Big data analysis and simulation of distributed marine green energy resources grid-connected system

• Autorzy: Tian J. , Huang L.

Identyfikatory

DOI

10.1515/pomr-2017-0121

• Języki publikacji: EN

Abstrakty

EN

In order to improve the working stability of distributed marine green energy resources grid-connected system, we need the big data information mining and fusion processing of grid-connected system and the information integration and recognition of distributed marine green energy grid-connected system based on big data analysis method, and improve the output performance of energy grid-connected system. This paper proposed a big data analysis method of distributed marine green energy resources grid-connected system based on closed-loop information fusion and auto correlation characteristic information mining. This method realized the big data closed-loop operation and maintenance management of grid-connected system, and built the big data information collection model of marine green energy resources grid-connected system, and reconstructs the feature space of the collected big data, and constructed the characteristic equation of fuzzy data closed-loop operation and maintenance management in convex spaces, and used the adaptive feature fusion method to achieve the auto correlation characteristics mining of big data operation and maintenance information, and improved the ability of information scheduling and information mining of distributed marine green energy resources grid-connected system. Simulation results show that using this method for the big data analysis of distributed marine green energy resources grid-connected system and using the multidimensional analysis technology of big data can improve the ability of information scheduling and information mining of distributed marine green energy resources grid-connected system, realizing the information optimization scheduling of grid-connected system. The output performance of grid connected system has been improved.

Słowa kluczowe

EN

distributed; ocean; green energy resources; grid-connected systems; big data analysis

• Wydawca: Gdańsk University of Technology, Faculty of Ocean Engineering & Ship Technology
• Czasopismo: Polish Maritime Research
• Rocznik: 2017
• Tom: S 3
• Strony: 182--191
• Opis fizyczny: Bibliogr. 32 poz., rys., tab.

Twórcy

autor

Tian J.

• Foshan Polytechnic Foshan 528137 China

autor

Huang L.

