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Power system voltage collapse prediction using a new line stability index (NLSI-1): A case study of the 330-kV nigerian national grid
International Journal of Electrical and Computer Engineering (IJECE) · Vol. 9 No. 6 (2019)
- ISSN
- 2088-8708, 2088-8708
- Published
- 2019-12-01
- Publisher
- Institute of Advanced Engineering and Science
Abstract
The cumulative number of historical and recent power system outages substantiates the fact that further studies are necessary for an improved solution to the issue of voltage instability on the grid and the subsequent system collapse. Voltage collapse is a serious reliability issue which inhibits the objective of running a reliable and secure power system network. In this study, a new line stability index (NLSI_1) for predicting voltage collapse is presented. The new index considers a switching logic which is derived from the difference of voltage angle between the two load buses. The index is deployed for performance analysis using the 28-bus, 330-kV Nigeria National Grid (NNG). The simulation implemented in MATLAB shows that the index gives the same results as Line stability index (Lmn) and Fast Voltage Stability Index (FVSI) indices. The base case and the contingency scenarios were considered during the simulation. The base case analysis using the NNG values of all the three indices FVSI, Lmn, and NLSI_1 for simulation generates a value less than one for the entire lines which implies that the NNG is stable in this mode. The values of the three indices are almost the same, which confirms the accuracy of the novel index developed. The analysis for the contingency case reveals that the load bus 16 (Gombe) which has the lowest, maximum permissible reactive load of 139.5MVAR is the weakest; also power line 16-19 is identified as the critical line. The result of the simulation confirms that the accuracy was improved by using NLSI_1.
Collaboration network of the authors
29 authors · 75 co-authorship links · 28 across different organisations
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- Covenant University
- Namibia University of Science and Technology
- University of Ibadan
- Tshwane University of Technology
- Federal University of Agriculture, Abeokuta
- University of Lagos
- Lead City University
- Other organisations
- Affiliation unknown
- Different organisations
- Same organisation
- Affiliation unknown
- Thicker line = more joint articles · bigger circle = more articles
Show as table
| Co-author | Organisation | Joint articles |
|---|---|---|
| Ademola Abdulkareem | Covenant University | 2 |
| Olalekan Kabiru Kareem | University of Ibadan | 2 |
| Koto Omiloli | Covenant University | 2 |
| Isaac Samuel | Covenant University | 2 |
| Oghorchukwuyem Obiazi | Covenant University | 2 |
| Kayode Ebenezer Ojo | Lead City University | 2 |
| Seyi Fanifosi | New Mexico State University | 2 |
| A. E. Tope-Ojo | Covenant University | 1 |
| Samuel A. Isaac | Covenant University | 1 |
| A O Soyemi | Covenant University | 1 |
| Awelewa Ayokunle | Tshwane University of Technology | 1 |
| Awosope Claudius | Covenant University | 1 |
| Tobiloba Somefun | Covenant University | 1 |
| Oji K. Chinedum | Covenant University | 1 |
| Simeon Matthew | Federal University of Agriculture, Abeokuta | 1 |
| Hope Evwieroghene Orovwode | Covenant University | 1 |
| Abdulkareem Ademola | Covenant University | 1 |
| Oladipupo Oluwafemi K. | Covenant University | 1 |
| Agbetuyi Oluranti A. | Affiliation unknown | 1 |
| Timilehin Sanni | Covenant University | 1 |
| Anthony U. Adoghe | Affiliation unknown | 1 |
| Adeola Balogun | University of Lagos | 1 |
| Tobi Somefun | Affiliation unknown | 1 |
Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.