Overview

This book explores the role of computational intelligence techniques in addressing power quality challenges in modern power systems. It examines the integration of multiple energy sources, renewable energy systems, and energy storage units while analyzing nonlinearities in power plants.

The book introduces optimization methods, including machine learning, genetic algorithms, and neural networks, to enhance power system stability, reliability, and efficiency. It bridges theoretical insights with real-world applications in modern power networks.

Key Topics

Explores the use of AI techniques, including genetic algorithms and fuzzy logic, for power system analysis and optimization.

Examines power quality issues such as frequency deviations, tie-line power flow variations, and area control errors.

Discusses the tuning of secondary controllers, including PID controllers, using advanced optimization algorithms.

Addresses PQ challenges posed by renewable energy integration and energy storage units.

Covers real-time system monitoring and control for maintaining power quality under nonlinear operating conditions.

Provides a structured approach to analysing system behaviour using mathematical modeling and time-domain simulations.

Intended Audience

This book serves as a valuable resource for researchers, professionals, and students in power system engineering, computational intelligence, and smart grid technologies.