1932

Abstract

Power grids are critical infrastructure in modern society, and there are well-established theories for the stability and control of traditional power grids under a centralized paradigm. Driven by environmental and sustainability concerns, power grids are undergoing an unprecedented transition, with much more flexibility as well as uncertainty brought by the growing penetration of renewable energy and power electronic devices. A new paradigm for stability and control is under development that uses graph-based, data-based, and distributed analysis tools. This article surveys classic and novel results on the stability and control of power grids to provide a perspective on this both old and new subject.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-control-042820-011148
2022-05-03
2024-06-23
Loading full text...

Full text loading...

/deliver/fulltext/control/5/1/annurev-control-042820-011148.html?itemId=/content/journals/10.1146/annurev-control-042820-011148&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    Rocabert J, Luna A, Blaabjerg F, Rodriguez P. 2012. Control of power converters in AC microgrids. IEEE Trans. Power Electron. 27:4734–49
    [Google Scholar]
  2. 2. 
    Kundur P, Paserba J, Ajjarapu V, Andersson G, Bose A et al. 2004. Definition and classification of power system stability. IEEE Trans. Power Syst. 19:1387–401
    [Google Scholar]
  3. 3. 
    Vorotnikov VI. 2012. Partial Stability and Control Basel, Switz: Birkhäuser
    [Google Scholar]
  4. 4. 
    Sastry S, Varaiya PP. 1980. Hierarchical stability and alert state steering control of interconnected power systems. IEEE Trans. Circuits Syst. 27:1102–12
    [Google Scholar]
  5. 5. 
    Chiang HD. 1989. Study of the existence of energy functions for power systems with losses. IEEE Trans. Circuits Syst. 36:1423–29
    [Google Scholar]
  6. 6. 
    Bergen AR, Hill DJ. 1981. A structure preserving model for power system stability analysis. IEEE Trans. Power App. Syst. PAS-100:25–35
    [Google Scholar]
  7. 7. 
    Motter AE, Myers SA, Anghel M, Nishikawa T 2013. Spontaneous synchrony in power-grid networks. Nat. Phys. 9:191–97
    [Google Scholar]
  8. 8. 
    Pai MA. 1989. Energy Function Analysis for Power System Stability Boston: Kluwer Acad.
    [Google Scholar]
  9. 9. 
    Padiyar K. 2013. Structure Preserving Energy Functions in Power Systems: Theory and Applications Boca Raton, FL: CRC
    [Google Scholar]
  10. 10. 
    Hill DJ, Hiskens IA, Popovic DH. 1994. Stability analysis of power system loads with recovery dynamics. Int. J. Electr. Power Energy Syst. 16:277–86
    [Google Scholar]
  11. 11. 
    Van Cutsem T, Vournas C. 1998. Voltage Stability of Electric Power Systems Boston: Kluwer Acad.
    [Google Scholar]
  12. 12. 
    Hatziargyriou N, Milanovic JV, Rahmann C, Ajjarapu V, Canizares C et al. 2020. Definition and classification of power system stability – revisited & extended. IEEE Trans. Power Syst. 36:3271–81
    [Google Scholar]
  13. 13. 
    Wang X, Blaabjerg F 2019. Harmonic stability in power electronic-based power systems: concept, modeling, and analysis. IEEE Trans. Smart Grid 10:2858–70
    [Google Scholar]
  14. 14. 
    Schiffer J, Zonetti D, Ortega R, Stanković A, Sezi T, Raisch J. 2016. A survey on modeling of microgrids—from fundamental physics to phasors and voltage sources. Automatica 74:135–50
    [Google Scholar]
  15. 15. 
    Vorobev P, Huang PH, Al Hosani M, Kirtley JL, Turitsyn K 2018. High-fidelity model order reduction for microgrids stability assessment. IEEE Trans. Power Syst. 33:874–87
    [Google Scholar]
  16. 16. 
    Magnusson PC. 1947. The transient-energy method of calculating stability. AIEE Trans 66:747–55
    [Google Scholar]
  17. 17. 
    Chiang HD. 2011. Direct Methods for Stability Analysis of Electric Power Systems: Theoretical Foundation, BCU Methodologies, and Applications Hoboken, NJ: Wiley & Sons
    [Google Scholar]
  18. 18. 
