[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
Main Menu
Home::
Journal Information::
Articles archive::
For Authors::
For Reviewers::
Registration::
Contact us::
Site Facilities::
::
Social Network Membership
Linkedin
Researchgate
..
Indexing Databases
..
DOI
کلیک کنید
..
ِDOR
..
Search in website

Advanced Search
..
Receive site information
Enter your Email in the following box to receive the site news and information.
..
:: Volume 14, Issue 4 (12-2025) ::
ieijqp 2025, 14(4): 34-43 Back to browse issues page
A scheme for fault distance and section estimation in microgrid using voltage magnitude measurement at the beginning of the grid and least squares
Golnaz Abasi1 , Mahmood Hosseini Aliabadi *1 , Mohammad Daisy2
1- Department of Electrical Engineering Central Tehran Branch, Islamic Azad University, Tehran Iran
2- Intelligent Power System Research Center Central Tehran Branch, Islamic Azad University, Tehran Iran
Abstract:   (1014 Views)
Microgrids are exposed to various types of faults, and the fast identification of fault location is essential for minimizing outages, preventing equipment damage, and improving reliability indices. This paper presents a two-stage scheme for fault section identification and fault distance estimation, relying solely on the voltage magnitude difference at the point of common coupling (PCC) and employing a least-squares approximation for distance estimation. In the first stage, the voltage magnitude difference pre and post fault is calculated and compared with simulated voltage differences across different sections; the section corresponding to the minimum index is identified as the faulty one. In the second stage, within the identified section, faults similar to the actual fault are simulated at 100-meter intervals to build an online databank. The relationship between the fault-induced voltage difference and the fault distance is then fitted with a quadratic polynomial, enabling analytical determination of the fault location. To enhance accuracy, the proposed method employs the π-line model (including line capacitance) and is independent of current data, current direction, and transformer saturation effects. Moreover, it does not require advanced communication systems or data synchronization. Evaluation on a modified IEEE 15-bus test system with distributed generation demonstrates that the proposed approach correctly identifies the faulty section and achieves a high fault location accuracy (99%) across a wide range of fault resistances, fault inception angles, and penetration levels of renewable resources. These features, combined with reliance on a single voltage measurement at the feeder head, make the method simple, cost-effective, and practical for real-world applications.
Keywords: Fault Location, Microgrid, Voltage Magnitude, Least Squares, Point of Common Coupling
Full-Text [PDF 1194 kb]   (311 Downloads)    
Type of Study: Research |
Received: 2025/03/6 | Accepted: 2025/12/27 | Published: 2025/12/27
References
1. AC microgrid based on voltage and current difference measurements. International Journal of Electrical Power & Energy Systems, 153, 109343.
2. Daisy, M., Hosseini Aliabadi, H. A., Javadi, J., & Meyar Naimi, H. (2024). Fault Location in Direct Current Microgrids Using DC Components of Voltage and Current. Journal of Iranian Association of Electrical and Electronics Engineers, 21(2), 135-145. doi:10.61186/jiaeee.21.2.135 [DOI:10.61186/jiaeee.21.2.135]
3. Daisy, M., Hosseini Aliabadi, M., Javadi, S., & Meyar Naimi, H. (2023). Data-Driven Fault Location Approach in AC/DC Microgrids Based on Fault Voltage and Current Differences. Sustainable Energy, Grids and Networks, 36. [DOI:10.1016/j.segan.2023.101235]
