Analysis and control of DC-DC converters using average power balance control (APBC) in solar power applications

Author(s):  
Sathish Kumar Kollimalla ◽  
Mahesh Kumar Mishra ◽  
N. Lakshmi Narasamma
2019 ◽  
Vol 27 (2) ◽  
pp. 341-351
Author(s):  
王 坤 WANG Kun ◽  
李 腾 LI Teng ◽  
毛 琨 MAO Kun ◽  
韩邦成 HAN Bang-cheng

2021 ◽  
Vol 24 (1) ◽  
Author(s):  
Valeria Rosso ◽  
Vesa Linnamo ◽  
Yves Vanlandewijck ◽  
Walter Rapp ◽  
Benedikt Fasel ◽  
...  

AbstractIn Paralympic cross-country sit skiing, athlete classification is performed by an expert panel, so it may be affected by subjectivity. An evidence-based classification is required, in which objective measures of impairment must be identified. The purposes of this study were: (i) to evaluate the reliability of 5 trunk strength measures and 18 trunk control measures developed for the purposes of classification; (ii) to rank the objective measures, according to the largest effects on performance. Using a new testing device, 14 elite sit-skiers performed two upright seated press tests and one simulated poling test to evaluate trunk strength. They were also subjected to unpredictable balance perturbations to measure trunk control. Tests were repeated on two separate days and test–retest reliability of trunk strength and trunk control measures was evaluated. A cluster analysis was run and correlation was evaluated, including all strength and control measures, to identify the measures that contributed most to clustering participants. Intraclass correlations coefficients (ICC) were 0.71 < ICC < 0.98 and 0.83 < ICC < 0.99 for upright seated press and perturbations, respectively. Cluster analysis identified three clusters with relevance for strength and balance control measures. For strength, in upright seated press peak anterior pushing force without backrest (effect size = 0.77) and ratio of peak anterior pushing force without and with backrest (effect size = 0.72) were significant. For balance control measures, trunk range of motion in forward (effect size = 0.81) and backward (effect size = 0.75) perturbations also contributed. High correlations (− 0.76 < r < − 0.53) were found between strength and control measures. The new testing device, protocol, and the cluster analysis show promising results in assessing impairment of trunk strength and control to empower an evidence-based classification.


2013 ◽  
Vol 694-697 ◽  
pp. 2228-2232
Author(s):  
Yuan Hua Zhou ◽  
Hong Wei Ma

Considering the power balance control in two motors driving shearer, a novel multi-motor power balance control scheme based on ANFIS (Adaptive Neuro-Fuzzy Inference System) is presented. The scheme avoided to modeling the control model of the coal mining machine and could meet the demand of the control system and prevent individual motor from overloading. In MATLAB, use the filed data to simulate and the simulation verify the proposed scheme is valid.


2018 ◽  
Vol 11 (6) ◽  
pp. 1046-1054 ◽  
Author(s):  
Manyuan Ye ◽  
Lixuan Kang ◽  
Yunhuang Xiao ◽  
Pinggang Song ◽  
Song Li

2014 ◽  
Vol 521 ◽  
pp. 252-255
Author(s):  
Jian Yuan Xu ◽  
Jia Jue Li ◽  
Jie Jun Zhang ◽  
Yu Zhu

The problem of intermittent generation peaking is highly concerned by the grid operator. To build control model for solving unbalance of peaking is great necessary. In this paper, we propose reserve classification control model which contain constant reserve control model with real-time reserve control model to guide the peaking balance of the grid with intermittent generation. The proposed model associate time-period constant reserve control model with real-time reserve control model to calculate, and use the peaking margin as intermediate variable. Therefore, the model solutions which are the capacity of reserve classification are obtained. The grid operators use the solution to achieve the peaking balance control. The proposed model was examined by real grid operation case, and the results of the case demonstrate the validity of the proposed model.


2011 ◽  
Vol 26 (4) ◽  
pp. 1154-1166 ◽  
Author(s):  
Jianjiang Shi ◽  
Wei Gou ◽  
Hao Yuan ◽  
Tiefu Zhao ◽  
Alex Q. Huang

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