The DOI matches its Crossref record.
Optimal power flow by enhanced genetic algorithm
Abstract
This paper presents an enhanced genetic algorithm (EGA) for the solution of the optimal power flow (OPF) with both continuous and discrete control variables. The continuous control variables modeled are unit active power outputs and generator-bus voltage magnitudes, while the discrete ones are transformer-tap settings and switchable shunt devices. A number of functional operating constraints, such as branch flow limits, load bus voltage magnitude limits, and generator reactive capabilities, are included as penalties in the GA fitness function (FF). Advanced and problem-specific operators are introduced in order to enhance the algorithm's efficiency and accuracy. Numerical results on two test systems are presented and compared with results of other approaches.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Optimal power flow: a bibliographic survey I: Formulations and deterministic methods
Frank, S., Steponavice, I., & Rebennack, S. (2012) · Energy Systems · doi:10.1007/s12667-012-0056-y · Cited by 554
Optimal power flow using gravitational search algorithm
Duman, S., Güvenç, U., Sönmez, Y., & Yörükeren, N. (2012) · Energy Conversion and Management · doi:10.1016/j.enconman.2012.02.024 · Cited by 502
Optimal power flow solution in power systems using a novel Sine-Cosine algorithm
Attia, A.-F., El Sehiemy, R. A., & Hasanien, H. M. (2018) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2018.01.024 · Cited by 361
Optimal power flow: a bibliographic survey II: Non-deterministic and hybrid methods
Frank, S., Steponavice, I., & Rebennack, S. (2012) · Energy Systems · doi:10.1007/s12667-012-0057-x · Cited by 294
A novel hybrid GWO-SCA approach for optimization problems
Singh, N., & Singh, S. B. (2017) · Engineering Science and Technology, an International Journal · doi:10.1016/j.jestch.2017.11.001 · Cited by 214
Metadata
- Authors
- Bakirtzis, A. G., Biskas, P. N., Zoumas, C. E., & Petridis, V.
- Venue
- IEEE Transactions on Power Systems
- Published
- 2002-05
- Volume
- 17
- Issue
- 2
- Pages
- 229-236
- ISSN
- 0885-8950 (ISSN-L)
- OpenAlex
- W2103601587
- Cited by
- 661
- References
- 0
- FWCI
- 8.41
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Optimal power flow by enhanced genetic algorithm. https://app.cochat.ai/papers/10.1109/tpwrs.2002.1007886
- The paper
- Bakirtzis, A. G., Biskas, P. N., Zoumas, C. E., & Petridis, V. (2002). Optimal power flow by enhanced genetic algorithm. IEEE Transactions on Power Systems. https://doi.org/10.1109/tpwrs.2002.1007886
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
