In the era of increasing renewable energy integration into power grids, ensuring grid stability and resilience has become a critical challenge, particularly in regions with weak grid infrastructure. Voltage stability, as key concern in modern power systems, is significantly impacted by the proliferation of renewable energy sources, which often exacerbate the vulnerabilities of these weak grids. The voltage control loop of a Virtual Synchronous Generator (VSG), commonly implemented using conventional reactive power-voltage droop control, is particularly prone to instability when subjected to disturbances or operating under weak grid conditions. Addressing these challenges is essential for the reliable operation of future power systems. This PhD thesis comprises three interconnected studies addressing crucial aspects of voltage stability and power quality in weak grids, focusing on mitigating the adverse effects of grid disturbances caused by distributed generation (DG), such as solar and wind power. The first study presents an innovative approach to enhance grid stability by optimizing passive LCL (inductor-capacitor-inductor) filters using evolutionary algorithms. Specifically, Multi-Objective Particle Swarm Optimization and Multi-Objective Genetic Algorithm techniques are employed to design filters that improve power quality and minimize harmonic distortion. This study highlights the importance of optimizing filter parameters to address weak grids. Through numerical simulations, the proposed method demonstrates significant improvements in total harmonic distortion (THD) and stability margin when connecting grid-connected converters to weak and very weak grids. The second study utilizes artificial intelligence (AI) to address the persistent issue of voltage instability in modern power systems. It introduces an AI-driven method that combines backpropagation-based artificial neural networks (ANNs) with surrogate optimization techniques. The new model optimizes the droop coefficient of voltage-reactive power control and fine-tunes proportional-integral controller parameters used in VSG strategy control, to enhance the performance of grid-connected converters in weak grids. Stability analyses and simulation results validate the effectiveness of this AI-driven approach in addressing voltage instability in the weak grid conditions. The study proposes an advanced control strategy to enhance voltage stability in weak grids, addressing the limitations of conventional reactive power-voltage droop control, especially in enhanced VSG. This research introduces a novel cascade droop control (CDC) technique that enhances voltage support and improves grid stability, even under challenging conditions. The proposed controller extends conventional droop control with layered control loops, offering improved performance without sacrificing simplicity or cost efficiency. The effectiveness of the CDC method is validated through extensive time- domain and frequency-domain analyses, confirming its ability to reduce voltage fluctuations and enhance grid stability. The three studies advance grid integration technologies by leveraging evolutionary algorithms, advanced control strategies, and AI-driven optimization to improve voltage stability and power quality in weak grids amid renewable energy integration.
