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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Weiwei Shi Yongxin Zhu Jinkui Zhang Xiang Tao Gehao Sheng Yong Lian Guoxing Wang Yufeng Chen |
| Copyright Year | 2015 |
| Description | Author affiliation: Electr. Power Res. Inst., Shandong Power Supply Co. of State Grid, Shandong Province, China (Yufeng Chen) || Sch. of Electron. Inf. & Electr. Eng., Shanghai Jiao Tong Univ., Shanghai, China (Weiwei Shi; Yongxin Zhu; Jinkui Zhang; Xiang Tao; Gehao Sheng; Yong Lian; Guoxing Wang) |
| Abstract | Big data techniques has been applied to power grid for the evaluation and prediction of grid conditions. However, the raw data quality rarely can meet the requirement of precise data analytics since raw data set usually contains samples with missing data to which the common data mining models are sensitive. Though classic interpolation or neural network methods can been used to fill the gaps of missing data, their predicted data often fail to fit the rules of power grid conditions. This paper presents a machine learning framework (OR_MLF) to improve the prediction accuracy for datasets with missing data points, which mainly combines preprocessing, optimizing support vector machine (OSVM) and refining SVM (RSVM). On top of the OSVM engine, the scheme introduces dedicated data training strategies. First, the original data originating from data generation facilities is preprocessed through standardization. Traditional SVM is then trained to obtain a preliminary prediction model. Next, the optimized SVM predictors are achieved with new training data set, which is extracted based on the preliminary prediction model. Finally, the missing data prediction result depending on OSVM is selectively inputted into the traditional SVM and the refined SVM is lastly accomplished. We test the OR_MLF framework on missing data prediction of power transformers in power grid system. The experimental results show that the predictors based on the proposed framework achieve lower mean square error than traditional ones. Therefore, the framework OR_MLF would be a good candidate to predict the missing data in power grid system. |
| Starting Page | 417 |
| Ending Page | 422 |
| File Size | 281112 |
| Page Count | 6 |
| File Format | |
| e-ISBN | 9781479989379 |
| DOI | 10.1109/HPCC-CSS-ICESS.2015.16 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-08-24 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Support vector machines Training power transformer support vector machine (SVM) Predictive models Feature extraction Data models Power grids missing data prediction Data mining machine learning |
| Content Type | Text |
| Resource Type | Article |
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