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How to cite this paper
APA: Aikhuele, D., Periola, A & Ighravwe, D. (2019). Wind turbine systems operational state and reliability evaluation: An artificial neural network approach. International Journal of Data and Network Science, 3(4), 323-330.
Chicago/Turabian: Aikhuele, D., Periola, A & Ighravwe, D. 2019. "Wind turbine systems operational state and reliability evaluation: An artificial neural network approach." International Journal of Data and Network Science 3, no. 4 (2019): 323-330.
AMA: Aikhuele, D., Periola, A & Ighravwe, D. Wind turbine systems operational state and reliability evaluation: An artificial neural network approach. International Journal of Data and Network Science. 2019;3(4):323-330.
References
Aikhuele, D. O., & Turan, F. B. (2016). Intuitionistic fuzzy-based model for failure detection. Spring-erPlus, 5(1), 1938.
Aikhuele, D. O., & Turan, F.M. (2018). A modified exponential score function for troubleshooting an improved locally made Offshore Patrol Boat engine. Journal of Marine Engineering & Technology, 17(1), 52-58.
Aikhuele, D. O. (2018). Intuitionistic Fuzzy Model for Reliability Management in Wind Turbine System. Applied Computing and Informatics.
Aikhuele, D. O., Sorooshian, S., Ansah, R. H., & Turan, F. M. (2017). Application of intuitionistic fuzzy TOPSIS model for troubleshooting an offshore patrol boat engine. Polish Maritime Research, 24(2), 68-76.
Alwi, H., Edwards, C., & Tan, C. P. (2011). Fault tolerant control and fault detection and isolation. In Fault Detection and Fault-Tolerant Control Using Sliding Modes (pp. 7-27). Springer, London.
Anderson, V., & Johnson, L. (1997). Systems Thinking Basics. From Concepts to Causal Loops. Wal-tham: Mass Pegasus Comm., Inc.
Ata, R. (2015). Artificial neural networks applications in wind energy systems: a review. Renewable and Sustainable Energy Reviews, 49, 534-562.
Aval, S. M. M., & Ahadi, A. (2016). Reliability evaluation of wind turbine systems’ components. Bulle-tin of Electrical Engineering and Informatics, 5(2), 160-168.
Awedni, O., & Krichen, L. (2016, April). Adaptive observer-based fault estimation for a DFIG based wind turbine system. In Ecological Vehicles and Renewable Energies (EVER). 2016 Eleventh Inter-national Conference on (pp. 1-7). IEEE.
Bai, Y., Sun, Z., Deng, J., Li, L., Long, J., & Li, C. (2017). Manufacturing quality prediction using in-telligent learning approaches: A comparative study. Sustainability, 10(1), 85.
Blinco, L. J., Simpson, A. R., Lambert, M. F., Auricht, C. A., Hurr, N. E., Tiggemann, S. M., & Marchi, A. (2014). Genetic algorithm optimization of operational costs and greenhouse gas emissions for wa-ter distribution systems. Procedia Engineering, 89, 509-516.
Bianchi, L., & Dorigo, M. (2006). Ant colony optimization and local search for the probabilistic travel-ing salesman problem: a case study in stochastic combinatorial optimization.
Chengbing, H., & Xinxin, F. (2012). Institutions function and failure statistic and analysis of wind tur-bine. Physics Procedia, 24, 25-30.
Gao, Z., Cecati, C., & Ding, S. X. (2015). A survey of fault diagnosis and fault-tolerant techniques—Part I: Fault diagnosis with model-based and signal-based approaches. IEEE Transactions on Indus-trial Electronics, 62(6), 3757-3767.
Hoel, M., & Kverndokk, S. (1996). Depletion of fossil fuels and the impacts of global warming. Re-source and Energy Economics, 18(2), 115-136.
Kahrobaee, S., & Asgarpoor, S. (2010, September). Short and long-term reliability assessment of wind farms. In North American Power Symposium (NAPS), 2010 (pp. 1-6). IEEE.
Kusiak, A., & Li, W. (2011). The prediction and diagnosis of wind turbine faults. Renewable Energy, 36(1), 16-23.
Mensah, A. F., & Dueñas-Osorio, L. (2012). A closed-form technique for the reliability and risk as-sessment of wind turbine systems. Energies, 5(6), 1734-1750.
