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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Zhang Xueqing Liang Jun Dong Xiaoming Ren Jingguo |
| Copyright Year | 2012 |
| Description | Author affiliation: School of Electrical Engineering, Shandong University, Jinan 250061, China (Zhang Xueqing; Liang Jun; Dong Xiaoming; Ren Jingguo) |
| Abstract | According to the future power grids encountering the new problems such as the imbalance on power distribution, the ambiguity on the trend of power flow and the power development from centralization to distribution, a regulation and controlling strategy with power grids aggregation based on gradual learning was proposed. Under the premise of the panoramic observation of the future grid, the power grid aggregation equivalent model was constructed by mining the process information of power grid in a panoramic view. Thereby, the mechanism for regulation and controlling strategy, which was emerged with self-learning and association between time and space, was established by using modern learning theory, and it is compatible with the traditional regulation and controlling measures of power grid, which reflected the intelligence of future power grids and appeared continuous improvement on gradual progressive learning. Finally, the actual operating data examples of power girds were presented, and the simulation results illustrated that the mechanism of regulation and controlling strategy proposed was identified to the feasibility, which provided a useful and effective reference to the operation regulation and controlling of the future power grids. |
| Starting Page | 1 |
| Ending Page | 4 |
| File Size | 262955 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781467312219 |
| e-ISBN | 9781467312202 |
| DOI | 10.1109/ISGT-Asia.2012.6303396 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-05-21 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training Gradual learning Least Squares Support Vector Machine(LSSVM) Automatic generation control Wind power generation Aerospace electronics Power grids Dispatching Regulation and controlling Smart grid |
| Content Type | Text |
| Resource Type | Article |
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