A Data-Driven Approach to Voltage Stability Support via FVSI-Based Distributed Generator Placement in Contingency Scenarios

Manuel Jaramillo (), Diego Carrión, Filippos Perdikos and Luis Tipan
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Manuel Jaramillo: Smart Grid Research Group—GIREI (Spanish Acronym), Electrical Engineering Department, Salesian Polytechnic University, Quito EC170702, Ecuador
Diego Carrión: Smart Grid Research Group—GIREI (Spanish Acronym), Electrical Engineering Department, Salesian Polytechnic University, Quito EC170702, Ecuador
Filippos Perdikos: Openchip & Software Technologies SL, 08034 Barcelona, Spain
Luis Tipan: Smart Grid Research Group—GIREI (Spanish Acronym), Electrical Engineering Department, Salesian Polytechnic University, Quito EC170702, Ecuador

Energies, 2025, vol. 18, issue 10, 1-31

Abstract: This research presents a novel methodology based on data analysis for improving voltage stability in transmission systems. The proposal aims to determine a single distributed generator’s optimal location and sizing using the Fast Voltage Stability Index (FVSI) as the primary metric under N − 1 contingency conditions. The developed strategy systematically identifies the most critical transmission lines close to instability through a frequency analysis of the FVSI in the base case and across multiple contingency scenarios. Subsequently, the weak buses associated with the most critical line are determined, on which critical load increases are simulated. The Distributed Generator (DG) sizing and location parameters are then optimized through a statistical analysis of the inflection point and the rate of change of the FVSI statistical parameters. The methodology is validated in three case studies: IEEE systems with 14, 30, and 118 buses, demonstrating its scalability and effectiveness. The results show significant reductions in FVSI values and notable improvements in voltage profiles under stress and contingency conditions. For example, in the 30-bus IEEE system, the average FVSI for all contingency scenarios was reduced by 26% after applying the optimal solution. At the same time, the voltage profiles even exceeded those of the base case. This strategy represents a significant contribution, as it is capable of improving the stability of the electrical power system in all N − 1 contingency scenarios with overload at critical nodes. Using a single DG as a low-cost and highly effective corrective measure, the proposed approach outperforms conventional solutions through statistical analysis and a data-centric approach.

Keywords: voltage stability enhancement; contingency analysis ( N − 1); fast voltage stability index; data-driven DG placement; power system resilience (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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