Mingjun, L. I. U., Wenyuan, L. I., Juan, Y. U., Zhouyang, R. E. N., & Ruilin, X. U. (2016). Reliability evaluation of tidal and wind power generation system with battery energy storage. Journal of Mod-ern Power Systems and Clean Energy, 4(4), 636-647.
Nivedh, B. S. (2014). Major failures in the Wind Turbine components and the Importance of Periodic Inspection,” http://www.windinsider.com/, 32–36.
Periola, A. A., & Falowo, O. E. (2017). Cognitive communications for commercial networked earth ob-serving fractionated small satellites. Wireless Personal Communications, 97(1), 443-467.
Su, C., & Hu, Z. (2018). Reliability assessment for Chinese domestic wind turbines based on data min-ing techniques. Wind Energy, 21(3), 198-209.
Simani, S., & Farsoni, S. (2018). Fault Diagnosis and Sustainable Control of Wind Turbines: Robust Data-Driven and Model-Based Strategies. Butterworth-Heinemann.
Tabatabaeipour, S. M., Odgaard, P. F., Bak, T., & Stoustrup, J. (2012). Fault detection of wind turbines with uncertain parameters: a set-membership approach. Energies, 5(7), 2424-2448.
Vogus, T. J., & Welbourne, T. M. (2003). Structuring for high reliability: HR practices and mindful pro-cesses in reliability‐seeking organizations. Journal of Organizational Behavior: The International Journal of Industrial, Occupational and Organizational Psychology and Behavior, 24(7), 877-903.
Wang, G. S., Huang, F. K., & Lin, H. H. (2004, August). Application of genetic algorithm to structural dynamic parameter identification. In 13th World Conference on Earthquake Engineering, Vancouver (pp. 1-6).
Witczak, M., Rotondo, D., Puig, V., Nejjari, F., & Pazera, M. (2018). Fault estimation of wind turbines using combined adaptive and parameter estimation schemes. International Journal of Adaptive Con-trol and Signal Processing, 32(4), 549-567.
Witczak, M., Puig, V., Rotondo, D., de Rozprza Faygel, M., & Mrugalski, M. (2015, June). A robust ℋ∞ observer design for unknown input nonlinear systems: Application to fault diagnosis of a wind turbine. In Control and Automation (MED), 2015 23th Mediterranean Conference on (pp. 162-167). IEEE.
Zheng, M., & Ming, X. (2017). Construction of cyber-physical system–integrated smart manufacturing workshops: A case study in automobile industry. Advances in Mechanical Engineering, 9(10), 1687814017733246.
Aikhuele, D. O., & Turan, F.M. (2018). A modified exponential score function for troubleshooting an improved locally made Offshore Patrol Boat engine. Journal of Marine Engineering & Technology, 17(1), 52-58.
Aikhuele, D. O. (2018). Intuitionistic Fuzzy Model for Reliability Management in Wind Turbine System. Applied Computing and Informatics.
Aikhuele, D. O., Sorooshian, S., Ansah, R. H., & Turan, F. M. (2017). Application of intuitionistic fuzzy TOPSIS model for troubleshooting an offshore patrol boat engine. Polish Maritime Research, 24(2), 68-76.
Alwi, H., Edwards, C., & Tan, C. P. (2011). Fault tolerant control and fault detection and isolation. In Fault Detection and Fault-Tolerant Control Using Sliding Modes (pp. 7-27). Springer, London.
Anderson, V., & Johnson, L. (1997). Systems Thinking Basics. From Concepts to Causal Loops. Wal-tham: Mass Pegasus Comm., Inc.
Ata, R. (2015). Artificial neural networks applications in wind energy systems: a review. Renewable and Sustainable Energy Reviews, 49, 534-562.
Aval, S. M. M., & Ahadi, A. (2016). Reliability evaluation of wind turbine systems’ components. Bulle-tin of Electrical Engineering and Informatics, 5(2), 160-168.
Awedni, O., & Krichen, L. (2016, April). Adaptive observer-based fault estimation for a DFIG based wind turbine system. In Ecological Vehicles and Renewable Energies (EVER). 2016 Eleventh Inter-national Conference on (pp. 1-7). IEEE.
