Optimal location of SVC using hybrid ABCPSO, ABC, PSO & GA

MATLAB raw file for journal paper: Optimizing SVC placement for enhanced voltage stability using a novel index and hybrid ABC-PSO algorithm

This MATLAB raw file supports a detailed research study aimed at enhancing voltage stability and optimizing the placement of Static VAR Compensators (SVCs) in power systems. The implementation uses a hybrid optimization algorithm that combines Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) techniques to determine the optimal location of SVCs in standard IEEE 14-bus and 30-bus systems.

Central to this approach is the development and application of the Modified Collapse Prediction Index (MCPI)—a novel voltage stability index that overcomes the limitations of traditional indices such as the Fast Voltage Stability Index (FVSI), Line Stability Index (Lmn), and Line Stability Factor (LQP). Unlike those indices, which often ignore the influence of active power and rely solely on reactive components, MCPI integrates both active and reactive power parameters by combining the strengths of FVSI and a Novel Collapse Prediction Index (NCPI) through a switching function. This dual-parameter approach significantly improves the accuracy of voltage collapse prediction, especially under high loading or contingency scenarios.

The MATLAB file executes a multi-objective optimization routine, where MCPI is used as the main objective function. Additionally, the algorithm minimizes active power losses, voltage deviations, and total SVC installation costs. The relative importance of each objective is determined using the Analytic Hierarchy Process (AHP), allowing for flexible prioritization based on system operator preferences and real-world requirements.

A key feature of this work is its integration of a novel recovery time estimation method to evaluate the economic viability of each optimized solution. The tool assesses the return on investment (ROI) for SVC installations and helps determine whether the system improvements justify the cost. The results reveal significant operational and economic benefits: optimal SVC placement leads to up to 46% savings in reactive power generation and over 7% reduction in reactive power losses. In the best-case scenarios, the ROI is achieved in less than 1.5 years.

The hybrid ABC-PSO algorithm employed in this file is designed for fast convergence, achieving optimal results in just 10 to 40 iterations. It demonstrates a 100% success rate on the IEEE 14-bus system and maintains an average computation time of only 0.041 seconds per iteration, outperforming five other benchmark optimization techniques.

In summary, this MATLAB raw file offers a powerful and efficient framework for improving voltage stability in power systems. By combining advanced voltage stability assessment through MCPI with a flexible, fast, and accurate hybrid optimization algorithm, it provides engineers and researchers with a practical solution for strategic SVC deployment. The code is particularly valuable for those working with IEEE 14-bus and 30-bus systems, and it bridges theoretical innovation with practical, cost-effective implementation.