PSO Applied to PMU Configuration
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This article demonstrates how to apply the Particle Swarm Optimization (PSO) algorithm to PMU configuration through a simplified example. After practical testing and validation, this method proves effective and provides reliable results. The implementation typically involves defining the objective function for optimal PMU placement, initializing particle positions and velocities representing possible configurations, and iteratively updating these parameters using PSO's velocity and position update equations. Key algorithmic components include fitness evaluation based on measurement redundancy or observability criteria, and social/cognitive parameter tuning for convergence optimization. In the following sections, I will detail the step-by-step implementation process, including code structure for fitness function calculation and swarm optimization loops, and explain how PSO enhances PMU configuration performance and accuracy through intelligent search space exploration.
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