Fakultät für Mathematik und Naturwissenschaften

Das Layout großer Photovoltaikkraftwerke

  1. R. Stanev, T. Tanev, Mathematical model of photovoltaic power plant, In: 2018 20th International Symposium on Electrical Apparatus and Technologies (SIELA), pp. 1-4. IEEE, 2018.
  2. M.J. Mayer, G. Gróf, Techno-economic optimization of grid-connected, ground-mounted photovoltaic power plants by genetic algorithm based on a comprehensive mathematical model, Solar Energy 202 (2020): 210-226.
  3. T. Ivanov, Tanyo, R. Stanev, Mathematical model of photovoltaic inverters, In: 2019 11th Electrical Engineering Faculty Conference (BulEF), pp. 1-5. IEEE, 2019.
  4. V. Milenov, Z. Zarkov, B. Demirkov, I. Bachev, Modeling of electrical characteristics of various PV panels, In 2019 16th Conference on Electrical Machines, Drives and Power Systems (ELMA), pp. 1-5. IEEE, 2019.
  5. Z.A. Zakaria, B.-C. Chen, M. O. Hassan, Modeling of photovoltaic power plants, In: 2008 International Conference on Electrical Machines and Systems, pp. 3835-3839. IEEE, 2008.
  6. M.J. Mayer, A. Szilágyi, G. Gróf, Ecodesign of ground-mounted photovoltaic power plants: Economic and environmental multi-objective optimization, Journal of Cleaner Production 278 (2021), 123934.

Optimierte Auslegung von Gasheizgeräten

  1. N.B. Samineni, T. Prabu, D.R. Yadav, G.A.P. Rao, A mathematical framework for design and optimization of regenerative storage heater, Applied Thermal Engineering, 135 (2018), 521-529.
  2. E. Martelli, E. Amaldi, S. Consonni, Numerical optimization of heat recovery steam cycles: Mathematical model, two-stage algorithm and applications, Computers & Chemical Engineering, 35(12) (2011), 2799-2823.
  3. M.E. Masoumi, Z. Izakmehri, Improving of refinery furnaces efficiency using mathematical modeling, International Journal of Modeling and Optimization 1.1 (2011), 74.
  4. L.M. Tiwari, S.P. Parikh, M. Gampawar, P. Choudhary, Designing and mathematical simulation of cylindrical and box type fired heater (used for heating of coke oven gas), International Journal of Advance Research, Ideas & Innovations (2018), 2037-2045.

Statistik bestimmt die Wahl der Windturbine

  1. E.K. Akpinar, S. Akpinar, An assessment on seasonal analysis of wind energy characteristics and wind turbine characteristics, Energy conversion and management 46, no. 11-12 (2005): 1848-1867.
  2. B.G. Kumaraswamy, B.K. Keshavan, Y.T. Ravikiran, Analysis of seasonal wind speed and wind power density distribution in Aimangala wind farm at Chitradurga Karnataka using two parameter Weibull distribution function, In 2011 IEEE Power and Energy Society General Meeting, pp. 1-4. IEEE, 2011.
  3. A.K. Azad, M.G. Rasul, M.M. Alam, S.M. Ameer Uddin, S.K. Mondal, Analysis of wind energy conversion system using Weibull distribution, Procedia Engineering 90 (2014): 725-732.
  4. K.A. Abed, A.A. El-Mallah, Capacity factor of wind turbines, Energy 22, no. 5 (1997): 487-491.
  5. M.H. Albadi, E.F. El-Saadany, Wind turbines capacity factor modeling - A novel approach, IEEE Transactions on Power Systems 24, no. 3 (2009): 1637-1638.

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