Skip to main content

Advertisement

SpringerLink
Log in

Optimal deep learning control for modernized microgrids

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

In this study, a new control approach is introduced for active/reactive power control in modernized microgrids (MMGs). The dynamics of MMG are considered to be unknown and a fuzzy reference tracking linear quadratic regulator (FRT-LQR) is designed. To tackle the effect of uncertainties and faults such as short-Circuit, weak connection, unbalanced grids, an optimal \(H_{\infty }\)-based deep learned control (OHDLC) is presented. The main contributions are: (1) The dynamics are unknown, and are online identified by the restricted Boltzmann machines (RBMs). (2) The parameters in hidden layers are tuned by the unsupervised contrastive divergence (UCD) algorithm, and the parameters in the output layers are tuned by the designed Lyapunov based learning rules that ensure the stability. (3) The designed \(H_{\infty }\)-based supervisor compensates the perturbations. (4) Several simulations, comparisons, and real-time examination as Hardware-in-the Loop (HiL) setup verify the applicability of the suggested control method. A comparison between the suggested approach and related controllers shows that the designed controller is more robust and accurate. In the suggested method, besides the fact that the deep learning approach improves the accuracy, the designed \(H_{\infty }\)-based supervisor also enhances the robustness.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Algorithm 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

References

  1. Rai A, Das DK (2022) Ennoble class topper optimization algorithm based fuzzy pi-pd controller for micro-grid. Appl Intell 52(6):6623–6645

    Article  Google Scholar 

  2. Jagadeshwar M, Das DK (2022) A novel adaptive model predictive control scheme with incremental conductance for extracting maximum power from a solar panel. Iranian J Sci Technol Trans Electr Eng:1–12

  3. Aatabe M, El Guezar F, Vargas AN, Bouzahir H (2021) A novel stochastic maximum power point tracking control for off-grid standalone photovoltaic systems with unpredictable load demand, Energy:121272

  4. Suppioni VP, Grilo AP (2021) Unbalance compensation control in microgrids based on the unbalance profile. Electr Power Syst Res 196:107199

    Article  Google Scholar 

  5. El Hamdi I, Vargas AN, Bouzahir H, Oliveira RC, Acho L (2021) Robust stability of stochastic systems with varying delays: application to RLC circuit with intermittent closed-loop. Appl Math Computat 411:126541

    Article  MathSciNet  MATH  Google Scholar 

  6. Mohammadi K, Azizi E, Choi J, Beheshti MTH, Bidram A, Bolouki S (2021) Asynchronous periodic distributed event-triggered voltage and frequency control of microgrids, IEEE Trans Power Syst

  7. Liang Y, Zhang H, Zhang J, Luo Y (2021) Integral reinforcement learning-based guaranteed cost control for unknown nonlinear systems subject to input constraints and uncertainties. Appl Math Computat 408:126336

    Article  MathSciNet  MATH  Google Scholar 

  8. Cao G, Lou G, Gu W, Sheng L (2021) \(H_{\infty }\) robustness for distributed control in autonomous microgrids considering cyber disturbances, CSEE J Power Energy Syst

  9. Rosini A, Labella A, Bonfiglio A, Procopio R, Guerrero JM (2021) A review of reactive power sharing control techniques for islanded microgrids. Renewable Sustain Energy Rev 141: 110745

    Article  Google Scholar 

  10. Han Y, Ning X, Li L, Yang P, Blaabjerg F (2021) Droop coefficient correction control for power sharing and voltage restoration in hierarchical controlled DC microgrids. Int J Electr Power Energy Syst 133:107277

    Article  Google Scholar 

  11. Barros JD, Rocha L, Silva JF (2021) Backstepping predictive control of hybrid microgrids interconnected by neutral point clamped converters. Electronics 10(10):1210

    Article  Google Scholar 

  12. Wang Z, Yi H, Zhuo F, Lv N, Ma Z, Wang F, Zhou W, Liang J, Fan H (2021) Active power control of voltage-controlled photovoltaic inverter in supporting islanded microgrid without other energy sources. IEEE J Emerging Select Topics Power Electr

  13. Ijaz S, Fuyang C, Hamayun MT, Anwaar H (2021) Adaptive integral-sliding-mode control strategy for maneuvering control of F16 aircraft subject to aerodynamic uncertainty. Appl Math Computat 402:126053

    Article  MathSciNet  MATH  Google Scholar 

  14. Wang S, Xia J, Park JH, Shen H, Chen G (2019) Adaptive event-triggered control for mimo nonlinear systems with asymmetric state constraints based on unified barrier functions. Int J Robust Nonlinear Contr

  15. Wang X, Wang Z, Lu J, Meng B (2021) Stability, bifurcation and chaos of a discrete-time pair approximation epidemic model on adaptive networks. Math Comput Simul 182:182–194

