Introduction:

Traditionally, RPD aims to minimize active power losses while improving voltage profiles and enhancing power transfer capabilities. It adjusts reactive power control variables such as generator voltages, transformer tap-settings, and capacitor banks to achieve optimal system performance.

The introduction of Particle Swarm Optimization (PSO), a population-based optimization technique inspired by the social behavior of birds, has proven effective for addressing RPD problems. However, PSO faces challenges like premature convergence and limited exploration in complex, high-dimensional search spaces. To overcome these limitations, the Comprehensive Learning Particle Swarm Optimization (CLPSO) method was proposed in this study by Mahadevan and Kannan.

Problem Definition:

The RPD problem is a complex, multi-objective optimization task that seeks to:

• Minimize real power losses.
• Improve voltage profiles.
• Enhance voltage stability in power systems.

Achieving these objectives ensures improved system security, stability, and overall operational efficiency. The research focuses on optimizing the control variables that impact reactive power flow in the grid, particularly using the standard IEEE 30-bus and 118-bus test systems for experimentation.

Methodology:

PSO vs. CLPSO:

PSO operates by having a swarm of particles (solutions) move through the search space based on their own best-known positions and the best-known positions of other particles. While effective, PSO is prone to premature convergence, meaning that it can get stuck in local optima, especially when dealing with large-scale optimization problems.

CLPSO, introduced in this study, addresses this challenge by employing a comprehensive learning strategy that enhances the particle’s exploration capabilities. Instead of using only the global best solution for guidance, each particle learns from multiple other particles in the swarm. This diversity in learning paths allows CLPSO to escape local optima, making it more effective in exploring complex solution landscapes.

Test Systems:

The research utilized two standard IEEE test systems:

• 30-bus system: A relatively smaller test case for validating the basic performance of the optimization algorithms.
• 118-bus system: A more complex system that presents a greater challenge for reactive power dispatch optimization.

Key Results:

The performance of PSO and CLPSO was compared using the following metrics:

• Minimization of Real Power Losses: Both PSO and CLPSO demonstrated their capability to reduce real power losses significantly. However, CLPSO consistently outperformed PSO in achieving better optimization results, particularly in the larger 118-bus test case.
• Voltage Profile Improvement: By adjusting reactive power control variables, both algorithms managed to improve the overall voltage profiles across the grid. CLPSO’s superior exploration ability led to more stable and improved voltage profiles, particularly in the larger test system.
• Voltage Stability Enhancement: CLPSO outperformed PSO in enhancing the voltage stability of the grid. Its comprehensive learning strategy enabled it to identify better solutions to maintain voltage stability under varying conditions.

AI Perspective:

The application of AI-driven optimization algorithms like CLPSO in solving power grid-related problems marks a significant leap toward smart grid management. CLPSO's ability to explore diverse solutions without premature convergence showcases the potential of AI techniques in handling large, complex, and multi-dimensional optimization tasks. In power systems, where stability, efficiency, and reliability are paramount, advanced AI algorithms like CLPSO can significantly improve operational outcomes.

Comparative Analysis:

The study conclusively shows that both PSO and CLPSO are feasible for solving the RPD problem. However, CLPSO demonstrated a clear advantage in overcoming the premature convergence issues commonly associated with traditional PSO. Its superior exploration capabilities led to better optimization performance, especially in larger systems with more complex dynamics.

Conclusion:

This study illustrates the powerful role AI can play in optimizing power system operations through advanced algorithms like CLPSO. By enhancing voltage profiles, reducing real power losses, and improving voltage stability, CLPSO contributes to more efficient, secure, and reliable power grid operations. The research indicates that AI-driven approaches are essential for addressing the increasing complexity of modern power systems, offering promising solutions for large-scale reactive power dispatch optimization. The success of CLPSO in overcoming the limitations of traditional PSO underscores the potential of intelligent algorithms to revolutionize grid management and pave the way for smarter, more resilient energy systems.

Implications:

As the energy landscape continues to evolve toward more distributed and renewable power sources, the demand for intelligent, adaptive optimization techniques will grow. CLPSO’s demonstrated effectiveness in RPD suggests that similar AI-based methods could be leveraged to optimize other critical power system functions, such as load forecasting, demand-side management, and renewable energy integration.

Authors

K. Mahadevan - Electrical & Electronics Engineering, Kalasalingam University, Krishnankoil – 626 190, Tamilnadu, India

P.S. Kannan - Electrical & Electronics Engineering, Thiagarajar College of Engineering, Madurai – 625 015, Tamilnadu, India

Source: Article

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