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Hybrid fuzzy particle swarm optimization approach for reactive power optimization

机译:混合模糊粒子群算法在无功优化中的应用

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摘要

This paper presents a new approach to the optimal reactive power planning based on fuzzy logic and particle swarm optimization (PSO). The objectives are to minimize real power loss and to improve the voltage profile of a given interconnected power system. Transmission loss is expressed in terms of voltage increments by relating the control variables i.e. reactive var generations by the generators, tap positions of transformers and reactive power injections by the shunt capacitors. The objective function and the constraints are modeled by fuzzy sets. A term ‘sensitivity’ at each bus is defined which depends on variation of real power loss with respect to the voltage at that bus. Based on the Fuzzy membership values of the sensitivity, corrective action at a particular bus is taken i.e. shunt capacitors are installed at the candidate buses based on real power loss and sets of solution. Then, PSO is applied to get final solution. PSO is used for optimal setting of transformer tap positions and reactive generations of generators. The solutions obtained by this method is compared with the solutions obtained by other evolutionary algorithms like genetic algorithm (GA), differential evolution (DE) and particle swarm optimization (PSO).
机译:本文提出了一种基于模糊逻辑和粒子群算法(PSO)的最优无功规划方法。目的是最大程度地减少实际功率损耗并改善给定互连电源系统的电压曲线。通过将控制变量(即发电机的无功功率产生,变压器的抽头位置和并联电容器的无功功率注入)相关联,以电压增量表示传输损耗。通过模糊集对目标函数和约束条件进行建模。定义了每条总线上的“灵敏度”一词,这取决于实际功率损耗相对于该总线上的电压的变化。根据灵敏度的模糊隶属度值,在特定母线上采取纠正措施,即根据实际功率损耗和解决方案在候选母线上安装并联电容器。然后,应用PSO获得最终解决方案。 PSO用于优化变压器抽头位置和发电机的无功发电。将通过此方法获得的解与通过其他进化算法(例如遗传算法(GA),差分进化(DE)和粒子群优化(PSO))获得的解进行比较。

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