Energy and Power Engineering

  • Seyyed Ebrahim Hosseini, Mojtaba Najafi, Ali Akhavein, Mahdi Shahparasti, 2022, Day-ahead scheduling for economic dispatch of combined heat and power with uncertain demand response, IEEE Access, 10:1–1, January 2022.

    DOI: 10.1109/ACCESS.2022.3168306. Abstract.
     

  • Mohseni, Soheil and Brent, Alan C., 2022, Risk-based dispatch optimization of microgrids considering the uncertainty in EV driving patterns, 17th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). Abstract.DOI10.1109/PMAPS53380.2022.9810595
     
  • Singh, Amita; Sethi, Basant Kumara; Kumar, Abhishekb; Singh, Devendera; Misra, Rakesh Kumar; 2022, Three-Level Hierarchical Management of Active Distribution System With Multimicrogrid, IEEE Systems Journal, Pages 1-12. Abstract.
     
  • Mahmoudnezhad, Fayezeh; Mirzaei, Mohammad Amin; Plaum, Freddy; Ahmadiahangar, Roya; Kilter, Jakob; Rosin, Argo; 2022, Info-Gap-Based Optimization of Microgrids Integrated with Power, Cooling and Hydrogen Generation Units, 16th IEEE International Conference on Compatibility, Power Electronics, and Power Engineering, CPE-POWERENG, Birmingham, 29 June 2022 through 1 July 2022, Code 182944. Abstract.
     
  • Khalil Gholami, Ali Azizivahed, Ali Arefi, Li Li, 2022, Risk-averse Volt-VAr management scheme to coordinate distributed energy resources with demand response program, November 2022, Intl J Electrical Power \& Energy Systems, 146(2).DOI: 10.1016/j.ijepes.2022.108761. Abstract.
  • Khalil Gholami, Ali Azizivahed, Ali Arefi, 2022, Risk-oriented energy management strategy for electric vehicle fleets in hybrid AC-DC microgrids, Journal of Energy Storage, Volume 50, June 2022, 104258. Abstract.
     
  • Hafiz Muhammad Ashraf, Jin-Sol Song and Chul-Hwan Kim, 2022, A smart power system operation using sympathetic impact of IGDT and smart demand response with the high penetration of RES , Jan 2022, in IEEE Access, doi: 10.1109/ACCESS.2022.3206825. Abstract.
     
  • Marcos-Tostado Veliz, Bablesh Kumar Jha, Salah Kamel, Naran M. Pindoriya, Francisco Jurado, 2022, A three-stage Stochastic-IGDT model for photovoltaic-battery domestic systems considering outages and real-time pricing, Journal of Cleaner Production, Volume 370, 10 October 2022, 133558. Abstract.
     
  • Sahar Rahim and Pierluigi Siano, 2022, A survey and comparison of leading-edge uncertainty handling methods for power grid modernization, Expert Systems with Applications, Volume 204, 15 October 2022, 117590. Abstract.
     
  • Wachnik, B.; Kłodawski, M.; Kardas-Cinal, E. Reduction of the information gap problem in industry 4.0 projects as a way to reduce energy consumption by the industrial sector, Energies, 2022, 15, 1108. https://doi.org/10.3390/en15031108. Abstract.
     
  • Xiaolin Ge, Xiaohe Zhu, Xing Ju, Yang Fu, Kwok Lun Lo, Yang Mi, 2021, Optimal day-ahead scheduling for active distribution network based on improved information gap decision theory, \emph{IET Renewable Power Generation,} First published: 23 February 2021. https://doi.org/10.1049/rpg2.12045. Abstract.
     
  • Xiong Wu, Nailiang Li, Mingkang He, Xiuli Wang, Song Ma and Jingjing Cao, 2021, Risk-constrained day-ahead scheduling for gravity energy storage system and wind turbine based on IGDT, Renewable Energy, Available online 27 December 2021. Abstract.
     
  • Qiao Peng, Xiuli Wang, Xuanyue Shuai, Zhenzi Song, Ji Wu, Liang Zhang, and Chaoshan Xin, 2021, Planning of integrated energy system based on information gap decision theory, The 6th International Conference on Power and Renewable Energy, September 2021, pp. 1489-1494, DOI: 10.1109/ICPRE52634.2021.9635565. Abstract.
     
  • Alireza Arastou, Pouria Ahmadi, and Mehdi Karrari, 2021, Modeling and parameter estimation of a steam power plant including condenser back-pressure uncertainty using operational data, IEEE Systems Journal, Nov 2021. Abstract.
     
