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RISS 인기검색어
- 검색키워드
- 저자 : Jianlou (검색결과 3 건)
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An Output Power Prediction Method for Multiple Wind Farms under Energy Internet Environment
Jianlou Lou,Hui Cao,Bin Song,Jizhe Xiao 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.11
Traditional wind power prediction is only applicable to a single wind farm. Aim at this isolated prediction method. In this paper, combing with the information sharing and Interconnection mechanism of energy Internet, we propose an output power prediction method for multiple wind farms based on DBPSO-LSSVM model. Firstly, collect SCADA data of multiple wind farms in different areas. Secondly, delete outliers of different farms based on DBSCAN algorithm and select multiple wind fields training samples. And searching the optimal input parameters of LSSVM based on particle swarm algorithm to construct every wind farm model. Thirdly, predict multiple wind fields power combined with numerical weather prediction system. The method we propose can be used to make the scheduling plan in advance to solve a large number of abandoned wind power rationing problem every year. In experiment, the method we propose has the lowest error rate compares to LSSVM and BP-neural network. It’s more suitable to predict wind fields in different areas.
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A Data-Mining Approach for Wind Turbine Power Generation Performance Monitoring Based on Power Curve
Jianlou Lou,Heng Lu,Jia Xu,Zhaoyang Qu 보안공학연구지원센터 2016 International Journal of Smart Home Vol.10 No.2
A new data-mining approach based on power curve profiles is put forward to monitor the power generation performance of wind turbines in this paper. Through assessing the wind-speed power datasets, the weakened power generation performance of turbines could be identified effectively by this approach. Shapes of power curve profiles over consecutive time intervals are constructed by fitting power curve models into wind-speed power datasets. In this research, we designed the Auto-adapt Optimal Interclass Variance algorithm, optimal constraint in each wind-speed power sub-dataset is explored for governing the data-driven method based on distance-based outlier detection and variance analysis model. The AOIV algorithm achieves the self-optimization of the threshold parameter and reaches a high degree of robustness to variations in wind-power generation performance monitoring. The blind industrial researches are conducted to validate the effectiveness of this approach, also indicates the decrease of error rates while detecting weakened power generation performance and the improvement of turbines’ power output.
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Jian Xu,Fei Zhong,Yonghong Zhang,Jianlou Zhang,Shanshan Huo,Hongyu Lin,Liyue Wang,Dan Cui,Xiujin Li 아세아·태평양축산학회 2017 Animal Bioscience Vol.30 No.4
Objective: To generate recombinant Bacillus subtilis (B. subtilis) engineered for expression of porcine β-defensin-2 (pBD-2) and cecropin P1 (CP1) fusion antimicrobial peptide and investigate their anti-bacterial activity in vitro and their growth-promoting and disease resisting activity in vivo. Methods: The pBD-2 and CP1 fused gene was synthesized using the main codons of B. subtilis and inserted into plasmid pMK4 vector to construct their expression vector. The fusion peptide-expressing B. subtilis was constructed by transformation with the vector. The expressed fusion peptide was detected with Western blot. The antimicrobial activity of the expressed fusion peptide and the recovered pBD-2 and CP1 by enterokinase digestion in vitro was analyzed by the bacterial growth-inhibitory activity assay. To analyze the engineered B. subtilis on growth promotion and disease resistance, the weaned piglets were fed with basic diet supplemented with the recombinant B. subtilis. Then the piglets were challenged by enteropathogenic Escherichia coli (E. coli). The weight gain and diarrhea incidence of piglets were measured after challenge. Results: The recombinant B. subtilis engineered for expression of pBD-2/CP1 fusion peptide was successfully constructed using the main codons of the B. subtilis. Both expressed pBD-2/CP1 fusion peptide and their individual peptides recovered from parental fusion peptide by enterokinase digestion possessed the antimicrobial activities to a variety of the bacteria, including gram-negative bacteria (E. coli, Salmonella typhimurium, and Haemophilus parasuis) and gram-positive bacteria (Staphylococcus aureus). Supplementing the engineered B. subtilis to the pig feed could significantly promote the piglet growth and reduced diarrhea incidence of the piglets. Conclusion: The generated B. subtilis strain can efficiently express pBD-2/CP1 fusion antimicrobial peptide, the recovered pBD-2 and CP1 peptides possess potent antimicrobial activities to a variety of bacterial species in vitro. Supplementation of the engineered B. subtilis in pig feed obviously promote piglet growth and resistance to the colibacillosis.
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