HGPSO Based Reactive Power Optimization of Distribution Network with Photovoltaic Generation

Abstract

With large centralized photovoltaic (PV) power stations connected to the grid, its effects on the distribution networks cannot be ignored. The mathematical model of a photovoltaic power station was built and the power outputs of this model with the changes of illumination were simulated. One photovoltaic power station and one reactive power compensation capacitor were accessed to IEEE 30-node network. The voltages of generators, location and capacity of the PV power station and reactive power compensation capacitor are regarded as controllable variables. A hybrid intelligent algorithm (HGPSO) combining GA and PSO was applied to optimize the reactive power and voltage of the IEEE 30-node network and an optimal power flow distribution and the minimum network losses are realized.

Capacitive Power Transfer With Series-Parallel Compensation for Step-Up Voltage Output

“…La estrategia más utilizada para la variación del factor de potencia es el control de caída convencional (droop control), que permite determinar la cantidad de potencia reactiva en función de la tensión medida en el punto de conexión y sin necesidad de una red de comunicación (Demirok et al, 2011;Gandhi, Rodriguez-Gallegos, Reindl, & Srinivasan, 2018;Momeneh et al, 2016). Aunque, desde luego, la técnica puede ser mejorada mediante infraestructura de comunicación que permita balancear la absorción de potencia reactiva entre diferentes plantas conectadas a la red (Akhtar, Amir Alavi, & Mehran, 2018;Li et al, 2012;Momeneh et al, 2016).…”

Comparative study of reactive power control methods for photovoltaic inverters in low‐voltage grids

“…Traditionally, the over voltage problem has been solved by reactive power compensation methods [19][20][21][22][23][24][25][26]. The conventional technique is based on a droop function [19,20].…”

“…The advances in networked control systems, where low bandwidth communication infrastructure links cooperating entities, offer new scenarios to possibly improve the performance of conventional techniques and/or to achieve complementary operating features. For example, in [21][22][23], the local data is sent to all the PV units with the aim of equalising the reactive power injection. In addition, non-linear control techniques have been used to optimise the operation of these systems, such as fuzzy logic in [24], neural network in [25], swarm optimisation in [26], and genetic and evolutionary algorithms in [27][28][29].…”