  • Morteza Shafiekhani, Abdollah Ahmadi, Omid Homaee, Miadreza Shafie-khah, João P.S.Catalão, 2022, Optimal bidding strategy of a renewable-based virtual power plant including wind and solar units and dispatchable loads, Energy, Volume 239, Part D, 15 January 2022, 122379. https://doi.org/10.1016/j.energy.2021.122379. Abstract.
     
  • Mahdis Haddadi, Abbas Rabiee, and Saman Nikkhah, 2022, Location-based uncertainty management of off-shore wind farms: A multiple radius robust decision making, Intl. J. Electrical Power & Energy Systems, vol. 136, March 2022, 107667. Abstract.
     
  • Ying Xue, Li Ge and Zhao Xue, 2021, Discussion and Commentary on “Robust scheduling of multi-chiller system with chilled-water storage under hourly electricity pricing” in “Energy and Buildings” 218 (2020) 110058, appearing in Energy and Buildings, vol. 252, 1 December 2021, 111445. Abstract.
     
  • Mostafa Kafaei, Davoud Sedighizadeh, Mostafa Sedighizadeh, Alireza Sheikhi Fini, 2022, An IGDT/Scenario based stochastic model for an energy hub considering hydrogen energy and electric vehicles: A case study of Qeshm Island, Iran, International Journal of Electrical Power & Energy Systems, Volume 135, February 2022, 107477. Abstract.
     
  • Mostafa Kafaei, Davoud Sedighizadeh, Mostafa Sedighizadeh, Alireza Sheikhi Fini, 2021, A two-stage IGDT/TPEM model for optimal operation of a smart building: A case study of Gheshm Island, Iran, Thermal Science and Engineering Progress, Volume 24, 1 August 2021, 100955. Abstract.
     
  • Yakov Ben-Haim, 2021, Feedback for energy conservation: An info-gap approach, Energy, 223: 119957. Abstract. Free access until 4 April 2021.
     
  • Rahim Fathi , Behrouz Tousi, and Sadjad Galvani, 2021, A new approach for optimal allocation of photovoltaic and wind clean energy resources in distribution networks with reconfiguration considering uncertainty based on info-gap decision theory with risk aversion strategy, Journal of Cleaner Production, 295 (2021) 125984. Abstract.
     
  • Xuguang Yu, Gang Li, Yapeng Li, Chuntian Cheng, 2021, Robust short-term scheduling based on information-gap decision theory for cascade reservoirs considering bilateral contract fulfillment and day-ahead market bidding in source systems, February 2021, Environmental Research Letters, https://iopscience.iop.org/article/10.1088/1748-9326/abe6c3. Abstract.
     
  • Gang Li, Jia Lu, Rui Yang and Chuntian Cheng, 2021, IGDT-Based Medium-Term Optimal Cascade Hydropower Operation in Multimarket with Hydrologic and Economic Uncertainties, Journal of Water Resources Planning and Management, 147(10).DOI: 10.1061/(ASCE)WR.1943-5452.0001444. Abstract.
     
  • M. Davari, H. Nafisi, M.-A. Nasr and F. Blaabjerg, A Novel IGDT-Based Method to Find the Most Susceptible Points of Cyberattack Impacting Operating Costs of VSC-Based Microgrids, in IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 9, no. 3, pp. 3695-3714, June 2021, doi: 10.1109/JESTPE.2020.3015447. Abstract.
     
  • Reza Saki, Esmaeel Rokrok, Mohammad Abedini, and Meysam Doostizadeh, 2020, Robust microgrid clustring approach for improving distribution system characteristics considering uncertainties of renewable energy resources IET Renewable Power Generation, Aug 2020, DOI: 10.1049/iet-rpg.2019.1155. Abstract.
     
  • Alisan Ayvaz and V.M. Istemihan Genc, 2020, Information gap decision theory based transient stability constrained optimal power flow considering the uncertainties of wind energy resources, IET Renewable Power Generation, Aug 2020, Vol. 14, issue 11, pp. 1946-1955. doi: 10.1049/iet-rpg.2019. Abstract.
     
  • Sima Aznavi, Poria Fajri, Eric M. Wilcox and Mohammad B. Shadmand, 2020, Risk Assessment of Smart Buildings Equipped with Solar Generation Using Information Gap Decision Theory, 2020 IEEE Energy Conversion Congress and Exposition (ECCE), Detroit, MI, USA, pp. 2142-2147, doi: 10.1109/ECCE44975.2020.9235433. Abstract.
     
