Prediction of short circuit current of wind turbines based on artificial neural network model

Ebrahim Aghajari; Ali AbdulKarim AbdulRahim

doi:10.4108/airo.5955

Authors

Ebrahim Aghajari Islamic Azad University, Ahvaz Branch
Ali AbdulKarim AbdulRahim Islamic Azad University

DOI:

https://doi.org/10.4108/airo.5955

Keywords:

Power Plant, Short Circuit Current, Neural Network

Abstract

The growth of renewable energy on a global scale is making significant strides in power plants. This is due to the increasing concern about climate change, the rising demand for electricity, and the necessity to reduce reliance on fossil fuels. Ensuring the successful integration of new energy resources into the existing network is just as crucial as it requires the system to be reliable and adaptable. For instance, wind energy, which is one of the renewable sources, has an intermittent nature that necessitates the ability to synchronize its actions to achieve the desired system performance. The objective of this study is to utilize a new neural network system to calculate the short circuit current of power plants. Specifically, the focus is on identifying and categorizing the short circuit faults that occur between the stator coils of the squirrel cage induction generator used in wind power generation. To achieve this, a system was developed to simulate turbine data. Subsequently, four feature extraction techniques and machine learning algorithms were employed to enable early detection of short circuit faults. The numerical results obtained from the simulation demonstrated the high efficiency and accuracy of the proposed model.

Downloads

Download data is not yet available.

References

J. Shair, X. Xie, W. Liu, X. Li, and H. Li, “Modeling and stability analysis methods for investigating subsynchronous control interaction in large-scale wind power systems,” in Renew Sustain Energy Rev. vol. 135 pp. 110420, 2021.

G. Rudy, and K.H. Khwee, “A new T-circuit model of wind turbine generator for power system steady state studies,” Bulletin of Electrical Engineering and Informatics. vol. 10, no.2, pp. 550-558, 2021.

G.T. Enes, S. Emiroglu, and M.A. Yalcin, “Optimal DG allocation and sizing in distribution systems with Thevenin based impedance stability index,” International Journal of Electrical Power & Energy Systems, vol. 144, pp. 108555, 2023.

D. Oliveira, R. Alves, and M.H. Bollen, “Susceptibility of large wind power plants to voltage disturbances-Recommendations to stakeholders,” Journal of Modern Power Systems and Clean Energy. vol. 10, no. 2. pp. 416-429, 2021.

L. Trevor, “Wind energy engineering: A handbook for onshore and offshore wind turbines,” Elsevier, 2023.

J.B. Francisco, “Short-circuit current contribution of doubly-fed wind turbines according to IEC and IEEE standards,” IEEE Transactions on Power Delivery. vol. 36, no. 5, pp. 2904-2912, 2020.

R.M. Furlaneto, I. Kocar, A. Grilo-Pavani, U. Karaagac, A. Haddadi, and E. Farantatos, “Short circuit network equivalents of systems with inverter-based resources reference,” Electric Power Systems Research. Elsevier, vol. 199, no. 107314, 2021.

F. Jimenez-Buendıa, A. Honrubia-Escribano, A. Lorenzo-Bonache, E. Artigao, and E. Gomez-Lazaro, “Short-Circuit Current Contribution of Doubly-Fed WindTurbines according to IEC and IEEE Standards,” IEEE TRANSACTIONS ONPOWER DELIVERY. IEEE, Minneapolis, Minnesota, USA, pp. 1-10, 2020.

K. Mahesh, “Optimal multi-objective placement and sizing of distributed generation in distribution system: a comprehensive review,” Energies. vol. 15, no. 21, pp. 7850, 2022.

A.H. Mohammadzadeh Niaki, and A.H. Solat, “A Novel Method to Determine the Maximum Penetration Level of Distributed Generation in the Distribution Network,” 2020 28th Iranian Conference on Electrical Engineering (ICEE). IEEE, 2020.

S.H. Abdurrahman, Y. Sun, and Z. Wang, “Optimization techniques applied for optimal planning and integration of renewable energy sources based on distributed generation: Recent trends,” Cogent Engineering. vol. 7, no. 1, pp. 1766394, 2020.

D. Mohammad, Z. Montazeri, and O.P. Malik, “Optimal sizing and placement of capacitor banks and distributed generation in distribution systems using spring search algorithm,” International Journal of Emerging Electric Power Systems. vol. 21, no. 1, pp. 20190217, 2020.

K. Eshan, “The optimal placement and sizing of distributed generation in an active distribution network with several soft open points,” Energies, vol. 14, no. 4, pp. 1084, 2021.

E.M. Hemmat, “Investigation of best artificial neural network topology to model the dynamic viscosity of MWCNT-ZnO/SAE 5W30 nano-lubricant,” Materials Today Communications. vol. 35, pp. 106074, 2023.

E.M. Hemmat, “A well-trained artificial neural network for predicting the optimum conditions of MWCNT–ZnO (10: 90)/SAE 40 nano-lubricant at different shear rates, temperatures, and concentration of nanoparticles,” Arabian Journal of Chemistry. vol. 16, no. 2, pp. 104508, 2023.

G.M. Mahdi, “Considering transient short-circuit currents of wind farms in overcurrent relays coordination using binary linear programming,” International Journal of Electrical Power & Energy Systems. vol. 131, pp. 107086, 2021.

C. Pedro, “A comprehensive overview of power converter applied in high-power wind turbine: Key challenges and potential solutions,” IEEE Transactions on Power Electronics. vol. 38, no. 5, pp. 6169-6195, 2023.

E.M. Hemmat, “Designing the best ANN topology for predicting the dynamic viscosity and rheological behavior of MWCNT-CuO (30: 70)/SAE 50 nano-lubricant,” Colloids and Surfaces A: Physicochemical and Engineering Aspects. vol. 651, pp. 129691, 2022.