    Article  MathSciNet  MATH  Google Scholar 

  16. Ma K, Hu X, Yue Z, Wang Y, Yang J, Zhao H, Liu Z (2022) Voltage regulation with electric taxi based on dynamic game strategy. IEEE Trans Veh Technol 71(3):2413–2426

    Article  Google Scholar 

  17. Zhou H, Zhao H, Zhang Y (2020) Nonlinear system modeling using self-organizing fuzzy neural networks for industrial applications. Appl Intell 50(5):1657–1672

    Article  Google Scholar 

  18. Pan X, Wang L, Qiu Q, Qiu F, Zhang G (2022) Many-objective optimization for large-scale evs charging and discharging schedules considering travel convenience. Appl Intell 52(3):2599–2620

    Article  Google Scholar 

  19. Chang X-H, Jin X (2022) Observer-based fuzzy feedback control for nonlinear systems subject to transmission signal quantization. Appl Math Computat 414:126657

    Article  MathSciNet  MATH  Google Scholar 

  20. Lin F-J, Chen C-I, Xiao G-D, Chen P-R (2021) Voltage stabilization control for microgrid with asymmetric membership function based wavelet petri fuzzy neural network. IEEE Trans Smart Grid

  21. Ertuğrul Ö F, Tekin H, Tekin R (2021) A novel regression method in forecasting short-term grid electricity load in buildings that were connected to the smart grid. Electr Eng 103(1):717–728

    Article  Google Scholar 

  22. Chen C-I, Lan C-K, Chen Y-C, Chen C-H, Chang Y-R (2020) Wavelet energy fuzzy neural network-based fault protection system for microgrid. Energies 13(4):1007

    Article  Google Scholar 

  23. Babaei M, Azizi E, Beheshti MT, Hadian M (2020) Data-driven load management of stand-alone residential buildings including renewable resources, energy storage system, and electric vehicle. J Energy Storage 28:101221

    Article  Google Scholar 

  24. Ghazzali M, Haloua M, Giri F (2020) Modeling and adaptive control and power sharing in islanded ac microgrids. Int J Contr Autom Syst 18(5):1229–1241

    Article  Google Scholar 

  25. Babayomi O, Zhang Z, Li Y, Kennel R (2021) Adaptive predictive control with neuro-fuzzy parameter estimation for microgrid grid-forming converters. Sustainability 13(13):7038

    Article  Google Scholar 

  26. Singh P, Lather J (2020) Accurate power-sharing, voltage regulation, and SOC regulation for LVDC microgrid with hybrid energy storage system using artificial neural network. Int J Green Energy 17(12):756–769

    Article  Google Scholar 

  27. Jabeen G, Rahim S, Afzal W, Khan D, Khan AA, Hussain Z, Bibi T (2022) Machine learning techniques for software vulnerability prediction: a comparative study. Appl Intell:1–22

  28. Haque M, Sarker S, Dewan M et al (2022) Driving maneuver classification from time series data: a rule based machine learning approach. Appl Intell:1–16

  29. Lu H, Uddin S, Hajati F, Moni MA, Khushi M (2022) A patient network-based machine learning model for disease prediction: the case of type 2 diabetes mellitus. Appl Intell 52(3):2411–2422

    Article  Google Scholar 

  30. Woźniak M, Zielonka A, Sikora A, Piran MJ, Alamri A (2020) 6G-enabled IoT home environment control using fuzzy rules. IEEE Internet Things J 8(7):5442–5452

    Article  Google Scholar 

  31. Sahoo KK, Dutta I, Ijaz MF, Woźniak M, Singh PK (2021) TLEFuzzyNet: fuzzy rank-based ensemble of transfer learning models for emotion recognition from human speeches. IEEE Access 9:166518–166530

    Article  Google Scholar 

  32. Wang S, Takyi-Aninakwa P, Jin S, Yu C, Fernandez C, Stroe D-I (2022) An improved feedforward-long short-term memory modeling method for the whole-life-cycle state of charge prediction of lithium-ion batteries considering current-voltage-temperature variation. Energy:124224

  33. Wang S, Jin S, Bai D, Fan Y, Shi H, Fernandez C (2021) A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries. Energy Rep 7:5562–5574

    Article  Google Scholar 

  34. Liu K, Ke F, Huang X, Yu R, Lin F, Wu Y, Ng DWK (2021) Deepban: a temporal convolution-based communication framework for dynamic wbans. IEEE Trans Commun 69(10):6675–6690

    Article  Google Scholar 

  35. Kavousi-Fard A, Su W, Jin T (2020) A machine-learning-based cyber attack detection model for wireless sensor networks in microgrids. IEEE Trans Industr Inf 17(1):650–658

    Article  Google Scholar 

  36. Ashtari B, Alizadeh Bidgoli M, Babaei M, Ahmarinejad A (2022) A two-stage energy management framework for optimal scheduling of multi-microgrids with generation and demand forecasting. Neural Comput Appl:1–15