  • Saeid Ahmadi; Hani Mavalizadeh; Ali Asghar Ghadimi; Mohammad Reza Miveh; Abdollah Ahmadi, 2020, Dynamic robust generation–transmission expansion planning in the presence of wind farms under long- and short-term uncertainties, IET Generation, Transmission & Distribution, 4 December 2020, vol. 14, issue 23: pp. 5418-5427. Abstract.
     
  • Mohammad Salehimaleh, Adel Akbarimajd, Khalil Valipour and Abdolmajid Dejamkhooy, 2020, Uncertainty modeling in operation of multi-carrier energy networks, in Planning and Operation of Multi-Carrier Energy Networks, Springer, pp.257-338. Abstract.
     
  • Ali Mohammad Rostami, Hossein Ameli, Mohammad Taghi Ameli and G. Strbac, 2020, Information-gap decision theory for robust operation of integrated electricity and natural gas transmission networks, International Conference on Smart Energy Systems and Technologies (SEST), September 2020. DOI: 10.1109/SEST48500.2020.9203435. Abstract.
     
  • Samira Salahi, Navid Rezaei and Jamal Moshtagh, 2020, An info‐gap risk‐averse optimization model for coordination of overcurrent protective relays considering power system uncertainty, International Transactions on Electrical Energy Systems, first published 21 September 2020. Abstract.
     
  • Mohammad Salimi, Mohamad-Amin Nasr, Seyed Hossein Hosseinian, Gevork B. Gharehpetian, and Mohammad Shahidehpour, 2020, Information gap decision theory-based active distribution system planning for resilience enhancement, IEEE Transactions on Smart Grid, to appear. Abstract.
     
  • Mohamad-Amin Nasr, Ehsan Nasr-Azadani and Hamed Nafisi, 2019, Assessing the effectiveness of weighted information gap decision theory integrated with energy management systems for isolated microgrids, IEEE Transactions on Industrial Informatics, to appear. Abstract.
     
  • Tanuj Rawat, Khaleeq Niazi, Nikhil Gupta and Sachin Sharma, 2020, Multi-objective information gap decision theory based operation of smart distribution grid  integrated with demand response, December 2020, DOI:  10.1109/NPSC49263.2020.9331832, Conference: 2020 21st National Power Systems Conference (NPSC). Abstract.
     
  • Tanuj Rawat and K. R. Niazi, 2019, Risk averse energy management for grid connected microgrid using information gap decision theory, Intelligent Computing Techniques for Smart Energy Systems, pp.465-473, Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 607). Abstract.
     
  • Zhao, Y., Lin, Z, Wen, F, Ding, Y, Hou, J, Yang, L., 2019, Risk-Constrained Day-Ahead Scheduling for Concentrating Solar Power Plants with Demand Response Using Info-Gap Theory, IEEE Transactions on Industrial Informatics, Vol. 15, # 10, pp.5475-5488. Abstract.
     
  • Reza Aboli, Maryam Ramezani and Hamid Falaghi, 2019, A hybrid robust distributed model for short-term operation of multi-microgrid distribution networks, Electric Power Systems Research, Volume 177, December 2019, 106011. Abstract.
     
  • A. Rezaee Jordehi, 2018, How to deal with uncertainties in electric power systems? A review, Renewable and Sustainable Energy Reviews, 96: 145-155. Abstract.
     
  • Arman Alahyari, Mehdi Ehsan, Mojtaba Moghimi, 2019, Managing Distributed Energy Resources (DERs) Through Virtual Power Plant Technology (VPP): A Stochastic Information-Gap Decision Theory (IGDT) Approach, Iranian Journal of Science and Technology — Transactions of Electrical Engineering, DOI: 10.1007/s40998-019-00248-w. Abstract.
     
  • Abouzar Samimi and Navid Rezaei, 2019, Robust optimal energy and reactive power management in smart distribution networks: An info‐gap multi‐objective approach, Electrical Energy Systems, DOI: 10.1002/2050-7038.12115. Abstract.
     
  • Jiafeng Ren, Haifeng Liang, and Yajing Gao, 2019, Research on Evaluation of Power Supply Capability of Active Distribution Network with Distributed Power Supply with High Permeability, Energies, 12(11), 2223; https://doi.org/10.3390/en12112223. Abstract.
     
