Search

In this paper, DC-link voltage control in DC microgrids with photovoltaic (PV) generation and battery, is addressed based on an intelligent approach. The proposed strategy is based on the modeling of the power interface, i.e. power electronic converter, located between the PV array, battery and DC bus, by use of measurement data. For this purpose, a local model network (LMN) is developed to model the converter and then a local linear control (LLC) strategy is designed based on the LMN. Simulation results on a DC microgrid demonstrates efficacy of the proposed approach.

This paper presents a hierarchical control structure for Multi terminal HVDC grids. The hierarchical control structure is inspired by the three-layer control structure of largescale AC power systems and it is divided into primary, secondary and tertiary control. This structure permits to create an easy interaction between power dispatch and the lower controllers. Moreover, a generalized voltage droop (GVD) characteristic is also proposed which facilitates control issues inside the Multi terminal HVDC grids. In the proposed control structure, the coefficients of the GVD characteristics are set based on the solution of a DC power flow algorithm, executed in the secondary control level.

With the rapid proliferation of the DC distribution systems, special attentions are paid to the photovoltaic (PV) generations. This paper addresses the problem of maximum power point tracking (MPPT) for PV systems using a local neuro fuzzy (LNF) network and steepest descent (SD) optimization algorithm. The proposed approach, termed LNF + SD, first identifies a valid an accurate model for the PV system using the LNF network and through measurement data. Then the identified PV model is used for MPPT by SD optimization algorithm. The salient modeling abilities of the proposed LNF network results in a reliable and dependable PV model which takes voltage, temperature and insolation variations into account. The proposed approach is evaluated using several identification and MPPT simulations. The simulation results showed the accuracy of the LNF network in modeling of PV systems. Furthermore, simulations carried out for assessment of the MPPT performance during insolation transients demonstrated the high efficiency of the proposed LNF + SD approach for MPPT applications. Performance of the proposed method MPPT, while the PV array was supplying loads through DC-DC converters was also analyzed. Comparisons to the perturb-and-observe (P&O) and fuzzy logic methods revealed the superiority of the proposed approach