  37. Ye L, Wang Z, Liu Y, Chen P, Li H, Zhang H, Wu M, He W, Shen L, Zhang Y et al (2021) The challenges and emerging technologies for low-power artificial intelligence IoT systems. IEEE Trans Circuits Syst I Regular Papers

  38. Vakitbilir N, Hilal A, Direkoğlu C (2022) Hybrid deep learning models for multivariate forecasting of global horizontal irradiation. Neural Comput Appl:1–22

  39. Arwa EO, Folly KA (2020) Reinforcement learning techniques for optimal power control in grid-connected microgrids: a comprehensive review. IEEE Access 8:208992–209007

    Article  Google Scholar 

  40. Xin L (2021) Evaluation of factors affecting dance training effects based on reinforcement learning. Neural Comput Appl:1–13

  41. Li C, Dong Z, Chen G, Zhou B, Zhang J, Yu X (2021) Data-driven planning of electric vehicle charging infrastructure: a case study of sydney, australia, IEEE Trans Smart Grid

  42. Mao Y, He B, Wang D, Jiang R, Zhou Y, He X, Zhang J, Dong Y (2021) Microgrid group control method based on deep learning under cloud edge collaboration. Wireless Commun Mobile Comput

  43. Delavari H, Naderian S (2021) Design and hil implementation of a new robust fractional sliding mode control of microgrids, IET Generation. Transmission Distrib 14(26):6690–6702

    Article  Google Scholar 

  44. Wang D, Chen F, Meng B, Hu X, Wang J (2021) Event-based secure H\(_{\infty }\) load frequency control for delayed power systems subject to deception attacks. Appl Math Computat 394:125788

    Article  MathSciNet  MATH  Google Scholar 

  45. Xu X, Niu D, Peng L, Zheng S, Qiu J (2022) Hierarchical multi-objective optimal planning model of active distribution network considering distributed generation and demand-side response. Sustainable Energy Technol Assessments 53:102438

    Article  Google Scholar 

  46. Davari M, Aghababa MP, Blaabjerg F, Saif M (2021) An innovative, adaptive faulty signal rectifier along with a switching controller for reliable primary control of GC-VSIs in CPS-based modernized microgrids. IEEE Trans Power Electr 36(7):8370–8387

    Article  Google Scholar 

  47. Zhong C, Zhou Y, Chen J, Liu Z (2022) Dc-side synchronous active power control of two-stage photovoltaic generation for frequency support in islanded microgrids. Energy Rep 8:8361–8371

    Article  Google Scholar 

  48. Gershon E, Shaked U, Yaesh I (2005) H-infinity control estimation of state-multiplicative linear systems vol 318, Springer science & business media

  49. Liu H, Zhao F, Chen X, Hou H, Qiu J (2022) Observer-based finite-time \(h_{\infty }\) sliding mode control of stochastic nonlinear singular systems and its applications. Nonlinear Dynamics:1–10

  50. Zhou K, Oh S-K, Qiu J (2022) Design of ensemble fuzzy-rbf neural networks based on feature extraction and multi-feature fusion for gis partial discharge recognition and classification. J Electr Eng Technol 17(1):513–532

    Article  Google Scholar 

  51. Gholami S, Saha S, Aldeen M (2018) Fault tolerant control of electronically coupled distributed energy resources in microgrid systems. Int J Electr Power Energy Syst 95:327–340

    Article  Google Scholar 

  52. Davari M, Aghababa MP, Blaabjerg F, Saif M (2020) An innovative, adaptive faulty signal rectifier along with a switching controller for reliable primary control of GC-VSIs in CPS-based modernized microgrids. IEEE Trans Power Electr 36(7):8370–8387

    Article  Google Scholar 

Download references

Acknowledgements

This work was financially supported by the Key project of the National Social Science Fundation (18AJY013); The Planning Fund Project of Humanities and Social Sciences Research of the Ministry of Education(19YJA790102); the Fujian Social Science Planning Project(FJ2021MJDZ045;FJ2020MJDZ052).

Author information

Authors and Affiliations

  1. School of Business Administration, Hunan University, Changsha, 410082, China

    Shu-Rong Yan

  2. School of Credit Management, Guangdong University of Finance, Guangzhou, 510521, China

    Wei Guo

  3. Multidisciplinary Center for Infrastructure Engineering, Shenyang University of Technology, Shenyang, 110870, China

    Ardashir Mohammadzadeh

  4. Department of Applied Mathematics, Bharathiar University, Coimbatore, 641-046, India

    Sakthivel Rathinasamy

Authors
  1. Shu-Rong Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ardashir Mohammadzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sakthivel Rathinasamy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ardashir Mohammadzadeh.

Ethics declarations

Conflict of Interests

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yan, SR., Guo, W., Mohammadzadeh, A. et al. Optimal deep learning control for modernized microgrids. Appl Intell 53, 15638–15655 (2023). https://doi.org/10.1007/s10489-022-04298-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-022-04298-2

Keywords

Search

Navigation