  • Seyed-Ehsan Razavi, Ali Esmaeel Nezhad, Hani Mavalizadeh, Fatima Raeisi, Abdollah Ahmadi, 2018, Robust hydrothermal unit commitment: A mixed-integer linear framework, Energy, vol. 165, pp. 593-602. Abstract.
     
  • Deping Ke , Feifan Shen, C. Y. Chung , Chen Zhang , Jian Xu , and Yuanzhang Sun, 2018, Application of information gap decision theory to the design of robust wide-area power system stabilizers considering uncertainties of wind power, IEEE Transactions on Sustainable Energy, vol. 9, no. 2, April 2018, pp.805-817. Abstract.
     
  • Sayyad Nojavan, Hamed Pashaei-Didani, Kasra Saberi, Kazem Zare, 2019, Risk assessment in a central concentrating solar power plant, Solar Energy, Vol. 180, pp.293-300. Abstract.
     
  • Farkhondeh Jabari, Sayyad Nojavan, Behnam Mohammadi-ivatloo, Hadi Ghaebi and Hasan Mehrjerdi, 2018, Risk-constrained scheduling of solar Stirling engine based industrial continuous heat treatment furnace, Applied Thermal Engineering, Vol.128, #5, pp.940-955. Abstract.
     
  • M. Majidi, B. Mohammadi-Ivatloo, A. Soroudi, 2019, Application of information gap decision theory in practical energy problems: A comprehensive review,  Applied Energy, 249: 157-165. Abstract.
     
  • Sayyad Nojavan, Majid Majidi, Kazem Zare, 2017, Performance improvement of a battery/PV/fuel cell/grid hybrid energy system considering load uncertainty modeling using IGDT, Energy Conversion and Management, Volume 147, #1 pp.29-39. Abstract.
     
  • Sayyad Nojavan, Majid Majidi, Kazem Zare, 2017, Risk-based optimal performance of a PV/fuel cell/battery/grid hybrid energy system using information gap decision theory in the presence of demand response program, Intl Journal of Hydrogen Energy, to appear. Abstract.
     
  • Ali Mehdizadeh, Navid Taghizadegan, Javad Salehi, 2018, Risk-based energy management of renewable-based microgrid using information gap decision theory in the presence of peak load management, Applied Energy, 211: 617-630. Abstract.
     
  • Morteza Aien, Ali Hajebrahimi, Mahmud Fotuhi-Firuzabad, 2016, A comprehensive review on uncertainty modeling techniques in power system studies. Renewable and Sustainable Energy Reviews, 57: 1077-1089.
    Abstract

    Abstract

    As a direct consequence of power systems restructuring on one hand and unprecedented renewable energy utilization on the other, the uncertainties of power systems are getting more and more attention. This fact intensifies the difficulty of decision making in the power system context; therefore, the uncertainty analysis of the system performance seems necessary. Generally, uncertainties in any engineering system study can be represented probabilistically or possibilistically. When sufficient historical data of the system variables is not available, a probability density function (PDF) might not be defined, they must be represented in another manner i.e. using possibilistic theory. When some of the system uncertain variables are probabilistic and some are possibilistic, neither the conventional pure probabilistic nor pure possibilistic methods can be implemented. Hence, a combined solution is needed. This paper gives a complete review on uncertainty modeling approaches for power system studies making sense about the strengths and weakness of these methods. This work may be used in order to select the most appropriate method for each application.

    Author keywords

    Decision making, Probabilistic uncertainty modeling, Possibilistic uncertainty modeling, Uncertain power system studies, Joint possibilisticג€“probabilistic uncertainty modeling

  • Mohammad Sadegh Javadi, Amjad Anvari-Moghaddam and Josep M. Guerrero, 2017, Robust energy hub management using information gap decision theory, Annual Conference of the IEEE Industrial Electronics Society (IECON 2017), Beijing. Abstract.
     
  • Alireza Soroudi, Pouria Maghouli, Andrew Keane, 2017, Resiliency oriented integration of distributed series reactors in transmission networks, IET Generation Transmission & Distribution. Abstract.
     
  • Abbas Rabiee, Saman Nikkhah and Alireza Soroudi, 2018, Information Gap Decision Theory to Deal with Long-term Wind Energy Planning Considering Voltage Stability Energy, Volume 147, Pages 451-463. Abstract.
     
  • Mohamad-Amin Nasr, Abbas Rabiee, Innocent Kamwa, 2020, MPC and robustness optimisation-based EMS for microgrids with high penetration of intermittent renewable energy, IET Generation, Transmission & Distribution, Vol. 14 Iss. 22, pp. 5239-5248. Abstract.
     
