Yizi Shang

Prof. Dr. Yizi Shang

China Institute of Water Resources and Hydropower Research, Beijing, China
orcid

Research Focus & Metrics

Academic Career Length

20Years (2007-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 January 2018 in Applied Energy
Verified
Journal Article
Closed access
Yizi ShangYizi Shang
Yizi Shang
PHPengfei Hei
Pengfei Hei
SLShibao Lu
Shibao Lu
LSLing Shang
Ling Shang
XLXiaofei Li
Xiaofei Li
HWHao Wang
Hao Wang

1 May 2017 in Applied Energy
Verified
Journal Article
Closed access
Yizi ShangYizi Shang
Yizi Shang
SLShibao Lu
Shibao Lu
XLXiaofei Li
Xiaofei Li
PHPengfei Hei
Pengfei Hei
XLXiaohui Lei
Xiaohui Lei
HWHao Wang
Hao Wang

1 April 2017 in Journal of Cleaner Production
Verified
Journal Article
Closed access
SJShan Jiang
Shan Jiang
JWJianhua Wang
Jianhua Wang
YZYong Zhao
Yong Zhao
Yizi ShangYizi Shang
Yizi Shang
XGXuerui Gao
Xuerui Gao
YZYongnan Zhu
Yongnan Zhu

1 January 2017 in Journal of Cleaner Production
Verified
Journal Article
Closed access
Yizi ShangYizi Shang
Yizi Shang
SLShibao Lu
Shibao Lu
XLXiaofei Li
Xiaofei Li
GSGaohu Sun
Gaohu Sun
LSLing Shang
Ling Shang
HWHao Wang
Hao Wang

1 May 2017 in Applied Energy
Verified
Journal Article
Closed access
SLShibao Lu
Shibao Lu
JWJianhua Wang
Jianhua Wang
Yizi ShangYizi Shang
Yizi Shang
HBHaijun Bao
Haijun Bao
HCHuixiong Chen
Huixiong Chen

19 July 2021 in IEEE Transactions on Instrumentation and Measurement
Verified
Journal Article
Closed access
XMXin Ma
Xin Ma
HHHao Hu
Hao Hu
Yizi ShangYizi Shang
Yizi Shang
This research proposes a novel predictive model to improve the gas prediction accuracy in transformer oil and provide guarantees for accident prevention. First, this study constructs a cross-entropy loss function with variable thresholds and dynamic weights to reduce error transmission in the deep residual shrinkage network, enhancing the sensitivity of the normal and abnormal transformer states by the network. Second, the multiobjective particle swarm algorithm and random walk strategy are adopted to optimize the long short-term memory (LSTM) network to ensure the prediction model's objectivity. Finally, the improved subchannel threshold depth residual shrinkage network is integrated with the optimized LSTM network. The new model can identify potential abnormal conditions in advance and preproduce an approximate fault model using a feature gas vector (similar to image recognition). Experiments verify the effectiveness of the method. Compared with the existing prediction methods, the new method can avoid blind prediction and significantly improve the prediction accuracy and efficiency, which provides essential value for transformer fault prevention.