    Xue Y, Van Custem T, Ribbens-Pavella M. 1989. Extended equal area criterion justifications, generalizations, applications. IEEE Trans. Power Syst. 4:44–52
    [Google Scholar]
  19. 19. 
    Venkatasubramanian V, Schättler H, Zaborszky J. 1995. Local bifurcations and feasibility regions in differential-algebraic systems. IEEE Trans. Autom. Control 40:1992–2013
    [Google Scholar]
  20. 20. 
    Avalos RJ, Cañizares C, Milano F, Conejo AJ 2009. Equivalency of continuation and optimization methods to determine saddle-node and limit-induced bifurcations in power systems. IEEE Trans. Circuits Syst. I 56:210–23
    [Google Scholar]
  21. 21. 
    Ajjarapu V, Christy C. 1992. The continuation power flow: a tool for steady state voltage stability analysis. IEEE Trans. Power Syst. 7:416–23
    [Google Scholar]
  22. 22. 
    Cañizares C, Alvarado FL. 1993. Point of collapse and continuation methods for large AC/DC systems. IEEE Trans. Power Syst. 8:1–8
    [Google Scholar]
  23. 23. 
    Nguyen TB, Pai MA. 2003. Dynamic security-constrained rescheduling of power systems using trajectory sensitivities. IEEE Trans. Power Syst. 18:848–54
    [Google Scholar]
  24. 24. 
    Hou G, Vittal V. 2013. Determination of transient stability constrained interface real power flow limit using trajectory sensitivity approach. IEEE Trans. Power Syst. 28:2156–63
    [Google Scholar]
  25. 25. 
    Wang XF, Chen G. 2003. Complex networks: small-world, scale-free and beyond. IEEE Circuits Syst. Mag. 3:16–20
    [Google Scholar]
  26. 26. 
    Hill DJ, Chen G. 2006. Power systems as dynamic networks. 2006 IEEE International Symposium on Circuits and Systems722–25 Piscataway, NJ: IEEE
    [Google Scholar]
  27. 27. 
    Araposthatis A, Sastry S, Varaiya PP. 1981. Analysis of power-flow equation. Int. J. Electr. Power Energy Syst. 3:115–26
    [Google Scholar]
  28. 28. 
    Hill DJ, Bergen AR. 1982. Stability analysis of multimachine power networks with linear frequency dependent loads. IEEE Trans. Circuits Syst. 29:840–48
    [Google Scholar]
  29. 29. 
    Bapat RB. 2011. Graphs and Matrices London: Springer
    [Google Scholar]
  30. 30. 
    Olfati-Saber R, Fax JA, Murray RM. 2007. Consensus and cooperation in networked multi-agent systems. Proc. IEEE 95:215–33
    [Google Scholar]
  31. 31. 
    Zelazo D, Bürger M. 2017. On the robustness of uncertain consensus networks. IEEE Trans. Control Netw. Syst. 4:170–78
    [Google Scholar]
  32. 32. 
    Dörfler F, Chertkov M, Bullo F 2013. Synchronization in complex oscillator networks and smart grids. PNAS 110:2005–10
    [Google Scholar]
  33. 33. 
    Jafarpour S, Bullo F 2019. Synchronization of Kuramoto oscillators via cutset projections. IEEE Trans. Autom. Control 64:2830–44
    [Google Scholar]
  34. 34. 
    Dörfler F, Bullo F. 2011. Topological equivalence of a structure-preserving power network model and a non-uniform Kuramoto model of coupled oscillators. 2011 50th IEEE Conference on Decision and Control and European Control Conference7099–104 Piscataway, NJ: IEEE
    [Google Scholar]
  35. 35. 
    Song Y, Hill DJ, Liu T. 2019. On extension of effective resistance with application to graph Laplacian definiteness and power network stability. IEEE Trans. Circuits Syst. I 66:4415–28
    [Google Scholar]
  36. 36. 
    Song Y, Hill DJ, Liu T. 2020. Network-based analysis of rotor angle stability of power systems. Found. Trends Electr. Energy Syst. 4:222–345
    [Google Scholar]
  37. 37. 