  • Abbas Rabiee, Seyed Masoud Mohseni-Bonab, Innocent Kamwa, Saman Nikkhah, 2019, Risk averse energy management strategy in the presence of distributed energy resources considering distribution network reconfiguration: An information gap decision theory approach, IET Renewable Power Generation, to appear.
      
  • Alireza Soroudi, Abbas Rabiee and Andrew Keane, 2017, Information gap decision theory approach to deal with wind power uncertainty in unit commitment, Electric Power Systems Research, 145: 137-148. Abstract.
     
  • Mohamad-Amin Nasr, Ehsan Nasr Azadani, Abbas Rabiee, and S. H. Hosseinian, 2019, A Risk-Averse Energy Management System for Isolated Microgrids Considering Generation and Demand Uncertainties Based on Information Gap Decision Theory, IET Renewable Power Generation, to appear. Abstract.
     
  • Alison O’Connell, Alireza Soroudi and Andrew Keane, 2016, Distribution network operation under uncertainty using information gap decision theory, Transactions on Smart Grid, to appear.
    Abstract

    Abstract

    The presence of uncertain parameters in electrical power systems presents an ongoing problem for system operators and other stakeholders when it comes to making decisions. Determining the most appropriate dispatch schedule or system configuration relies heavily on forecasts for a number of parameters such as demand, generator availability and more recently weather. These uncertain parameters present an even more compelling problem at the distribution level, as these networks are inherently unbalanced, and need to be represented as such for certain tasks. The work in this paper presents an information gap decision theory based three-phase optimal power flow. Assuming that the demand is uncertain, the aim is to provide optimal and robust tap setting and switch decisions over a 24-hour period, while ensuring that the network is operated safely, and that losses are kept within an acceptable range. The formulation is tested on a section of realistic low voltage distribution network with switches and tap changers present.

    Index terms

    Load flow, optimisation, power distribution, smart grids, three-phase electric power, uncertainty.

     
  • Murphy, C.; Soroudi, A.; Keane, A., 2015, Information gap decision theory-based congestion and voltage management in the presence of uncertain wind power, IEEE Transactions on Sustainable Energy, DOI: 10.1109/TSTE.2015.2497544
    Abstract

    Abstract

    The supply of electrical energy is being increasingly sourced from renewable generation. The variability and uncertainty of renewable generation, compared to a dispatchable plant, is a significant dissimilarity of concern to the traditionally reliable and robust power system. This change is driving the power system towards a more flexible entity that carries greater amounts of reserve. For congestion management purposes it is of benefit to know the probable and possible renewable generation dispatch, but to what extent will these variations effect the management of congestion on the system? Reactive power generation from wind generators and demand response flexibility are the decision variables here in a risk averse multi-period AC optimal power flow (OPF) seeking to manage congestion on distribution systems. Information Gap Decision Theory is used to address the variability and uncertainty of renewable generation. In addition, this work considers the natural benefits to the congestion on a system from the over estimation of wind forecast; providing an opportunistic schedule for both demand response nodes and reactive power provision from distributed generation.

  • Rabiee, A.; Soroudi, A.; Keane, A., 2015, Information gap decision theory based OPF with HVDC connected wind farms,IEEE Transactions on Power Systems, vol.30, no.6, pp.3396-3406, DOI: 10.1109/TPWRS.2014.2377201
    Abstract

    Abstract

    A method for solving the optimal power flow (OPF) problem including HVDC connected offshore wind farms is presented in this paper. Different factors have been considered in the proposed method, namely, voltage source converter (VSC-HVDC) and line-commutated converter high-voltage DC (LCC-HVDC) link constraints, doubly fed induction generators’ (DFIGs) capability curve as well as the uncertainties of wind power generation. Information gap decision theory (IGDT) is utilized for handling the uncertainties associated with the volatility of wind power generation. It is computationally efficient and does not require the probability density function of wind speed. The proposed decision-making framework finds the optimal decision variables in a way that they remain robust against the considered uncertainties. To illustrate the effectiveness of the proposed approach, it is applied on the IEEE 118-bus system. The obtained results validate the applicability of the proposed IGDT-based OPF model for optimal operation of AC/DC power systems with high penetration of offshore wind farms.

