Research Article

A Data-Driven Approach for Reactive Power Optimization Incorporating Interval Values for Renewable Power Generation

Table 3

Parameters settings.

AlgorithmsSettings

GAPopulation size = 50, iterations = 200, crossover probability = 0.6, mutation probability = 0.01, and selection method: tournament selection
I-GWOPopulation Size = 50, iterations = 200, and was linearly decreased from 2 to 0
Improved PSOPopulation Size = 50, iterations = 200, inertia weight = [0.4, 0.9], , , , , and