    Song Y, Hill DJ, Liu T. 2018. Characterization of cutsets in networks with application to transient stability analysis of power systems. IEEE Trans. Control Netw. Syst. 5:1261–74
    [Google Scholar]
  38. 38. 
    Song Y, Hill DJ, Liu T. 2018. Network-based analysis of small-disturbance angle stability of power systems. IEEE Trans. Control Netw. Syst. 5:901–12
    [Google Scholar]
  39. 39. 
    Chen W, Wang D, Liu J, Chen Y, Khong SZ et al. 2021. On spectral properties of signed Laplacians with connections to eventual positivity. IEEE Trans. Autom. Control 66:2177–90
    [Google Scholar]
  40. 40. 
    Dörfler F, Bullo F. 2012. Synchronization and transient stability in power networks and nonuniform Kuramoto oscillators. SIAM J. Control Optim. 50:1616–42
    [Google Scholar]
  41. 41. 
    Zhu L, Hill DJ. 2018. Stability analysis of power systems: a network synchronization perspective. SIAM J. Control Optim. 56:1640–64
    [Google Scholar]
  42. 42. 
    Wu L, Pota HR, Petersen IR. 2019. Synchronization conditions for a multirate Kuramoto network with an arbitrary topology and nonidentical oscillators. IEEE Trans. Cybern. 49:2242–54
    [Google Scholar]
  43. 43. 
    Dörfler F, Bullo F. 2010. Synchronization of power networks: network reduction and effective resistance. IFAC Proc. Vol. 43:19197–202
    [Google Scholar]
  44. 44. 
    Simpson-Porco JW. 2018. A theory of solvability for lossless power flow equations—part I: fixed-point power flow. IEEE Trans. Control Netw. Syst. 5:1361–72
    [Google Scholar]
  45. 45. 
    Wang C, Bernstein A, Le Boudec JY, Paolone M 2018. Explicit conditions on existence and uniqueness of load-flow solutions in distribution networks. IEEE Trans. Smart Grid 9:953–62
    [Google Scholar]
  46. 46. 
    Bernstein A, Wang C, Dall'Anese E, Le Boudec JY, Zhao C 2018. Load flow in multiphase distribution networks: existence, uniqueness, non-singularity and linear models. IEEE Trans. Power Syst. 33:5832–43
    [Google Scholar]
  47. 47. 
    Nguyen HD, Dvijotham K, Yu S, Turitsyn K 2019. A framework for robust long-term voltage stability of distribution systems. IEEE Trans. Smart Grid 10:4827–37
    [Google Scholar]
  48. 48. 
    Wang Z, Cui B, Wang J 2017. A necessary condition for power flow insolvability in power distribution systems with distributed generators. IEEE Trans. Power Syst. 32:1440–50
    [Google Scholar]
  49. 49. 
    Song Y, Hill DJ, Liu T. 2019. Static voltage stability analysis of distribution systems based on network-load admittance ratio. IEEE Trans. Power Syst. 34:2270–80
    [Google Scholar]
  50. 50. 
    Lasseter R, Akhil A, Marnay C, Stephens J, Dagle J et al. 2002. The CERTS microgrid concept White Pap., Transm. Reliab. Program, Off. Power Technol. US Dep. Energy Washington, DC:
    [Google Scholar]
  51. 51. 
    Farrokhabadi M, Cañizares CA, Simpson-Porco JW, Nasr E, Fan L et al. 2020. Microgrid stability definitions, analysis, and examples. IEEE Trans. Power Syst. 35:13–29
    [Google Scholar]
  52. 52. 
    Simpson-Porco JW, Dörfler F, Bullo F 2013. Synchronization and power sharing for droop-controlled inverters in islanded microgrids. Automatica 49:2603–11
    [Google Scholar]
  53. 53. 
    Colombino M, Groß D, Brouillon JS, Dörfler F. 2019. Global phase and magnitude synchronization of coupled oscillators with application to the control of grid-forming power inverters. IEEE Trans. Autom. Control 64:4496–511
    [Google Scholar]
  54. 54. 
    Song Y, Hill DJ, Liu T. 2019. Impact of DG connection topology on the stability of inverter-based microgrids. IEEE Trans. Power Syst. 34:3970–72
    [Google Scholar]
  55. 55. 
