Distribution Network Operation Control Algorithm for Distributed Data Quality Management System

doi:10.23940/ijpe.20.05.p10.766774

Abstract

Abstract:

To improve the operation control ability of distribution networks, an operation control method based on a distributed data quality management system is proposed. A hybrid doubly-fed DC transmission configuration method is used to configure the quality distribution fusion feature of distribution network safety management combined with the dynamic parameter adjustment model of distribution network safety management quality distribution fusion feature is established. The interference suppression in the process of distribution network security management quality distribution fusion feature security control is carried out by combining the small disturbance steady-state suppression method. The distributed data quality management and information fusion method is used to adjust the error feedback and optimize the parameters in the process of distribution network security management quality distribution fusion feature security control, and the optimal design of distribution network operation control algorithm is realized. In additional, the proposed method has good output stability, large output gain and good anti-interference, and has a good guiding significance in the security networking control and distributed data quality management of distribution network. The simulation results show that the proposed method has good output stability, large output gain and good anti-interference ability in the security network control and distributed data quality management of distribution network.

Key words: distributed, data quality management, system, distribution network, operation control

Yu Tu. Distribution Network Operation Control Algorithm for Distributed Data Quality Management System [J]. Int J Performability Eng, 2020, 16(5): 766-774.

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Figures/Tables 7

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Figure 2.

Table 1

Table. 2

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References 15.

1.	A. Egea-Alvarez, S. Fekriasl, F. Hassan,O. G. Bellmunt, “Advanced Vector Control for Voltage Source Converters Connected to Weak Grids,” IEEE Transactions on Power Systems, Vol. 30, No. 6, pp. 3072-3081, 2015
2.	Y. Lin, Y. Y. Li, X. H. Yin,Z. Dou, “Multisensor Fault Diagnosis Modeling based on the Evidence Theory,” IEEE Transactions on Reliability, Vol. 60, No. 2, pp. 513-521, 2018
3.	T. Li and C. Y. Zhao, “Recovering the Modular Multilevel Converter from a Cleared or Isolated Fault,” IET Generation, Transmission and Distribution, Vol. 9, No. 6, pp. 550-559, 2015
4.	M. F. M.Arani and Y. A. R. I. Mohamed, “Analysis and Performance Enhancement of Vector-Controlled VSC in HVDC Links Connected to Very Weak Grids,” IEEE Transactions on Power Systems, Vol. 32, No. 1, pp. 684-693, 2017
5.	S. Liu, W. Fu,W. Zhao, “A Novel Fusion Method by Static and Moving Facial Capture,”Mathematical Problems in Engineering, DOI:10.1155/2013/503924, 2013
6.	S. Liu, W. N. Fu, H. M. Deng, C. H. Lan,J. T. Zhou, “Distributional Fractal Creating Algorithm in Parallel Environment,”International Journal of Distributed Sensor Networks, DOI:10.1155/2013/281707, 2013
7.	Y. Lin, C. Wang, J. X. Wang,Z. Dou, “A Novel Dynamic Spectrum Access Framework based on Reinforcement Learning for Cognitive Radio Sensor Networks,” Sensors, Vol. 16, No. 10, pp. 1-22, 2016
8.	K. Kamnitsas, C. Ledig, V. F. J.Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, et al., “Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation,”Medical Image Analysis, Vol. 36, pp. 61-78, 2016
9.	H. Chen, Q. Dou, L. Q. Yu, J. Qin,P. A. Heng, “VoxResNet: Deep Voxelwise Residual Networks for Brain Segmentation from 3D MR Images,”NeuroImage, Vol. 170, pp. 446-455, 2018
10.	O. Ronneber, P. Fischer,T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” inProceedings of the 2015 International Conference on Medical Computing and Computer-Assisted Intervention, pp. 234-241, Springer, Berlin, 2015
11.	C. H. Suder, W. Q. Li, T. Vercauteren, S. Ourselin,M. J. Cardoso, “Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations,” inProceedings of the 2017 International Conference on Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 240-248, Springer, Berlin, 2017
12.	Y. Lin, M. Y. Wang, X. L. Zhou, G. R. Ding,S. W. Mao, “Dynamic Spectrum Interaction of UAV Flight Formation Communication with Priority: A Deep Reinforcement Learning Approach,”IEEE Transactions on Cognitive Communications and Networking, DOI: 10.1109/TCCN.2020.2973376, 2020
13.	M. S. Ali, H. Tabassum,E. Hossain, “Dynamic User Clustering and Power Allocation for Uplink and Downlink Non-Orthogonal Multiple Access (NOMA) Systems,”IEEE Access, Vol. 4, pp. 6325-6343, 2016
14.	B. Kim, W. Chung, S. Lim, S. Suh, J. Kwun, S. Choi, et al., “Uplink NOMA with Multi-Antenna,” inProceedings of the 2015 IEEE 81st Vehicular Technology Conference, pp. 1-5, IEEE, Piscataway, NJ, 2015
15.	S. Liu, X. C. Cheng, C. H. Lan, W. N. Fu, J. T. Zhou, Q. Z. Li, et al., “Fractal Property of Generalized M-Set with Rational Number Exponent,”Applied Mathematics and Computation, Vol. 220, pp. 668-675, 2013

Parameter name	Specific parameters
RAM	32MB
Sampling time	1.5s
Screen	Display instrument operating status
Duration	More than 22 hours
Backpack battery	Long-term continuous monitoring
Chinese and English operation	Software analysis package

Distribution network feeder control parameters	Value
	43
/kHz)	13
	0.635
/V	43.5
/V	32
	0.034

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