Optimal scheduling of electrical power in energy-deficient scenarios using artificial neural network and Bootstrap aggregating |
| |
| Affiliation: | 1. University Hospital Maastricht, The Netherlands;2. Careggi Hospital, Florence Italy;3. University of Magna Graecia, Catanzaro, Italy;1. Dept. of Electrical Engineering, Columbia University in the City of New York, New York, USA;2. School of Electrical Engineering, Tel Aviv University, Israel;1. Department of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, No.139 Middle Renmin Road, Changsha, Hunan 410011, China;2. Department of Biochemistry and Molecular Biology, The Libin Cardiovascular Institute of Alberta, Cumming School of Medicine, The University of Calgary, Health Sciences Center, 3330 Hospital Dr NW, Calgary, Alberta T2N 4N1, Canada;3. Department of Cardiovascular Surgery, The Second Xiangya Hospital, Central South University, No.139 Middle Renmin Road, Changsha, Hunan 410011, China |
| |
| Abstract: | In a developing country like Pakistan, where the electrical power demand is more than the generated power, maintaining the power system stability is a big challenge. In such cases it becomes, thus, essential to shed just the right amount of load to keep a power system stable. This paper presents a case study of Pakistan’s power system where the generated power, the load demand, frequency deviation and the load shed during a 24-h duration have been provided. The data have been analyzed using two techniques; the conventional artificial neural network (ANN) by implementing feed forward back propagation model and the Bootstrap aggregating or bagging algorithm. The simulation results reveal the superiority of the Bootstrap aggregating algorithm over a conventional ANN technique using feed forward back propagation model. |
| |
| Keywords: | Power system stability Optimal load shedding Artificial neural network Feed forward back propagation model Bootstrap aggregating or bagging Disjoint partition |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
