-objective approach has been tested on the IEEE 39-bus system, taking into account time-varying practical load models. The results illustrate the effectiveness of the proposed approach in solving reactive power optimization problems while moderating the consequences of FIDVR.

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Abbreviations

FIDVR:

Fault-induced delayed voltage recovery

RACs:

Residential air conditioners

BRANN:

Bayesian regularized artificial neural network

NSGA-ӀӀ:

Non-dominated sorting genetic algorithm II

STVS:

Short-term voltage stability

FVC:

Fast voltage collapses

TSPF:

Time series power flow

TP:

Thermal protection

UVP:

Under voltage protection

LS:

Load shedding

DRL:

Deep reinforcement learning

UVLS:

Under-voltage load shedding

ELM:

Extreme learning machine

RNN:

Recurrent neural network

IM:

Induction motor

MILP:

Mixed integer linear programming

IBRs:

Inverter-based renewable resource

DER:

Distributed energy resource

LVRT:

Low voltage ride-through

DVS:

Dynamic voltage support

MIQP:

Mixed-integer quadratic programming

PSO:

Particle swarm optimization

MFC:

Model-free control

ABC:

Artificial bee colony

ACOR :

Ant colony optimization for continuous domains

TOPS:

Thermal overload protection switches

D-PMU:

Distribution phasor measurement units

WECC:

Western electricity coordinating council

UVLS:

Under-voltage load shedding

LVRT:

Low voltage ride-through

DAEs:

Differential-algebraic equations

\({\text{RVSI}}\) :

Root-mean-squared voltage-dip severity index

\({\text{TVSI}}\) :

Transient voltage-dip severity index

\({\text{TVDI}}_{i,t}\) :

Transient voltage deviation index in the bus \(i\) at time \(t\)

\(V_{i,0}\) :

Pre-fault voltage magnitude in the bus \(i\)

\(V_{i,t}\) :

Voltage magnitude in the bus \(i\) at time \(t\)

\(T\) :

Post-fault transient time frame

\(t_{{{\text{cl}}}}\) :

Fault clearing time

\(\mu\) :

Threshold of voltage deviation level

\(S_{i,j}^{k}\) :

Sensitivity index at bus \(i\) with respect to injection \(\Delta Q\) to bus \(j\) under contingency \(k\)

\(N_{b}\) :

Number of Busses in power system

\(S_{j}\) :

Total sensitivity index with respect to injection \(\Delta Q\) to bus \(j\)

\(E_{W}\) :

Sum of the squares of the errors

\(E_{D}\) :

Sum of squares of weights and biases

\(\alpha ,\beta\) :

Dispersion parameter for weights and biases

\(t_{i}\) :

Target variables

\(\hat{t}_{i}\) :

Predicted value of the target variables

\(w\) :

Network weights

\(Q_{i}\), \(C_{i}\) :

Installation size and cost of STATCOM

\(w_{1} ,w_{2}\) :

Weights assigned to the objectives

\(P\), \(Q\) :

Active and reactive power

\({\text{RVSI}}_{{{\text{th}}}}\) :

Threshold value of \({\text{RVSI}}\)

\(f\) :

Generator dynamic states function

\(g\) :

Equality system constraints

\(x,y\) :

State and algebraic variables

\(u\) :

Control variables

References

  1. Glavic M, Novosel D, Heredia E, Kosterev D, Salazar A, Habibi-Ashrafi F, Donnelly M (2012) See it fast to keep calm: real-time voltage control under stressed conditions. IEEE Power Energy Mag 10(4):43–55

    Article  Google Scholar 

  2. Dehghani-Ashkezari J, Hamedani-Golshan ME, Mirzaei A (2022) Secured zone 3 operation against fault induced delayed voltage recovery vent. IEEE Trans Power Deliv 37(1):507–516

    Article  Google Scholar 

  3. Zhong Z, Guan L, Su Y, Yu J, Huang J, Guo M (2022) A method of multivariate short-term voltage stability assessment based on heterogeneous graph attention deep network. Int J Electr Power Energy Syst 136:107648

    Article  Google Scholar 

  4. Nguyen HT, Hoang TT, Nguyen TL, Katuri K et al (2022) Transmission distribution coordination for DER fault ride through support. Energy Rep 8:14805–14819