  • P Maghouli, A Soroudi, A Keane, 2015, Robust computational framework for mid-term techno-economical assessment of energy storage, IET Generation, Transmission & Distribution, http://dx.doi.org/10.1049/iet-gtd.2015.0453
    Abstract

    Abstract

    Rapid expansion and integration of wind energy is restrained due to transmission capacity constraints and conventional generation technologies limited operational flexibility in today’s power systems. Energy storage is an attractive option to integrate and utilise more renewable energy without major and timely upgrade of existing transmission infrastructure. Moreover, it can be considered as a means for differing the reinforcement plans. The evaluation of energy storage deployment projects is a challenging task due to severe uncertainty of wind power generation. In this study, a robust techno-economic framework is proposed for energy storage evaluation based on information gap decision theory for handling wind generation uncertainty. The total social cost of the system including conventional generators’ fuel and pollution cost and wind power curtailment cost is optimised considering generators operational constraints and transmission system capacity limitations based on the DC model of the power grid. The effect of storage devices on system performance is evaluated taking into account wind power uncertainty. The proposed method is conducted on the modified IEEE reliability test system and the modified IEEE-118-bus test system to assess its applicability and performance in mid-term robust evaluation of energy storage implementation plans.

  • Tianyang Zhao, Jianhua Zhang, and Peng Wang, 2016, Flexible active distribution system management considering interaction with transmission networks using information-gap decision theory, Canadian Society for Electrical Engineering (CSEE) Journal of Power and Energy Systems, vol. 2, no. 4, pp.76-86. Abstract.
     
  • Hamid Reza Nikzad, Hamdi Abdi, Shahriar Abbasi, Robust unit commitment applying information gap decision theory and taguchi orthogonal array technique, chapter 7 in Behnam Mohammadi-ivatloo and Morteza Nazari-Heris, eds., 2019, Robust Optimal Planning and Operation of Electrical Energy Systems, Springer. Abstract.
      
  • Jian Zhao ; Can Wan ; Zhao Xu ; Jianhui Wang, 2017, Risk-based day-ahead scheduling of electric vehicle aggregator using information gap decision theory, IEEE Transactions on Smart Grid, vol. 8(4): 1609-1618, July 2017. Abstract.
     
  • Alireza Soroudi and Turaj Amraee, 2013, Decision making under uncertainty in energy systems: State of the art, Renewable and Sustainable Energy Reviews, 28: 376-384.
    Abstract

    Abstract

    The energy system studies include a wide range of issues from short term (e.g. real-time, hourly, daily and weekly operating decisions) to long term horizons (e.g. planning or policy making). The decision making chain is fed by input parameters which are usually subject to uncertainties. The art of dealing with uncertainties has been developed in various directions and has recently become a focal point of interest. In this paper, a new standard classification of uncertainty modeling techniques for decision making process is proposed. These methods are introduced and compared along with demonstrating their strengths and weaknesses. The promising lines of future researches are explored in the shadow of a comprehensive overview of the past and present applications. The possibility of using the novel concept of Z-numbers is introduced for the first time.

    Keywords

    Fuzzy arithmetic, Info-gap decision theory, Probabilistic modeling, Robust optimization, Interval based analysis, Z-number

     
  • Pereiro, D., Cogan, S., Sadoulet-Reboul, E., Martinez, F., Salgado, O., 2014, Wind turbine power train robust model calibration with load uncertainties, 26th International Conference on Noise and Vibration Engineering, ISMA 2014, Leuven, Belgium; 15-17 September 2014.
    Abstract

    Abstract

    The goal of this work is to propose a model calibration strategy for an industrial problem consisting in a MW class geared wind turbine power train subjected to uncertain loads. Lack of knowledge is commonplace in this kind of engineering system and a realistic model calibration cannot be performed without taking into account this type of uncertainty. The question at stake in this study is how to perform a robust predictive model of a dynamic system given that the excitations are poorly known. The uncertainty in the latter will be represented with an info-gap model. The tradeoff between fidelity to data and robustness to uncertainty is then investigated in order to maximize the robustness of the prediction error at a given horizon of uncertainty, a method is also proposed to increase confidence in model prediction for untested configurations. This methodology is illustrated on a simple academic model and on a more complex engineering system representing a wind turbine geared power train.

     
  • M.-P. Cheong, D. Berleant, and G. B. Shebl’e,Information Gap Decision Theory as a tool for Strategic Bidding in Competitive Electricity Markets, 8th International Conference on Probabilistic Methods Applied to Power Systems, Iowa State University, Ames, lowa, September 12-16,2004.