    Song Y, Hill DJ, Liu T, Zheng Y. 2017. A distributed framework for stability evaluation and enhancement of inverter-based microgrids. IEEE Trans. Smart Grid 8:3020–34
    [Google Scholar]
  56. 56. 
    Yang P, Liu F, Wang Z, Shen C 2020. Distributed stability conditions for power systems with heterogeneous nonlinear bus dynamics. IEEE Trans. Power Syst. 35:2313–24
    [Google Scholar]
  57. 57. 
    Zhang Y, Xie L. 2015. Online dynamic security assessment of microgrid interconnections in smart distribution systems. IEEE Trans. Power Syst. 30:3246–54
    [Google Scholar]
  58. 58. 
    Nandanoori SP, Kundu S, Du W, Tuffner FK, Schneider KP. 2020. Distributed small-signal stability conditions for inverter-based unbalanced microgrids. IEEE Trans. Power Syst. 35:3981–90
    [Google Scholar]
  59. 59. 
    Baros S, Bernstein A, Hatziargyriou ND. 2021. Distributed conditions for small-signal stability of power grids and local control design. IEEE Trans. Power Syst. 36:2058–67
    [Google Scholar]
  60. 60. 
    Milano F, Dörfler F, Hug G, Hill DJ, Verbič G. 2018. 2018 Power Systems Computation Conference Piscataway, NJ: IEEE. https://doi.org/10.23919/PSCC.2018.8450880
    [Google Scholar]
  61. 61. 
    Zhu L, Hill DJ. 2020. Synchronization of Kuramoto oscillators: a regional stability framework. IEEE Trans. Autom. Control 65:5070–82
    [Google Scholar]
  62. 62. 
    Lu Q, Sun Y, Mei S 2001. Nonlinear Control Systems and Power System Dynamics New York: Springer
    [Google Scholar]
  63. 63. 
    Ruiz-Vega D, Pavella M. 2003. A comprehensive approach to transient stability control. I. Near optimal preventive control. IEEE Trans. Power Syst. 18:1446–53
    [Google Scholar]
  64. 64. 
    Ruiz-Vega D, Pavella M. 2003. A comprehensive approach to transient stability control. II. Open loop emergency control. IEEE Trans. Power Syst. 18:1454–60
    [Google Scholar]
  65. 65. 
    Bhui P, Senroy N. 2017. Real-time prediction and control of transient stability using transient energy function. IEEE Trans. Power Syst. 32:923–34
    [Google Scholar]
  66. 66. 
    Kamali S, Amraee T, Fotuhi-Firuzabad M. 2021. Controlled islanding for enhancing grid resilience against power system blackout. IEEE Trans. Power Deliv. 36:2386–96
    [Google Scholar]
  67. 67. 
    Kundur P. 1994. Power Systems Stability and Control New York: McGraw-Hill
    [Google Scholar]
  68. 68. 
    Wood AJ, Wollenberg BF, Sheblé GB. 2013. Power Generation, Operation, and Control Hoboken, NJ: Wiley, 3rd ed..
    [Google Scholar]
  69. 69. 
    Hill DJ, Liu T, Verbic G. 2012. Smart grids as distributed learning control. 2012 IEEE Power and Energy Society General Meeting Piscataway, NJ: IEEE. https://doi.org/10.1109/PESGM.2012.6344726
    [Google Scholar]
  70. 70. 
    Ulbig A, Borsche TS, Andersson G. 2014. Impact of low rotational inertia on power system stability and operation. IFAC Proc. Vol. 47:37290–97
    [Google Scholar]
  71. 71. 
    Paolone M, Gaunt T, Guillaud X, Liserre M, Meliopoulos S, Monti A et al. 2020. Fundamentals of power systems modelling in the presence of converter-interfaced generation. Electr. Power Syst. Res. 189:106811
    [Google Scholar]
  72. 72. 
    Yang L, Liu T, Hill D. 2021. Decentralized event-triggered frequency regulation for multi-area power systems. Automatica 126:109479
    [Google Scholar]
  73. 73. 
    Andreasson M, Dimarogonas DV, Sandberg H, Johansson KH. 2014. Distributed PI-control with applications to power systems frequency control. 2014 American Control Conference3183–88 Piscataway, NJ: IEEE
    [Google Scholar]
  74. 74. 