    Article  Google Scholar 

  5. Venkatraman R, Khaitan SK, Ajjarapu V (2019) Dynamic co-Simulation methods for combined transmission-distribution system with integration time step impact on convergence. IEEE Trans Power Syst 34(2):1171–1181

    Article  Google Scholar 

  6. Mohseni-Bonab SM, Hajebrahimi A, Kamwa I, Moeini A (2020) Transmission and distribution co-simulation: a review and propositions. IET Gener Transm Distrib 14(21):4631–4642

    Article  Google Scholar 

  7. Kenyon RW, Matherb B, Hodgea BM (2020) Coupled transmission and distribution simulations to assess distributed generation response to power system faults. Electr Power Syst Res 189:106746

    Article  Google Scholar 

  8. Ramasubramanian D, Mitra P, Dattaray P, Bello M, Boemer JC, Gaikwad A (2021) Analyzing impact of DER on FIDVR - comparison of EMT simulation of a combined transmission and distribution grid with aggregated positive sequence models. Electr Power Syst Res 201:107534

    Article  Google Scholar 

  9. Babaali AH, Ameli MT (2023) Weighted ensemble learning for real-time short-term voltage stability assessment with phasor measurements data. IET Gener Transm Distrib 17(10):2331–2343

    Article  Google Scholar 

  10. Nekkalapu S, Vittal V, Undrill J et al (2021) Synthesis of load and feeder models using point on wave measurement data. IEEE Open Access J Power Energy 8:198–210

    Article  Google Scholar 

  11. Saber H, Karimi MR, Hajipour E et al (2020) Investigating the effect of ambient temperature on fault-induced delayed voltage recovery events. IET Gener Transm Distrib 14(9):1781–1790

    Article  Google Scholar 

  12. Zhang Y, Xu Y, Dong ZY et al (2019) Real-time assessment of fault induced delayed voltage recovery: a probabilistic self-adaptive data driven method. IEEE Trans Smart Grid 10(3):2485–2494

    Article  Google Scholar 

  13. Maoa X, Zhua W, Wub L et al (2019) Real-time multi-state classification of short-term voltage stability based on multivariate time series machine learning. Int J Electr Power Energy Syst 108:402–414

    Article  Google Scholar 

  14. Zhang Y, Xu Y, Dong ZY, Zhang R (2019) A hierarchical self-adaptive data-analytics method for real-time power system short-term voltage stability assessment. IEEE Trans Ind Inform 15(1):74–84

    Article  Google Scholar 

  15. Wang W, Aguilo MD, Mak KB et al (2018) Time series power flow framework for the analysis of FIDVR using linear regression. IEEE Trans Power Deliv 33(6):2946–2955

    Article  Google Scholar 

  16. Sundarajoo S, Soomro DM (2022) Under voltage load shedding and penetration of renewable energy sources in distribution systems: a review. Int J Model Simul 42(4):653–679

    Google Scholar 

  17. Li Q, Xu Y, Ren C (2021) A hierarchical data-driven method for event-based load shedding against fault-induced delayed voltage recovery in power systems. IEEE Trans Ind Inform 17(1):699–709

    Article  Google Scholar 

  18. Huang Q, Huang R, Hao W et al (2020) Adaptive power system emergency control using deep reinforcement learning. IEEE Trans Smart Grid 11(2):1171–1182

    Article  MathSciNet  Google Scholar 

  19. Ren C, Xu Y, Zhang Y et al (2020) A hybrid randomized learning system for temporal-adaptive voltage stability assessment of power systems. IEEE Trans Ind Inform 16(6):3672–3684

    Article  Google Scholar 

  20. Taheri M, Abedini M, Aminifar F (2023) A novel centralized load shedding approach to assess short-term voltage stability: a model-free using time series forecasting. IEEE Trans Power Deliv (early access)

  21. Ghotbi-Maleki M, Mohammadi R, Javadi H (2023) Load shedding method aimed fast voltage recovery to prevent interference of FIDVR with UV relays. IET Gener Transm Distrib 17(11):2667–2686

    Article  Google Scholar 

  22. Ghotbi-Maleki M, Mohammadi-Chabanloo R, Javadi H (2023) Load shedding strategy using online voltage estimation process for mitigating fault-induced delayed voltage recovery in smart networks. Electr Power Syst Res 214:108899