Bai, Y., Sun, Z., Deng, J., Li, L., Long, J., & Li, C. (2017). Manufacturing quality prediction using in-telligent learning approaches: A comparative study. Sustainability, 10(1), 85.
Blinco, L. J., Simpson, A. R., Lambert, M. F., Auricht, C. A., Hurr, N. E., Tiggemann, S. M., & Marchi, A. (2014). Genetic algorithm optimization of operational costs and greenhouse gas emissions for wa-ter distribution systems. Procedia Engineering, 89, 509-516.
Bianchi, L., & Dorigo, M. (2006). Ant colony optimization and local search for the probabilistic travel-ing salesman problem: a case study in stochastic combinatorial optimization.
Chengbing, H., & Xinxin, F. (2012). Institutions function and failure statistic and analysis of wind tur-bine. Physics Procedia, 24, 25-30.
Gao, Z., Cecati, C., & Ding, S. X. (2015). A survey of fault diagnosis and fault-tolerant techniques—Part I: Fault diagnosis with model-based and signal-based approaches. IEEE Transactions on Indus-trial Electronics, 62(6), 3757-3767.
Hoel, M., & Kverndokk, S. (1996). Depletion of fossil fuels and the impacts of global warming. Re-source and Energy Economics, 18(2), 115-136.
Kahrobaee, S., & Asgarpoor, S. (2010, September). Short and long-term reliability assessment of wind farms. In North American Power Symposium (NAPS), 2010 (pp. 1-6). IEEE.
Kusiak, A., & Li, W. (2011). The prediction and diagnosis of wind turbine faults. Renewable Energy, 36(1), 16-23.
Mensah, A. F., & Dueñas-Osorio, L. (2012). A closed-form technique for the reliability and risk as-sessment of wind turbine systems. Energies, 5(6), 1734-1750.
Mingjun, L. I. U., Wenyuan, L. I., Juan, Y. U., Zhouyang, R. E. N., & Ruilin, X. U. (2016). Reliability evaluation of tidal and wind power generation system with battery energy storage. Journal of Mod-ern Power Systems and Clean Energy, 4(4), 636-647.
Nivedh, B. S. (2014). Major failures in the Wind Turbine components and the Importance of Periodic Inspection,” http://www.windinsider.com/, 32–36.
Periola, A. A., & Falowo, O. E. (2017). Cognitive communications for commercial networked earth ob-serving fractionated small satellites. Wireless Personal Communications, 97(1), 443-467.
Su, C., & Hu, Z. (2018). Reliability assessment for Chinese domestic wind turbines based on data min-ing techniques. Wind Energy, 21(3), 198-209.
Simani, S., & Farsoni, S. (2018). Fault Diagnosis and Sustainable Control of Wind Turbines: Robust Data-Driven and Model-Based Strategies. Butterworth-Heinemann.
Tabatabaeipour, S. M., Odgaard, P. F., Bak, T., & Stoustrup, J. (2012). Fault detection of wind turbines with uncertain parameters: a set-membership approach. Energies, 5(7), 2424-2448.
Vogus, T. J., & Welbourne, T. M. (2003). Structuring for high reliability: HR practices and mindful pro-cesses in reliability‐seeking organizations. Journal of Organizational Behavior: The International Journal of Industrial, Occupational and Organizational Psychology and Behavior, 24(7), 877-903.
Wang, G. S., Huang, F. K., & Lin, H. H. (2004, August). Application of genetic algorithm to structural dynamic parameter identification. In 13th World Conference on Earthquake Engineering, Vancouver (pp. 1-6).
Witczak, M., Rotondo, D., Puig, V., Nejjari, F., & Pazera, M. (2018). Fault estimation of wind turbines using combined adaptive and parameter estimation schemes. International Journal of Adaptive Con-trol and Signal Processing, 32(4), 549-567.
Witczak, M., Puig, V., Rotondo, D., de Rozprza Faygel, M., & Mrugalski, M. (2015, June). A robust ℋ∞ observer design for unknown input nonlinear systems: Application to fault diagnosis of a wind turbine. In Control and Automation (MED), 2015 23th Mediterranean Conference on (pp. 162-167). IEEE.
Zheng, M., & Ming, X. (2017). Construction of cyber-physical system–integrated smart manufacturing workshops: A case study in automobile industry. Advances in Mechanical Engineering, 9(10), 1687814017733246.