Conceitos essenciais
Comparative analysis of loss minimization algorithm and particle swarm optimization for improving the performance and stability of electric distribution systems.
Resumo
The article presents a comparative study of two optimization algorithms, the loss minimization algorithm and the particle swarm optimization (PSO), for improving the efficiency of electric power distribution systems.
The key highlights and insights are:
Modeling of electric distribution systems: The article describes the network infrastructure, operational limits, electrical devices, and power losses (active and reactive) in the distribution system.
Loss Minimization Algorithm:
The algorithm aims to optimize a combined loss reduction strategy by considering the cost of power loss, replacement cost of distribution lines and transformers, and the cost of reactive power compensation.
The algorithm uses a cost-benefit ratio approach to select the optimal loss reduction strategy.
Constraints include power loss rate, power flow, branch transmission capacity, node voltage, and reactive power compensation capacity.
Particle Swarm Optimization (PSO):
PSO is a metaheuristic optimization algorithm that mimics the behavior of a swarm of particles to find the optimal solution.
The algorithm updates the position and velocity of each particle based on its own best position and the global best position of the swarm.
The proposed PSO-based solution aims to minimize the real power loss while maintaining the voltage magnitude.
Results and Discussion:
The article presents the results of applying the loss minimization algorithm and the PSO algorithm on a dataset of electric consumption profiles.
The loss minimization algorithm shows better performance in terms of the cost-benefit ratio compared to the PSO algorithm.
The active power is more fluctuating than the reactive power when applying the PSO algorithm.
Conclusion:
The loss minimization algorithm is more effective than the PSO algorithm in optimizing the electric distribution system performance.
The article highlights the importance of comparative analysis of different optimization techniques for improving the efficiency of electric distribution networks.
Estatísticas
The dataset used in the study includes the following metrics:
Global active power
Global reactive power
Global intensity
Citações
"Power systems are very large and complex, it can be influenced by many unexpected events this makes power system optimization problems difficult to solve, hence methods for solving these problems ought to be an active research topic."
"The cost-benefit ratio, μLR, represents the ratio of the cost of loss reduction, CLR, to the benefit of loss reduction, BLR."