    Article  Google Scholar 

  23. Varma RK, Mohan S (2023) MILP-based load shedding strategy for mitigating FIDVR phenomenon in smart networks. Int J Electr Power Energy Syst 146:108736

    Article  Google Scholar 

  24. Haji-Mohammadi A, Abedini M, Sanaye-Pasand M (2023) Novel relative slip based under-voltage load shedding protection scheme to mitigate FIDVR. IEEE Trans Power Deliv 38(1):277–286

    Article  Google Scholar 

  25. Li Q, Xu Y, Ren C et al (2023) A probabilistic data-driven method for response-based load shedding against fault-induced delayed voltage recovery in power systems. IEEE Trans Power Syst 38(4):3491–3503

    Google Scholar 

  26. Hashemi SM, Sanaye-Pasand M, Abedini M (2019) Under-impedance load shedding: a new preventive action against voltage instability. IET Gener Transm Distrib 13(2):201–208

    Article  Google Scholar 

  27. Bekhradian R, Sanaye-Pasand M, Davarpanah M (2023) Innovative load shedding scheme to restore synchronous generator-based microgrids during FIDVR. IEEE Trans Smart Grid 14(1):388–399

    Article  Google Scholar 

  28. Alzahrani A, Shah R, Mithulananthan N (2021) Examination of effective VAr with respect to dynamic voltage stability in renewable rich power grids. IEEE Access 9:75494–75508

    Article  Google Scholar 

  29. Wang W, Leon FD (2020) Quantitative evaluation of DER smart inverters for the mitigation of FIDVR in distribution systems. IEEE Trans Power Deliv 35(1):420–429

    Article  Google Scholar 

  30. Boricic A, Torres JLR, Popov M (2021) Fundamental study on the influence of dynamic load and distributed energy resources on power system short-term voltage stability. Int J Electr Power Energy Syst 131:107141

    Article  Google Scholar 

  31. Lammert G, Premm D, Ospina LDP et al (2019) Control of photovoltaic systems for enhanced short-term voltage stability and recovery. IEEE Trans Energy Convers 34(1):243–254

    Article  Google Scholar 

  32. Varma RK, Mohan S (2020) Mitigation of fault induced delayed voltage recovery (FIDVR) by PV-STATCOM. IEEE Trans Power Syst 35(6):4251–4262

    Article  Google Scholar 

  33. Ahmed M, Al-Masood N, Aziz T (2023) Optimal selection of single tuned passive filters to enhance post-fault voltage recovery and power quality in a renewable integrated microgrid. IEEE Trans Power Syst 17(7):1747–1767

    Google Scholar 

  34. Sun H, Guo Q, Qi J et al (2019) Review of challenges and research opportunities for voltage control in smart grids. IEEE Trans Power Syst 34(4):2790–2801

    Article  Google Scholar 

  35. Huang W, Sun K, Qi J, Ning J (2017) Optimal allocation of dynamic var sources using the voronoi diagram method integrating linear programing. IEEE Trans Power Syst 32(6):4644–4655

    Article  Google Scholar 

  36. Qi J, Huang W, Sun K, Kang W (2017) Optimal placement of dynamic var sources by using empirical controllability covariance. IEEE Trans Power Syst 32(1):240–249

    Article  Google Scholar 

  37. Huang W, Sun K (2019) Optimization of SVC settings to improve post-fault voltage recovery and angular stability. J Mod Power Syst Clean Energy 7(3):491–499

    Article  Google Scholar 

  38. Ghatak SR, Sannigrahi S, Acharjee P (2018) Comparative performance analysis of DG and DSTATCOM using improved PSO based on success rate for deregulated environment. IEEE Syst J 12(3):2791–2802

    Article  Google Scholar 

  39. Devi S, Geethanjali M (2014) Optimal location and sizing determination of distributed generation and DSTATCOM using particle swarm optimization algorithm. Int J Electr Power Energy Syst 62:562–570

    Article  Google Scholar 

  40. Park B, Olama MM (2021) A model-free voltage control approach to mitigate motor stalling and FIDVR for smart grids. IEEE Trans Smart Grid 12(1):67–78

    Article  Google Scholar 

  41. Park B, Olama MM (2021) Mitigation of motor stalling and FIDVR via energy storage systems with signal temporal logic. IEEE Trans Power Syst 36(2):1164–1174