• Foshan Polytechnic, Foshan 528137, China

Bibliografia

• 1. M. Ceci, D. Malerba, 2007. Classifying Web documents in a hierarchy of categories:a comprehensive study. Journal of Intelligent Information System, 28(1): 37-78.
• 2. H. X. Wang, S. Y. Wang, X. Wang, et al. 2014. Analysis of LFM signals and improvement of IFM system. Acta Armamentarii, 35(8): 1193-1199.
• 3. M. A. Govoni, H. Li, J. A. Kosinski, 2013. Range-Doppler resolution of the linear-FM noise radar waveform. IEEE Transactions on Aerospace and Electronic Systems, 49(1): 658-664.
• 4. J. H. Lu, X. Han, J. X. Li, 2016. Consensus-based distributed fusion estimator with communication bandwidth constraints. Control and Decision, 31(12): 2155-2162.
• 5. M. Huang, L. T. Wang, H. C. Zhang, 2016. Face Recognition Based on Gabor Wavelet Transform and K-L Gaussian Riemannian Manifold Discriminant. Computer Engineering, 42(9): 208-213.
• 6. H. Hao, 2013. Multi component LFM signal detection and parameter estimation based on EEMD-FRFT. OptikInternational Journal for Light and Electron Optics, 124(23): 6093-6096.
• 7. M. A. Govoni, H. Li, J. A. Kosinski, 2013. Range-Doppler resolution of the linear-FM noise radar waveform. IEEE Transactions on Aerospace and Electronic Systems, 49(1):658-664.
• 8. Y. Y. Fu, M. Zhang, D. G., Feng et al. 2014. Attribute privacy preservation in social networks based on node anatomy. Journal of Software, 25(4): 768-780.
• 9. D. G. Feng, M. Zhang, H. Li, 2014. Big data security and privacy protection. Chinese Journal of Computers, 37(1): 246-258.
• 10. J. Song, G. Y. Xu, R. P. Yao, 2016. Anonymized data privacy protection method based on differential privacy. Journal of Computer Applications, 36(10): 2753-2757.
• 11. C. Hazay, K. Nissim, 2012. Efficient set operations in the presence of malicious adversaries. Journal of Cryptology, 25(3):383-433.
• 12. Y. Pan, Y. Tang, H. Liu, 2012. Access control in very loosely structured data model using relational databases. Acta Electronica Sinica, 240(3): 600-606.
• 13. A. Rahman, H. Muhammah, L. Sungyoung, et al. 2015. Rough set-based approaches for discretization: a compact reviews. Artificial Intelligence Review, 44(2): 235-263.
• 14. Y. H. Qian, H. Zhang, Y. Sang, et al. 2014. Multigranulation decision-theoretic rough sets. International Journal of Approximate Reasoning, 55(1): 225-237.
• 15. F. Xu, J. J. Ma, 2015. Improvement of Threshold RSA Signature Scheme Based on Chinese Remainder Theorem. JEIT, 37(10): 2495-2500.
• 16. P. Curt, R. J. Thomas, S. Deming, 2012. A high-fidelity harmonic drive model. ASME J of Dynamic Systems, Measurement, and Control, 134(1): 457-461.
• 17. S. Ali, R. Ali, A. Iftikhar, 2017. Physico-chemical and microbiological assessment of some freshwater aquifers and associated diseases in district ghizer, gilgit-baltistan, Pakistan. Acta Scientifica Malaysia, 1(1): 08-12.
• 18. Y. Pan, C. A. Yuan, W. J. Li, M. H. Cheng, 2016. Access Control Method for Supporting Update Operations in Dataspace. JEIT, 38(8): 1935-1941.
• 19. M. J. Guo, Y. Huang, Z. Xie, 2013. A WebGIS Model Optimization Strategy under Multi-core Environment. Computer Engineering, 39(8): 15-19.
• 20. Y. Huang, J. Paisley, Q. Lin, et al. 2014. Bayesian nonparametric dictionary learning for compressed sensing MRI. IEEE Transactions on Image Processing, 23(12): 5007-5019.
• 21. L. Shen, G. Sun, Q. Huang, et al. 2015. Multi-level discriminative dictionary learning with application to large scale image classification. IEEE Transactions on Image Processing, 24(10): 3109-3123.
• 22. L. Shen, G. Sun, Q. Huang, et al. 2015. Multi-level discriminative dictionary learning with application to large scale image classification. IEEE Transactions on Image Processing, 24(10): 3109-3123.
• 23. J. J. Thiagarajan, K. N. Ramamurthy, A. Spanias, 2015. Learning stable multilevel dictionaries for space representations. IEEE Transactions on Neural Networks & Learning Systems, 26(9): 1913-1926.
• 24. J. Z. Jiang, F. Zhou, 2013. Iterative design of two-dimensional critically sampled MDFT modulated filter banks. Signal Processing, 93(11): 3124-3132.
• 25. N. Rajapakaha, A. Madanayake, Lt. 2014. Bruton, 2D spacetime wave-digital multi-fan filter banks for sig[10]Gao, W. and W. Wang, The fifth geometric-arithmetic index of bridge graph and carbon nanocones. Journal of Difference Equations and Applications, 2017. 23(1-2SI): p. 100-109.
• 26. Gao, W., et al., Distance learning techniques for ontology similarity measuring and ontology mapping. Cluster Computing-The Journal of Networks Software Tools and Applications, 2017. 20(2SI): p. 959-968.nals consisting of multiple plane waves. Multidimensional Systems and Signal Processing, 25(1): 17-39.
• 27. H. Mahboubi, K. Moezzi, A. G. Aghdam, et al. 2014. Distributed deployment algorithms for improved coverage in a network of wireless mobile sensors. IEEE Transactions on Industrial Informatics, 10(1): 163-174.
• 28. N.S.A. Sukor, N. Jarani, S.F.M. Fisal, 2017. Analysis of Passengers’ Access and Egress Characteristics to The Train Station. Engineering Heritage Journal, 1(2): 01-04.
• 29. S.C.A. Mana, M.M. Hanafiah, A.J.K. Chowdhury, 2017. Environmental characteristics of clay and clay-based minerals. Geology, Ecology, and Landscapes, 1(3): 155-161.
• 30. M. Bahmani, A. Noorzad, J. Hamedi, F. Sali, 2017. The role of bacillus pasteurii on the change of parameters of sands according to temperatur compresion and wind erosion resistance. Journal CleanWAS, 1(2): 1-5.
• 31. N.A. Rahman, Z. Tarmudi, M. Rossdy, F.A. Muhiddin, 2017. Flood Mitigation Measres Using Intuitionistic Fuzzy Dematel Method. Malaysian Journal Geosciences, 1(2):01-05.
• 32. R. Roslee, N. Simon, 2017. Rock Slopes Kinametic Analysis Along the Bundu Tuhan To Kundasang Highway, Sabah, Malaysia. Geological Behavior, 1(2):01–04.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).