In the proposed study, the gain of the PI controllers at outer voltage loop (AC voltage control and DC voltage control) is kept constant and the optimal tuning of PI controller gains by PIPSO is implemented at the inner current loop.
Parameter Improved Particle Swarm Optimization Algorithm (PIPSO)
By extending the concept of existing particle swarm optimization algorithm, PIPSO based technique has been developed.
The parameters of the PIPSO such as [omega], [c.sub.1] and [c.sub.2] are improved by the following equations,
The flowchart of the PIPSO algorithm is shown in Fig.
Step 1: Read PIPSO parameters such as m, n, [n.sub.max], ([[omega].sub.min] and [[omega].sub.max]), ([c.sub.1min] and [c.sub.1max]) and ([c.sub.2min] and [c.sub.2max]).
Though the process may be continuous still the best values of the gains obtained from the three cases has been used with a presumption that it will allow the PIPSO for assuring at the desired performance.
It can be concluded that the GS-VSC with proposed PIPSO based direct current vector control strategy can smoothly regulate the DC-link voltage, during solar insolation variation.
It is inferred that the GS-VSC with proposed PIPSO based direct current vector control strategy can efficiently control the DC-link voltage during solar insolation variation.
The comparisons of power conversion efficiency of the existing control method and the proposed optimal PIPSO based direct-current vector control strategy for different case studies are reported in the Table IV.
This paper purports PIPSO based optimal direct current vector control technique for GS-VSC in a photovoltaic incorporated AC micro-grid under load fluctuations and solar insolation variation.