    Lyu X, Jia Y, Liu T, Chai S. 2021. System-oriented power regulation scheme for wind farms: the quest for uncertainty management. IEEE Trans. Power Syst. 36:4259–69
    [Google Scholar]
  75. 75. 
    Serban I, Marinescu C. 2014. Control strategy of three-phase battery energy storage systems for frequency support in microgrids and with uninterrupted supply of local loads. IEEE Trans. Power Electron. 29:5010–20
    [Google Scholar]
  76. 76. 
    Mégel O, Liu T, Hill DJ, Andersson G. 2018. Distributed secondary frequency control algorithm considering storage efficiency. IEEE Trans. Smart Grid 9:6214–28
    [Google Scholar]
  77. 77. 
    Callaway DS, Hiskens IA. 2011. Achieving controllability of electric loads. Proc. IEEE 99:184–99
    [Google Scholar]
  78. 78. 
    Lee CK, Chaudhuri NR, Chaudhuri B, Hui SR 2013. Droop control of distributed electric springs for stabilizing future power grid. IEEE Trans. Smart Grid 4:1558–66
    [Google Scholar]
  79. 79. 
    Halevi Y, Kottick D. 1993. Optimization of load shedding system. IEEE Trans. Energy Convers. 8:207–13
    [Google Scholar]
  80. 80. 
    Liu T, Hill DJ, Zhang C. 2016. Non-disruptive load-side control for frequency regulation in power systems. IEEE Trans. Smart Grid 7:2142–53
    [Google Scholar]
  81. 81. 
    Zhang C, Liu T, Hill DJ. 2018. Granular optimal load-side control of power systems with electric spring aggregators. arXiv:1806.03679 [math.OC]
  82. 82. 
    Zhang X, Hill DJ, Lu C. 2020. Identification of composite demand side model with distributed photovoltaic generation and energy storage. IEEE Trans. Sustain. Energy 11:326–36
    [Google Scholar]
  83. 83. 
    Savulescu SC. 2014. Real-Time Stability in Power Systems: Techniques for Early Detection of the Risk of Blackout Cham, Switz: Springer
    [Google Scholar]
  84. 84. 
    Fusco G, Russo M. 2006. Adaptive Voltage Control in Power Systems: Modeling, Design and Applications London: Springer
    [Google Scholar]
  85. 85. 
    Corsi S. 2015. Voltage Control and Protection in Electrical Power Systems: From System Components to Wide-Area Control London: Springer
    [Google Scholar]
  86. 86. 
    Van Cutsem T, Vournas C. 2007. Emergency voltage stability controls: an overview. 2007 IEEE Power Engineering Society General Meeting Piscataway, NJ: IEEE. https://doi.org/10.1109/PES.2007.386089
    [Google Scholar]
  87. 87. 
    Sun H, Guo Q, Qi J, Ajjarapu V, Bravo R et al. 2019. Review of challenges and research opportunities for voltage control in smart grids. IEEE Trans. Power Syst. 34:2790–801
    [Google Scholar]
  88. 88. 
    Baghsorkhi SS, Hiskens IA. 2012. Impact of wind power variability on sub-transmission networks. 2012 IEEE Power and Energy Society General Meeting Piscataway, NJ: IEEE. https://doi.org/10.1109/PESGM.2012.6345683
    [Google Scholar]
  89. 89. 
    Sarimuthu CR, Ramachandaramurthy VK, Agileswari K, Mokhlis H 2016. A review on voltage control methods using on-load tap changer transformers for networks with renewable energy sources. Renew. Sust. Energy Rev. 62:1154–61
    [Google Scholar]
  90. 90. 
    Mahmud N, Zahedi A 2016. Review of control strategies for voltage regulation of the smart distribution network with high penetration of renewable distributed generation. Renew. Sust. Energy Rev. 64:582–95
    [Google Scholar]
  91. 91. 
    Senjyu T, Miyazato Y, Yona A, Urasaki N, Funabashi T. 2008. Optimal distribution voltage control and coordination with distributed generation. IEEE Trans. Power Deliv. 23:1236–42
    [Google Scholar]
  92. 92. 
