Optimizing SVC placement for enhanced voltage stability using a novel index and hybrid ABC-PSO algorithm - Hafizur Rahman; Roman Mia; Mustafizur Rahman; Md. Faiyaj Ahmed Limon; Md. Shahid Iqbal; Md. Fahad Jubayer; Md. Rabiul Karim; Md. Janibul Alam Soeb (Franklin Open)
Voltage instability poses a great threat to power systems, typically leading to catastrophic failures such as voltage collapse. In order to mitigate such threats, efficient prediction and optimal placement of FACTS devices such as static var compensators (SVCs) are of utmost importance. This paper presents an enhanced technique for maximizing voltage stability and optimizing the placement of SVCs using a Modified Collapse Prediction Index (MCPI). It integrates FVSI with a Novel Collapse Prediction Index (NCPI) with the help of a switching function. Unlike conventional indices such as the Fast Voltage Stability Index (FVSI) and Line Stability Index (Lmn), which rely predominantly on reactive power-based predictions, MCPI relies on both active and reactive power, providing an improved indication of system stability and is utilized as the objective function for the optimal placement of SVCs. A hybrid Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) approach is applied to determine the optimal location of SVCs in the IEEE 14-bus, 30-bus, and 118-bus systems. The optimization is led by four objective functions prioritized by the Analytic Hierarchy Process (AHP) and a novel recovery time calculation method to assess the economic viability of SVC investments. Comparative analysis with FVSI and Lmn demonstrates MCPI’s superior ability to identify critical instability points. In the IEEE 30-bus system, MCPI successfully detects vulnerabilities in a critical transmission line that conventional indices fail to recognize. Optimal placement of SVCs significantly improves operating efficiency and economic performance for all test systems. Maximum integration of SVCs yields up to 46 % savings in reactive power generation and over 7 % savings in reactive power losses. The economic analysis also shows substantial yearly cost savings, with best-case installations achieving return on investment (ROI) within <1.5 years. The ABC-PSO hybrid algorithm converges very quickly to optimal solutions in 10 to 40 iterations and outperforms five state-of-the-art optimizers with a 100 % success rate for the IEEE 14-bus system and a mean computation time of 0.041 s per iteration. These findings demonstrate the robustness and practicality of the methodology and provide system operators with a robust means to ensure grid stability, operational efficiency, and enhanced resilience in the face of increasing load and system complexity.