    Article  Google Scholar 

  42. Zheng H, DeMarco CL (2016) A new dynamic performance model of motor stalling and FIDVR for smart grid monitoring/planning. IEEE Trans Smart Grid 7(4):1989–1996

    Article  Google Scholar 

  43. Bennett M, Liu Y (2020) The impact of large-scale dynamic load modeling on frequency response in the U.S. Eastern Interconnection. Int J Electr Power 120:105983

    Article  Google Scholar 

  44. Bravo R, Xu Y, Chassin DP (2016) Induced delayed voltage recovery (FIDVR) model validation. In: IEEE transmission and distribution conference and exposition (T&D), Dallas, TX, USA, May 2016

  45. Kosterev D, Meklin A, Undrill J et al (2008) Load modeling in power system studies: WECC progress update. In: IEEE General Meeting Power & Energy Society, Pittsburgh, PA, USA, July 2008

  46. Liu Y, Wu L, Li J (2020) D-PMU based applications for emerging active distribution systems: A review. Electr Pow Syst Res 179:106063

    Article  Google Scholar 

  47. Venkatraman R, Khaitan SK, Ajjarapu V (2018) Application of combined transmission-distribution system modeling to WECC composite load model. In: 2018 IEEE Power & Energy Society Gen Meet (PESGM), Portland, OR, USA, August 2018

  48. Hajipour E, Saber H, Farzin N et al (2020) An improved aggregated model of residential air conditioners for FIDVR studies. IEEE Trans Power Syst 35(2):909–919

    Article  Google Scholar 

  49. Lesieutre B (2015) Simulation models for single-phase compressor motors, NERC FIDVR and Dynamic Load Modeling Workshop. September 2015

  50. Kosterev D (2015) Development and implementation of composite load model in WECC. In: 2015 CIGRE grid of future conference

  51. Zhang K, Zhu H, Guo S (2017) Dependency analysis and improved parameter estimation for dynamic composite load modeling. IEEE Trans Power Syst 32(4):3287–3297

    Article  Google Scholar 

  52. Abaci K, Yamacli V, Chen Z (2021) Voltage stability improvement with coordinated ULTC–STATCOM controller and VSC-HVDC in high wind penetration cases. Electr Eng J 103:837–851

    Article  Google Scholar 

  53. Xu Y, Dong ZY, Meng K et al (2014) Multi-objective dynamic VAR planning against short-term voltage instability using a decomposition based evolutionary algorithm. IEEE Trans Power Syst 29(6):2813–2822

    Article  Google Scholar 

  54. Eristi B, Eristi H (2022) A new deep learning method for the classification of power quality disturbances in hybrid power system. Electr Eng 104:3753–3768

    Article  Google Scholar 

  55. Wua D, Huang H, Qiu S et al (2022) Application of Bayesian regularization back propagation neural network in sensorless measurement of pump operational state. Energy Rep 8:3041–3050

    Article  Google Scholar 

  56. Kayri M (2016) Predictive abilities of Bayesian regularization and Levenberg–Marquardt algorithms in artificial neural networks: A aomparative empirical study on social data. Math Comput Appl 21(2):20

    MathSciNet  Google Scholar 

  57. Tahboub AM, Moursi MS, Woon WL et al (2018) Multi-objective dynamic VAR planning strategy with different shunt compensation technologies. IEEE Trans Power Syst 33(3):2429–2439

    Article  Google Scholar 

  58. DIgSILENT PowerFactory Version 15 User’s Manual, DIgSILENT GmbH, Gomaringen, Germany (2014)

  59. Sullivan D, Buterbaugh B, Paramalingam J (2018) Application of FACTS devices for a dynamic power system within the USA. In: International power electronics conference (IPEC- Niigata- ECCE Asia), Niigata, Japan, May 2018

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Authors and Affiliations

  1. Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Maryam Bahramgiri & S. Babak Mozafari

  2. Electrical Engineering Department, Sharif University of Technology, Tehran, Iran

    Mehdi Ehsan

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  1. Maryam Bahramgiri
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Bahramgiri, M., Ehsan, M. & Babak Mozafari, S. Multi-objective dynamic VAR planning against fault-induced delayed voltage recovery using heuristic optimization. Electr Eng (2024). https://doi.org/10.1007/s00202-024-02326-7

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