    Tang Z, Hill DJ, Liu T, Ma H. 2018. Hierarchical voltage control of weak subtransmission networks with high penetration of wind power. IEEE Trans. Power Syst. 33:187–97
    [Google Scholar]
  93. 93. 
    Tang Z, Hill DJ, Liu T. 2020. Distributed control of active distribution networks to support voltage control in subtransmission networks. Int. J. Electr. Power Energy Syst. 117:105715
    [Google Scholar]
  94. 94. 
    Song Y, Zheng Y, Liu T, Lei S, Hill DJ 2020. A new formulation of distribution network reconfiguration for reducing the voltage volatility induced by distributed generation. IEEE Trans. Power Syst. 35:496–507
    [Google Scholar]
  95. 95. 
    Bidram A, Davoudi A. 2012. Hierarchical structure of microgrids control system. IEEE Trans. Smart Grid 3:1963–76
    [Google Scholar]
  96. 96. 
    Chen F, Chen M, Li Q, Meng K, Zheng Y et al. 2017. Cost-based droop schemes for economic dispatch in islanded microgrids. IEEE Trans. Smart Grid 8:63–74
    [Google Scholar]
  97. 97. 
    Schiffer J, Ortega R, Astolfi A, Raisch J, Sezi T. 2014. Conditions for stability of droop-controlled inverter-based microgrids. Automatica 50:2457–69
    [Google Scholar]
  98. 98. 
    Barklund E, Pogaku N, Prodanovic M, Hernandez-Aramburo C, Green TC. 2008. Energy management in autonomous microgrid using stability-constrained droop control of inverters. IEEE Trans. Power Electron. 23:2346–52
    [Google Scholar]
  99. 99. 
    Hatziargyriou N. 2013. Microgrids: Architectures and Control Chichester, UK: Wiley & Sons
    [Google Scholar]
  100. 100. 
    Farrokhabadi M, Cañizares CA, Bhattacharya K. 2015. Frequency control in isolated/islanded microgrids through voltage regulation. IEEE Trans. Smart Grid 8:1185–94
    [Google Scholar]
  101. 101. 
    Vandoorn T, De Kooning J, Meersman B, Vandevelde L 2013. Review of primary control strategies for islanded microgrids with power-electronic interfaces. Renew. Sust. Energy Rev. 19:613–28
    [Google Scholar]
  102. 102. 
    Dörfler F, Simpson-Porco JW, Bullo F. 2014. Plug-and-play control and optimization in microgrids. 53rd IEEE Conference on Decision and Control211–16 Piscataway, NJ: IEEE
    [Google Scholar]
  103. 103. 
    Hug G, Kar S, Wu C 2015. Consensus plus innovations approach for distributed multiagent coordination in a microgrid. IEEE Trans. Smart Grid 6:1893–903
    [Google Scholar]
  104. 104. 
    Dörfler F, Simpson-Porco JW, Bullo F. 2016. Breaking the hierarchy: distributed control and economic optimality in microgrids. IEEE Trans. Control Netw. Syst. 3:241–53
    [Google Scholar]
  105. 105. 
    Ulbig A, Andersson G. 2015. Analyzing operational flexibility of electric power systems. Int. J. Electr. Power Energy Syst. 72:155–64
    [Google Scholar]
  106. 106. 
    Wang Z, Chen B, Wang J, Chen C 2016. Networked microgrids for self-healing power systems. IEEE Trans. Smart Grid 7:310–19
    [Google Scholar]
  107. 107. 
    Wang H, Huang J 2018. Incentivizing energy trading for interconnected microgrids. IEEE Trans. Smart Grid 9:2647–57
    [Google Scholar]
  108. 108. 
    Zhang Y, Xie L, Ding Q. 2016. Interactive control of coupled microgrids for guaranteed system-wide small signal stability. IEEE Trans. Smart Grid 7:1088–96
    [Google Scholar]
  109. 109. 
    Liu K, Liu T, Hill D. 2017. Frequency control in networked microgrids with voltage-sensitive loads. Paper presented at the 10th Bulk Power Systems Dynamics and Control Symposium Espinho, Port.: Aug. 27–Sept. 1
    [Google Scholar]
  110. 110. 
    GridWise Archit. Counc 2015. GridWise transactive energy framework version 1.0. Tech. Rep. GridWise Archit. Counc. Richland, WA:
    [Google Scholar]
  111. 111. 
