World's Best Scientists 2026 revealed!
Michael Negnevitsky

Michael Negnevitsky

D-Index & Metrics

Engineering and Technology

D-Index
49
Citations
15089
World Ranking
4216
National Ranking
229

Michael Negnevitsky publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Michael Negnevitsky sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 463 publications — 93rd percentile

93% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Michael Negnevitsky D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Michael Negnevitsky sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 49 D-Index — 57th percentile

57% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Michael Negnevitsky is affiliated with the University of Tasmania in Australia and specializes in the field of Engineering, with a focus on Electrical and Electronic Engineering, Control and Systems Engineering, Mechanical Engineering, Automotive Engineering, and Renewable Energy, Sustainability and the Environment.

Their recent research encompasses several key topics, including Microgrid Control and Optimization, Electric Vehicles and Infrastructure, Advanced Battery Technologies Research, Smart Grid Energy Management, Biodiesel Production and Applications, Hybrid Renewable Energy Systems, and Phase Change Materials Research.

Michael Negnevitsky has published extensively in various academic venues. The more frequent publication outlets include:

  • Energies
  • Energy
  • IEEE Transactions on Industry Applications
  • Energy Conversion and Management
  • Applied Thermal Engineering

Their recent papers cover a range of topics mainly related to battery technologies and thermal management systems for electric vehicles, as well as applications of artificial intelligence in power systems. Selected recent publications include:

  • "A review of air-cooling battery thermal management systems for electric and hybrid electric vehicles" (2021, Journal of Power Sources)
  • "An up-to-date review on the design improvement and optimization of the liquid-cooling battery thermal management system for electric vehicles" (2022, Applied Thermal Engineering)
  • "Connecting battery technologies for electric vehicles from battery materials to management" (2022, iScience)
  • "Review of AI applications in harmonic analysis in power systems" (2021, Renewable and Sustainable Energy Reviews)
  • "Melting and solidification characteristics of a semi-rotational eccentric tube horizontal latent heat thermal energy storage" (2022, Applied Thermal Engineering)

Frequent coauthors collaborating with Michael Negnevitsky include Xiaolin Wang, Chengjiang Li, Evan Franklin, Gang Zhao, and Nishant Modi.

Best Publications

  • Artificial Intelligence: A Guide to Intelligent Systems

    Michael Negnevitsky

  • A Novel Control Strategy for a Variable-Speed Wind Turbine With a Permanent-Magnet Synchronous Generator

    M.E. Haque;M. Negnevitsky;K.M. Muttaqi

  • Very short-term wind forecasting for Tasmanian power generation

    C.W. Potter;M. Negnevitsky

  • A review of air-cooling battery thermal management systems for electric and hybrid electric vehicles

    Gang Zhao;Xiaolin Wang;Michael Negnevitsky;Hengyun Zhang

  • An up-to-date review on the design improvement and optimization of the liquid-cooling battery thermal management system for electric vehicles

    Unknown

  • Pool-Based Demand Response Exchange—Concept and Modeling

    Duy Thanh Nguyen;Michael Negnevitsky;Martin de Groot

  • Distributed generation for minimization of power losses in distribution systems

    M.A. Kashem;A.D.T. Le;M. Negnevitsky;G. Ledwich

  • Connecting battery technologies for electric vehicles from battery materials to management

    Unknown

  • A Novel Operation and Control Strategy for a Standalone Hybrid Renewable Power System

    A. M. O. Haruni;M. Negnevitsky;M. E. Haque;A. Gargoom

  • A Neural-Fuzzy Classifier for Recognition of Power Quality Disturbances

    Jiansheng Huang;M. Negnevitsky;D.T. Nguyen

  • A Coordinated Voltage Control Approach for Coordination of OLTC, Voltage Regulator, and DG to Regulate Voltage in a Distribution Feeder

    Kashem M. Muttaqi;An D. T. Le;Michael Negnevitsky;Gerard Ledwich

  • Risk Assessment for Power System Operation Planning With High Wind Power Penetration

    Michael Negnevitsky;Dinh Hieu Nguyen;Marian Piekutowski

  • Innovative Short-Term Wind Generation Prediction Techniques

    M. Negnevitsky;C.W. Potter

  • Control of a stand alone variable speed wind turbine with a permanent magnet synchronous generator

    M.E. Haque;K.M. Muttaqi;M. Negnevitsky

  • Short term wind power forecasting using hybrid intelligent systems

    M. Negnevitsky;P. Johnson;S. Santoso

  • Melting and solidification characteristics of a semi-rotational eccentric tube horizontal latent heat thermal energy storage

    Unknown

  • Optimal Distributed Generation Parameters for Reducing Losses with Economic Consideration

    A.D.T. Le;M.A. Kashem;M. Negnevitsky;G. Ledwich

  • Dynamic operation and control of a hybrid wind-diesel stand alone power systems

    A. M. O. Haruni;A. Gargoom;M. E. Haque;M. Negnevitsky

  • Walrasian Market Clearing for Demand Response Exchange

    Duy Thanh Nguyen;M. Negnevitsky;M. de Groot

  • Review of AI applications in harmonic analysis in power systems

    Ahmadreza Eslami;Michael Negnevitsky;Evan Franklin;Sarah Lyden

  • An Algebraic Approach for Determination of DG Parameters to Support Voltage Profiles in Radial Distribution Networks

    K. M. Muttaqi;An D. T. Le;M. Negnevitsky;G. Ledwich

  • Machine Learning Applications for Load, Price and Wind Power Prediction in Power Systems

    Michael Negnevitsky;Paras Mandal;Anurag K. Srivastava

  • Market-Based Demand Response Scheduling in a Deregulated Environment

    Duy Thanh Nguyen;Michael Negnevitsky;Martin de Groot

  • Pool-based Demand Response Exchange: Concept and modeling

    Thanh Nguyen;Michael Negnevitsky;Martin de Groot

Frequent Co-Authors

Kashem M. Muttaqi
Kashem M. Muttaqi University of Wollongong
Gerard Ledwich
Gerard Ledwich Queensland University of Technology
Xiaolin Wang
Xiaolin Wang University of Tasmania
Paras Mandal
Paras Mandal The University of Texas at El Paso
Anurag K. Srivastava
Anurag K. Srivastava West Virginia University
Christian Rehtanz
Christian Rehtanz TU Dortmund University
Surya Santoso
Surya Santoso The University of Texas at Austin
Pooya Davari
Pooya Davari Aalborg University
Frede Blaabjerg
Frede Blaabjerg Aalborg University
Jovica V. Milanovic
Jovica V. Milanovic University of Manchester

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