Power system networks are currently facing an unprecedented surge in demand by consumers of power in addition to the stress facing mostly overhead lines such as line breaks, vandalism, etc. Thus, the need to maintain the demand whilst building much stronger and more resilient networks that can withstand such faults or abuse of power networks more proactively is becoming a core priority. In this paper, the functional methods utilized in the development of PMU-based fault localization considering the Optimal PMU Placement (OPP) problem are presented from the first principles.
Furthermore, the Evolutionary Computing (EC) approach which is based on a Modified Sparsity Genetic Algorithm (GA) optimizer (MSGAO) is presented and validated using a hypothetical Six Bus Network and an IEEE 14-Bus Network – both networks sourced from materials in the open domain. Then the overall architecture of the proposed system is presented and described. Artificial Intelligence applications, MATLAB Simulink program (2022a), and Electrical Transient Analytical Program (ETAP 19.0) were used for modeling, and this result shown in the ETAP software simulations was discussed.
The Fault Index was simulated in MATLAB using the approximate Positive Admittance Sequence (aPSA). Also, several IEEE Benchmarks were validated using the MATLAB program. The evolutionary computing heuristics for the Optimal PMU Placement (OPP) were developed and validated numerically as shown in the method.
Complete research article with detailed methodology, results, and references.
APA Style:
Konstantinou, N., & Fan, L. (2025). Metaheuristic Optimization of Phasor Measurement Unit Placement for Enhanced Fault Detection in Smart Grid Networks. International Journal of Advanced Research in Engineering and Related Sciences, 1(5), 1.
IEEE Style:
N. Konstantinou and L. Fan, "Metaheuristic Optimization of Phasor Measurement Unit Placement for Enhanced Fault Detection in Smart Grid Networks," International Journal of Advanced Research in Engineering and Related Sciences, vol. 1, no. 5, paper 1, 2025.