    Phadke AG, Thorp JS. 2017. Synchronized Phasor Measurements and Their Applications Cham, Switz: Springer, 2nd ed..
    [Google Scholar]
  112. 112. 
    Goodfellow I, Bengio Y, Courville A. 2016. Deep Learning Cambridge, MA: MIT Press
    [Google Scholar]
  113. 113. 
    Bishop CM. 2006. Pattern Recognition and Machine Learning New York: Springer
    [Google Scholar]
  114. 114. 
    Sutton RS, Barto AG. 2018. Reinforcement Learning: An Introduction Cambridge, MA: MIT Press
    [Google Scholar]
  115. 115. 
    He M, Zhang J, Vittal V. 2013. Robust online dynamic security assessment using adaptive ensemble decision-tree learning. IEEE Trans. Power Syst. 28:4089–98
    [Google Scholar]
  116. 116. 
    Wang B, Fang B, Wang Y, Liu H, Liu Y. 2016. Power system transient stability assessment based on big data and the core vector machine. IEEE Trans. Smart Grid 7:2561–70
    [Google Scholar]
  117. 117. 
    Zhang Y, Xu Y, Dong Z, Zhang R 2019. A hierarchical self-adaptive data-analytics method for power system short-term voltage stability assessment. IEEE Trans. Ind. Inform. 15:74–84
    [Google Scholar]
  118. 118. 
    Zhang Y, Xu Y, Zhang R, Dong ZY. 2019. A missing-data tolerant method for data-driven short-term voltage stability assessment of power systems. IEEE Trans. Smart Grid 10:5663–74
    [Google Scholar]
  119. 119. 
    Malbasa V, Zheng C, Chen PC, Popovic T, Kezunovic M. 2017. Voltage stability prediction using active machine learning. IEEE Trans. Smart Grid 8:3117–24
    [Google Scholar]
  120. 120. 
    Li S, Ajjarapu V, Djukanovic M 2018. Adaptive online monitoring of voltage stability margin via local regression. IEEE Trans. Power Syst. 33:701–13
    [Google Scholar]
  121. 121. 
    Wang Q, Li F, Tang Y, Xu Y. 2019. Integrating model-driven and data-driven methods for power system frequency stability assessment and control. IEEE Trans. Power Syst. 34:4557–68
    [Google Scholar]
  122. 122. 
    Zhu L, Hill DJ, Lu C. 2020. Hierarchical deep learning machine for power system online transient stability prediction. IEEE Trans. Power Syst. 35:2399–411
    [Google Scholar]
  123. 123. 
    Yu JQ, Hill DJ, Lam AYS, Gu J, Li VOK. 2018. Intelligent time-adaptive transient stability assessment system. IEEE Trans. Power Syst. 33:1049–58
    [Google Scholar]
  124. 124. 
    Zhu L, Lu C, Sun Y. 2016. Time series shapelet classification based online short-term voltage stability assessment. IEEE Trans. Power Syst. 31:1430–39
    [Google Scholar]
  125. 125. 
    Zhu L, Lu C, Dong ZY, Hong C. 2017. Imbalance learning machine-based power system short-term voltage stability assessment. IEEE Trans. Ind. Inform. 13:2533–43
    [Google Scholar]
  126. 126. 
    Xie S, Lu C, Zhu L, Zhang J. 2018. Online long-term voltage stability assessment based on time series shapelet extraction. 2018 IEEE Innovative Smart Grid Technologies - Asia1153–58 Piscataway, NJ: IEEE
    [Google Scholar]
  127. 127. 
    Ye L, Keogh E 2011. Time series shapelets: a novel technique that allows accurate, interpretable and fast classification. Data Min. Knowl. Discov. 22:149–82
    [Google Scholar]
  128. 128. 
    Huang J, Guan L, Su Y, Yao H, Guo M, Zhong Z 2020. Recurrent graph convolutional network-based multi-task transient stability assessment framework in power system. IEEE Access 8:93283–96
    [Google Scholar]
  129. 129. 
    Luo Y, Lu C, Zhu L, Song J 2021. Data-driven short-term voltage stability assessment based on spatial-temporal graph convolutional network. Int. J. Electr. Power Energy Syst. 130:106753
    [Google Scholar]
  130. 130. 
