What does this research mean for the field?

Advanced SVC strategies significantly enhance reactive power control and stability in Hybrid Power Systems (HPS). Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.SUPPORTS_CONSENSUS.

What question did this study set out to answer?

The research aims to enhance the stability and control of reactive power in hybrid power systems using advanced SVC strategies.

February 26, 2026Open Access

Enhanced Reactive Power Control and Stability Analysis of Hybrid Power System Using Lyapunov and Snake Algorithm-based SVC Strategies

Key Points

The research aims to enhance the stability and control of reactive power in hybrid power systems using advanced SVC strategies.
Conducted systematic analysis of Multiple Static Var Compensators (SVCs) in various Hybrid Power System (HPS) configurations.
Applied Lyapunov theory and Snake Optimization Algorithm for tuning SVC performance.
Performed robust simulations in MATLAB to evaluate control strategies under different reactive load conditions.
SVC Type 3, optimized via Snake Algorithm, achieved the fastest response time of 0.015 seconds.
SVC Type 2 excelled in voltage and exciter control with minimal voltage deviations.
SVC Type 1 maintained system stability but had slower response and higher deviations compared to other types.

Abstract

• This work focusses on Multi-SVC, Multi-HPS systematic and comparative analysis. • Lyapunov ensures stability via ISE, SOA tunes PI via ITAE for optimized control. • Robust simulation-based validation of the system is performed using MATLAB. • Enhanced reactive power control achieved using advanced SVC strategies. • SVC3 shows fastest response, best damping; SVC2 excels in voltage, exciter control. Static Var Compensator (SVC) is a key FACTS device for enhancing the stability of Wind–Diesel Hybrid Power System (HPS) by dynamically managing reactive power, regulating voltage, and reducing stress on Diesel Generator (DG). The study introduces an advanced control approach involving Lyapunov theory and the Snake Optimization Algorithm (SOA) to optimize SVC performance. The proposed framework is evaluated on three distinct HPS configurations comprising a wind-driven Induction Generator (IG) and a (DG)-driven Synchronous Generator (SG). SVC meets the IG’s reactive power demand and compensates for load fluctuations. Type 1 and Type 2 SVCs are tuned via Lyapunov-based Integral Square Error minimization. SVC Type 3 is tuned via SOA-based Integral Time Absolute Error minimization. The transient stability of HPS under step changes in reactive load is assessed with comprehensive simulations, supported by Eigenvalue (EGV) and Damping Ratio (DR) analyses. SVC Type 3 (SOA-tuned PI) consistently outperforms others, achieving the fastest settling times (0.015 sec in HPS3) and highest DR (0.211) for the dominant oscillatory mode. It ensures rapid oscillation damping and stability enhancement. SVC Type 2 delivers the best voltage and exciter control, recording minimal deviations in terminal and exciter voltage, making it suitable for voltage-sensitive applications. SVC Type 1 maintains stability but exhibits slower response and higher deviations, particularly in HPS1. The study demonstrates that metaheuristic-tuned SVCs significantly improve both dynamic and stability metrics in HPS. The methodology offers scalability to other renewable configurations, such as solar–micro hydro, paving the way for robust, efficient, and sustainable HPS operation.

Enhanced Reactive Power Control and Stability Analysis of Hybrid Power System Using Lyapunov and Snake Algorithm-based SVC Strategies

Key Points

Abstract

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