Advances ın Power Systems, Renewable Energy Integratıon and Electrıcal Machıne Desıgn

This academic work addresses key challenges in modern power systems by focusing on renewable energy integration and electrical machine design. The integration of renewable energy sources, particularly photovoltaic (PV) systems, introduces significant variability and uncertainty into the optimal power flow (OPF) problem. Classical deterministic optimization methods are often insufficient, leading to the adoption of metaheuristic algorithms such as Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA). The study contrasts these algorithms and their versions enhanced with Fitness-Distance Balance (FDB) selection strategies to improve convergence and diversity, employing lognormal stochastic modeling of PV output and testing on IEEE 30- and 57-bus systems with rigorous statistical validation. Additionally, a Hybrid Lévy-based Whale-Grey Wolf Optimization Algorithm (HLWOA) is proposed for solving a four-objective OPF incorporating fuel cost, power losses, emissions, and voltage deviation on the IEEE RTS 24-bus system across multiple renewable penetration scenarios. This hybrid algorithm integrates complementary search mechanisms and introduces Lévy flight perturbations, showing superior Pareto front quality and robustness. Further, a detailed speed-dependent electromagnetic performance analysis of Interior Permanent Magnet (IPM) machines using 2D finite element analysis reveals how torque capability, losses, ripple, and efficiency vary with speed. Complementary investigations compare the impacts of different magnet materials (NdFe, SmCo, AlNiCo) on performance-stability trade-offs. The contributions provide novel algorithmic approaches for multi-objective OPF under renewable uncertainties and practical insights into electrical machine design for high-performance applications.