    Zhu L, Hill DJ, Lu C. 2021. Intelligent short-term voltage stability assessment via spatial attention rectified RNN learning. IEEE Trans. Ind. Inform. 17:7005–16
    [Google Scholar]
  131. 131. 
    Zhu L, Lu C, Kamwa I, Zeng H 2020. Spatial-temporal feature learning in smart grids: a case study on short-term voltage stability assessment. IEEE Trans. Ind. Inform. 16:1470–82
    [Google Scholar]
  132. 132. 
    Genc I, Diao R, Vittal V, Kolluri S, Mandal S 2010. Decision tree-based preventive and corrective control applications for dynamic security enhancement in power systems. IEEE Trans. Power Syst. 25:1611–19
    [Google Scholar]
  133. 133. 
    Tian F, Zhou X, Yu Z, Shi D, Chen Y, Huang Y 2019. A preventive transient stability control method based on support vector machine. Electr. Power Syst. Res. 170:286–93
    [Google Scholar]
  134. 134. 
    Cai H, Ma H, Hill DJ 2020. A data-based learning and control method for long-term voltage stability. IEEE Trans. Power Syst. 35:3203–12
    [Google Scholar]
  135. 135. 
    Zhu L, Luo Y. 2021. Deep feedback learning based predictive control for power system undervoltage load shedding. IEEE Trans. Power Syst. 36:3349–61
    [Google Scholar]
  136. 136. 
    Huang Q, Huang R, Hao W, Tan J, Fan R, Huang Z 2020. Adaptive power system emergency control using deep reinforcement learning. IEEE Trans. Smart Grid 11:1171–82
    [Google Scholar]
  137. 137. 
    Wang W, Yu N, Gao Y, Shi J 2020. Safe off-policy deep reinforcement learning algorithm for Volt-VAR control in power distribution systems. IEEE Trans. Smart Grid 11:3008–18
    [Google Scholar]
  138. 138. 
    Yan Z, Xu Y 2020. A multi-agent deep reinforcement learning method for cooperative load frequency control of a multi-area power system. IEEE Trans. Power Syst. 35:4599–608
    [Google Scholar]
  139. 139. 
    Duan J, Xu H, Liu W. 2018. Q-learning-based damping control of wide-area power systems under cyber uncertainties. IEEE Trans. Smart Grid 9:6408–18
    [Google Scholar]
  140. 140. 
    Schrittwieser J, Antonoglou I, Hubert T, Simonyan K, Sifre L et al. 2020. Mastering Atari, Go, chess and shogi by planning with a learned model. Nature 588:604–9
    [Google Scholar]
  141. 141. 
    Ma H, Hill DJ. 2014. Adaptive coordinated voltage control—part I: basic scheme. IEEE Trans. Power Syst. 29:1546–53
    [Google Scholar]
  142. 142. 
    Ma H, Hill DJ 2014. Adaptive coordinated voltage control—part II: use of learning for rapid response. IEEE Trans. Power Syst. 29:1554–61
    [Google Scholar]
  143. 143. 
    Shen Y, Yao W, Wen J, He H, Jiang L 2019. Resilient wide-area damping control using GrHDP to tolerate communication failures. IEEE Trans. Smart Grid 10:2547–57
    [Google Scholar]
  144. 144. 
    Duchesne L, Karangelos E, Wehenkel L 2020. Recent developments in machine learning for energy systems reliability management. Proc. IEEE 108:1656–76
    [Google Scholar]
  145. 145. 
    Glavic M. 2019. Deep) reinforcement learning for electric power system control and related problems: a short review and perspectives. Annu. Rev. Control 48:22–35
    [Google Scholar]
  146. 146. 
    Shi Z, Yao W, Li Z, Zeng L, Zhao Y et al. 2020. Artificial intelligence techniques for stability analysis and control in smart grids: methodologies, applications, challenges and future directions. Appl. Energy 278:115733
    [Google Scholar]
  147. 147. 
    Yang Q, Liu Y, Chen T, Tong Y 2019. Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. 10:12
    [Google Scholar]
/content/journals/10.1146/annurev-control-042820-011148
Loading
/content/journals/10.1146/annurev-control-042820-011148
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error