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Yunpeng Jiang
International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma PhysicsState Key Laboratory of Advanced Electromagnetic Engineering and TechnologySchool of Electrical & Electronic Engineering, Huazhong University of Science & TechnologyWuhanHubei ProvincePeople's Republic of China

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Journal article
Published: 10 December 2018 in The Journal of Engineering
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The contaminants accumulated on the surface of transmission line insulator mainly come from the suspended particles in the air. Therefore, it is necessary to consider meteorological factors and environmental factors in the prediction of insulator contamination degree. In view of the advantages of generalised regression neural network (GRNN) in the aspects of fault tolerance and robustness, this study uses it to predict equivalent salt deposit density (ESDD). Furthermore, the adaptive mutation particle swarm optimisation and GRNN prediction model is proposed in this study. According to adaptive algorithm and mutation algorithm, the inertia weight and acceleration factor of particles are dynamically adjusted to achieve the purpose of searching global optimal smoothing factor. The optimisation method can effectively avoid the premature convergence of particle swarm optimisation (PSO) and solve the drawback that PSO is easy to fall into the local optimal value. The results show that the prediction model proposed in this study can effectively predict the insulators ESDD, and the prediction error is less than the GRNN and PSO–GRNN models. The research can provide guidance for the development of a more scientific and rational maintenance plan to achieve effective control of the contaminants of the line.

ACS Style

Rumeng Wang; Ming Zhang; Yunpeng Jiang; Yong Yang. Prediction model of insulator contamination degree based on adaptive mutation particle swarm optimisation and general regression neural network. The Journal of Engineering 2018, 2019, 1423 -1428.

AMA Style

Rumeng Wang, Ming Zhang, Yunpeng Jiang, Yong Yang. Prediction model of insulator contamination degree based on adaptive mutation particle swarm optimisation and general regression neural network. The Journal of Engineering. 2018; 2019 (16):1423-1428.

Chicago/Turabian Style

Rumeng Wang; Ming Zhang; Yunpeng Jiang; Yong Yang. 2018. "Prediction model of insulator contamination degree based on adaptive mutation particle swarm optimisation and general regression neural network." The Journal of Engineering 2019, no. 16: 1423-1428.

Journal article
Published: 25 September 2018 in Coatings
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The characteristics of contamination on the insulation medium surface play an important role in the surface flashover, especially size distribution of contaminated particles. After measuring the size of contaminated particles on the porcelain insulator surface, obvious size distribution characteristics of particles were found. To study the reason for these statistical characteristics, the movement of particles was analyzed in detail combining with fluid mechanics and collision dynamics. Furthermore, an adhesion model was established in this paper. In addition, the influences of different factors on the adhesion were studied. The results showed that the size of adhered particles on the porcelain insulator surface was easy to focus on a specific range, and the influences of relative humidity and wind speed were remarkable. However, the influences of electric field type, electric field strength, and aerodynamic shape were relatively weak. This research was significant and valuable to the study of artificial contamination simulation experiments, and the influence of particles size distribution on pollution flashover.

ACS Style

Ming Zhang; Rumeng Wang; Lee Li; Yunpeng Jiang. Size Distribution of Contamination Particulate on Porcelain Insulators. Coatings 2018, 8, 339 .

AMA Style

Ming Zhang, Rumeng Wang, Lee Li, Yunpeng Jiang. Size Distribution of Contamination Particulate on Porcelain Insulators. Coatings. 2018; 8 (10):339.

Chicago/Turabian Style

Ming Zhang; Rumeng Wang; Lee Li; Yunpeng Jiang. 2018. "Size Distribution of Contamination Particulate on Porcelain Insulators." Coatings 8, no. 10: 339.

Journal article
Published: 01 January 2018 in Acta Psychologica Sinica
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ACS Style

Xiuling Zhang; Zhaoyang Pang; Yunpeng Jiang; Ming Zhang; Yi Jiang. Access to awareness is improved by affective learning. Acta Psychologica Sinica 2018, 50, 1 .

AMA Style

Xiuling Zhang, Zhaoyang Pang, Yunpeng Jiang, Ming Zhang, Yi Jiang. Access to awareness is improved by affective learning. Acta Psychologica Sinica. 2018; 50 (3):1.

Chicago/Turabian Style

Xiuling Zhang; Zhaoyang Pang; Yunpeng Jiang; Ming Zhang; Yi Jiang. 2018. "Access to awareness is improved by affective learning." Acta Psychologica Sinica 50, no. 3: 1.

Journal article
Published: 20 December 2012 in Acta Psychologica Sinica
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ACS Style

Xiu-Ling Zhang; Bo Dong; Yun-Peng Jiang; Ming Zhang. The Gestalt in Unconscious Processing: Evidence for the Unconscious Binding Hypothesis. Acta Psychologica Sinica 2012, 44, 1563 -1570.

AMA Style

Xiu-Ling Zhang, Bo Dong, Yun-Peng Jiang, Ming Zhang. The Gestalt in Unconscious Processing: Evidence for the Unconscious Binding Hypothesis. Acta Psychologica Sinica. 2012; 44 (12):1563-1570.

Chicago/Turabian Style

Xiu-Ling Zhang; Bo Dong; Yun-Peng Jiang; Ming Zhang. 2012. "The Gestalt in Unconscious Processing: Evidence for the Unconscious Binding Hypothesis." Acta Psychologica Sinica 44, no. 12: 1563-1570.