4. Daisy, M., Hosseini Aliabadi, M., Javadi, S., & Meyar Naimi, H. (2023). A novel scheme for distance and section estimation of steady-state faults in microgrids using voltage and current measurement at the beginning bus and the end buses of the microgrid. Electrical Engineering, 1-15. [DOI:10.1007/s00202-023-01992-3]
5. Dashti, R., Daisy, M., Mirshekali, H., Shaker, H. R., & Aliabadi, M. H. (2021). A Survey of Fault Prediction and Location Methods in Electrical Energy Distribution Networks. Measurement, 184, 109947. [DOI:10.1016/j.measurement.2021.109947]
6. Fathy, A., Dashti, R., Najafi, M., & Shaker, H. R. (2021). Transient and steady-state faults location in intelligent distribution networks compensated with D-STATCOM using time-domain equations and distributed line model. Electrical Engineering, 103, 3033-3048. [DOI:10.1007/s00202-021-01270-0]
7. Ganivada, P. K., & Jena, P. (2021). A Fault Location Identification Technique for Active Distribution System. IEEE Transactions on Industrial Informatics, 18(5), 3000 - 3010. [DOI:10.1109/TII.2021.3103543]
8. Kurmaiah, A., & Vaithilingam, C. (2025). Optimization of fault identification and location using adaptive neuro-fuzzy inference system and support vector machine for an ac microgrid. IEEE Access. [DOI:10.1109/ACCESS.2025.3534147]
9. Karthick, R., Saravanan, R., & Arulkumar, P. (2025). Fault Detection and Fault Location in a Grid‐Connected Microgrid Using Optimized Deep Learning Neural Network. Optimal Control Applications and Methods, 46(3), 896-911. [DOI:10.1002/oca.3237]
10. Kavousi-Fard, A., Nikkhah, S., Pourbehzadi, M., Dabbaghjamanesh, M., & Farughian, A. (2021). IoT-based data-driven fault allocation in microgrids using advanced µPMUs. Ad Hoc Networks, 119, 102520. [DOI:10.1016/j.adhoc.2021.102520]
11. Marín-Quintero, J., Orozco-Henao, C., & Herrera-Orozco, A. (2024). Fault indicators allocation to maximize the performance of a fault locator based on artificial intelligence. Electric power systems research, 234, 110701. [DOI:10.1016/j.epsr.2024.110701]
12. Mirshekali, H., Dashti, R., Shaker, H. R., Samsami, R., & Torabi, A. J. (2021). Linear and Nonlinear Fault Location in Smart Distribution Network under Line Parameter Uncertainty. IEEE Transactions on Industrial Informatics, 17(12), 8308 - 8318. [DOI:10.1109/TII.2021.3067007]
13. Neto, J. A. D. O., Sartori, C. A. F., & Junior, G. M. (2021). Fault Location in Overhead Transmission Lines Based on Magnetic Signatures and on the Extended Kalman Filter. Ieee Access, 9, 15259-15270. [DOI:10.1109/ACCESS.2021.3050211]
14. Pettikkattil, j. (2022). IEEE 15 Bus Radial System. mathworks. Retrieved from
15. Srivastava, A., & Parida, S. (2021). A Robust Fault Detection and Location Prediction Module Using Support Vector Machine and Gaussian Process Regression for AC Microgrid. IEEE Transactions on Industry Applications, 58(1), 930-939. [DOI:10.1109/TIA.2021.3129982]
16. Srivastava, A., & Parida, S. (2022). Data driven approach for fault detection and Gaussian process regression based location prognosis in smart AC microgrid. Electric power systems research, 208, 107889. [DOI:10.1016/j.epsr.2022.107889]
17. Zheng, X., Zeng, Y., Zhao, M., & Venkatesh, B. (2021). Early identification and location of short-circuit fault in grid-connected AC microgrid. IEEE Transactions on smart grid, 12(4), 2869-2878. [DOI:10.1109/TSG.2021.3066803]


XML   Persian Abstract   Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Abasi G, Hosseini Aliabadi M, daisy M. A scheme for fault distance and section estimation in microgrid using voltage magnitude measurement at the beginning of the grid and least squares. ieijqp 2025; 14 (4) :34-43
URL: http://ieijqp.ir/article-1-1004-en.html


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 14, Issue 4 (12-2025) Back to browse issues page
نشریه علمی- پژوهشی کیفیت و بهره وری صنعت برق ایران Iranian Electric Industry Journal of Quality and Productivity
Persian site map - English site map - Created in 0.14 seconds with 40 queries by YEKTAWEB 4772