Optimal placement of grid-connected photovoltaic generators in a power system for voltage stability enhancement
The high penetration of photovoltaic (PV) generation can cause many technical issues such as power quality and impact on the power system voltage stability. To improve voltage stability and reducing power loss in a power system with PV generators, appropriate planning of PV generators is considered...
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| Main Authors: | , , |
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2019-01-01.
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| Online Access: | Get fulltext |
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| LEADER | 02424 am a22003133u 4500 | ||
|---|---|---|---|
| 001 | ijeecs13551_10301 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Kamaruzzaman, Zetty Adibah |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Mohamed, Azah |e author |
| 700 | 1 | 0 | |a mohamed, ramizi |e author |
| 245 | 0 | 0 | |a Optimal placement of grid-connected photovoltaic generators in a power system for voltage stability enhancement |
| 260 | |b Institute of Advanced Engineering and Science, |c 2019-01-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/13551 | ||
| 520 | |a The high penetration of photovoltaic (PV) generation can cause many technical issues such as power quality and impact on the power system voltage stability. To improve voltage stability and reducing power loss in a power system with PV generators, appropriate planning of PV generators is considered by optimal placement of PV. Thus, an effective heuristic optimization technique such as the Wind Driven Optimization (WDO) technique is applied for determining optimal location of PV generators in a power system. For determining the optimal location of PV, the objective function considers maximizing the Improved Voltage Stability Index. The proposed method for optimal location of PV generators is implemented on the IEEE 118 and 30 bus transmission systems and the 69-radial distribution system. The optimization results show that integrating PV into the test systems improves voltage stability in the system. Comparing the performance of the WDO with the particle swarm optimization technique, it is shown that the WDO technique gives faster convergence. | ||
| 540 | |a Copyright (c) 2018 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Electric; Artificial Intelligence | ||
| 690 | |a Photovoltaic generation; Optimal location; Wind driven optimization | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 13, No 1: January 2019; 339-346 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v13.i1 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/13551/10301 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/13551/10301 |z Get fulltext |