Innovative Research Award
Elijah Gbenga Osunkentan
Federal University of ABC
Elijah Gbenga Osunkentan is associated with engineering research focused on electrical power systems, microgrids, fault detection, transmission-line analysis, and intelligent computational methods. The supplied publication record indicates work involving statistical voltage-based islanding detection and machine-learning approaches for identifying electrical faults. These themes connect conventional power-system analysis with data-driven techniques for improving the monitoring and diagnosis of modern electrical networks. [1]
Abstract
The research profile of Elijah Gbenga Osunkentan centers on engineering applications in electrical power systems. Documented work includes passive islanding detection in microgrids, transmission-line fault detection using Support Vector Machines (SVM) and Artificial Neural Networks (ANN), and fault-location methods using Long Short-Term Memory (LSTM) neural networks. These studies represent complementary approaches to system monitoring, classification, and intelligent fault diagnosis. [1] [2] [3]
Keywords
Microgrids; islanding detection; power systems; fault detection; transmission lines; SVM; ANN; LSTM; intelligent fault location; electrical engineering.
Introduction
Modern electrical networks require dependable methods for detecting abnormal operating conditions and locating faults. Microgrids introduce additional operational considerations because distributed generation can alter system behavior during islanded and grid-connected states. Machine-learning techniques can complement established analytical approaches by supporting classification and pattern recognition. The supplied research record addresses these engineering challenges through statistical and intelligent computational methods. [1] [2]
Research Profile
Osunkentan’s documented research combines statistical signal interpretation with machine-learning models for electrical-system diagnosis. The reported areas include passive islanding detection, transmission-line fault classification, and intelligent fault-location techniques. This combination reflects an interdisciplinary engineering approach in which computational models are applied to practical power-system monitoring problems. [1] [2] [3]
Research Contributions
- Development of a statistical voltage-based approach for passive islanding detection in microgrids. [1]
- Comparative investigation of SVM and ANN techniques for transmission-line fault detection. [2]
- Application of LSTM neural networks to intelligent transmission-line fault-location analysis. [3]
Publications
The supplied publication information identifies three relevant research outputs. The 2026 article, “An efficient and cost-effective statistical voltage-based method for passive islanding detection in microgrids,” appears in Discover Electronics 3(1), article 132. A 2025 conference contribution examines SVM and ANN for transmission-line fault detection, while another work addresses transmission-line fault location using LSTM neural networks. [1] [2] [3]
Research Impact
The available record documents research activity across journal, conference, and preprint-oriented channels. The stated Scopus record contains one document; citation and h-index values were not supplied and are therefore not inferred here. The research themes have practical relevance to power-system monitoring because islanding detection and fault diagnosis address operational reliability and system protection requirements. [1]
Award Suitability
The documented research aligns with an Innovative Research Award category through its focus on statistical analysis, machine-learning methods, microgrid operation, and intelligent fault diagnosis. The supplied record provides identifiable research outputs and methods that can be considered in an academic recognition assessment. Final award decisions remain subject to the applicable evaluation criteria and review process of the International Forensic Scientist Awards. [4]
Conclusion
Elijah Gbenga Osunkentan’s supplied research record demonstrates a focused engineering interest in electrical-system diagnostics, microgrid islanding detection, transmission-line fault identification, and neural-network-based fault location. The combination of statistical and intelligent methods provides a coherent basis for documenting his research profile within an innovation-oriented academic recognition context.
External Links
References
- Osunkentan, E. G. (2026). An efficient and cost-effective statistical voltage-based method for passive islanding detection in microgrids. Discover Electronics, 3(1), 132.
- Osunkentan, E. G., dos Santos, R. C., & Da Silva, A. M. (2025). Comparative Analysis of SVM and ANN for Fault Detection in Transmission Lines. 2025 16th IEEE International Conference on Industry Applications (INDUSCON).
- Silva, A. M. (n.d.). An Effective Intelligent Method for Fault Location in Transmission Lines Using LSTM Neural Networks. SSRN 5928668.
- International Forensic Scientist Awards. (n.d.). International Forensic Scientist Awards.
forensicscientist.org
- Elsevier. (n.d.). Scopus author details: Elijah Gbenga Osunkentan, Author ID 60381839900. Scopus.
https://www.scopus.com/authid/detail.uri?authorId=60381839900
- Google Scholar. (n.d.). Elijah Gbenga Osunkentan publication and citation record.
Google Scholar Profile
