**6. Experimental Results**

In order to verify the feasibility of the PMS and the reliability of the proposed control strategies in practice, the experiments have been conducted using a laboratory testbed system. Figure 17 shows the configuration of a laboratory DCMG system which consists of three power converters for the connections with the grid, battery, and WPGS. For the grid agent, a bidirectional AC/DC converter which is connected to 3-phase grid through Y-Δ transformer and LCL filters, is employed to exchange the power between the grid and DC-link. In the WPGS agent, a PMSG coupled with an induction machine is used to emulate the wind turbine. The developed power of the WPGS agen<sup>t</sup> is adjusted by controlling the rotating speed of the induction machine through the induction machine drive system. The output power of PMSG is transferred to DC-link by a unidirectional AC/DC converter. In this study, the DSP TMS320F28335 is used to control the converters. The detailed information of the experimental rig can be seen in Figure 2.

**Figure 17.** Configuration of the experimental DCMG system.

## *6.1. Grid-connected Case*

Figure 18 shows the experimental results for system operation in the grid-connected mode by using MAS-based distributed control under the variations of load and wind power. Figure 18a shows DCMG operation under load variation. Initially, the grid agen<sup>t</sup> operates with DCVM-REC to supply the power of 1.2 kW from the grid to DCMG. As the load demand is increased suddenly from 1.2 kW to 2.8 kW, the grid agen<sup>t</sup> increases supplied power from the grid to DCMG to ensure supply–demand power balance. Similar to the simulation result in Figure 10, as the grid supplied power reaches the maximum exchange power of 2 kW, the grid agen<sup>t</sup> switches the operation into CPCM, and sends the data (*Gctrl* = 0) to other agents. After receiving the data, the battery agen<sup>t</sup> switches the operation into DCVM-DIS to compensate for the deficit power.

**Figure 18.** Experimental results for grid-connected mode. **(a)** Load variation; **(b)** Mode transition of grid agen<sup>t</sup> from DCVM-REC to DCVM-INV.

Figure 18b shows the system behavior under wind power variation. Before the variation, the WPGS agen<sup>t</sup> works with the MPPT mode, and the grid agen<sup>t</sup> operates with DCVM-REC to supply the power from the grid to DCMG. When the wind power increases beyond the load demand, the grid agen<sup>t</sup> switches the operation DCVM-REC into DCVM-INV to inject the surplus power to the grid. As can be seen from Figure 18, the DCV is stably regulated at 400 V by using the MAS-based distributed control in spite of the variations of load demand and wind power. In these results, Figure 18a corresponds to the instant of t = 1 s in the simulation result of Figure 10, and Figure 18b corresponds to the instant of t = 2.5 s in Figure 10.

## *6.2. Islanded Case*

Figure 19 shows the experimental results for MAS-based distributed control during the transition from the grid-connected to the islanded mode, and vice versa. Figure 19a illustrates DCMG operation for the transition from the grid-connected to islanded mode due to grid fault. Before the grid fault occurs, the system power balance is well maintained by the grid agent. At the instant of grid fault, the battery agen<sup>t</sup> starts the operation DCVM-DIS to compensate for the deficit power caused by the absence of grid. Consequently, the DCV is reliably maintained at 400 V. Figure 19b shows the results for the transition from the islanded to grid-connected mode. Before the grid is recovered, the battery agen<sup>t</sup> maintains the DCV at the nominal value by DCVM-CHA to absorb the surplus power of 0.8 kW. Without communication problems, as soon as the grid is recovered, the battery agen<sup>t</sup> stops the operation DCVM-CHA. Instead, the grid agen<sup>t</sup> activates DCVM-INV to regulate the DCV stably by injecting the surplus power to the grid. These experimental results correspond to the simulation results at t = 1 s and t = 3.5 s in Figure 11 and validate the feasibility of the PMS by using MAS-based distributed control.

**Figure 19.** Experimental results for islanded mode. (**a**) Transition from grid-connected to islanded mode; (**b**) Transition from islanded mode to grid-connected mode.

#### *6.3. Case of Communication Problems*

This Section presents the experimental results of the proposed DCV restoration and grid recovery identification.

Figures 20a and 20b show the results of the proposed DCV restoration under the delay in grid fault detection and communication by battery agen<sup>t</sup> without SHED and with SHED, respectively. In Figure 20a, the DCV is controlled by the grid agen<sup>t</sup> while the battery agen<sup>t</sup> is in IDLE mode before the grid fault. At the instant of the grid fault, according to the proposed DCV restoration algorithm in Figure 7, the battery agen<sup>t</sup> executes CPCM and DCVM-DIS in turn to restore the DCV, which coincides well with Figure 12 in the simulation. Similar to Figure 13 in the simulation, the proposed DCV restoration algorithm executes CPCM by the battery agent, SHED by the load agent, and DCVM-DIS by the battery agen<sup>t</sup> in turn to regulate the DCV stably. Figure 20c shows the results of the proposed DCV restoration by the WPGS agen<sup>t</sup> under the delay in grid fault detection and communication. Before the grid fault, the DCV is regulated at 400 V with DCVM-REC by the grid agent. When the grid has a fault and the battery cannot be used to control the DCV, the WPGS agen<sup>t</sup> intervenes to take a role of DCV regulation as shown in Figure 20c.

**Figure 20.** *Cont.*

**Figure 20.** Experimental results of the proposed DCV restoration. (**a**) By battery agen<sup>t</sup> without SHED; (**b**) By battery agen<sup>t</sup> with SHED (**c**) by WPGS agent.

All the experimental results are matched well with the simulations in Figure 12 through Figure 14, which confirms the feasibility of the DCV restoration algorithm.

Figure 21 shows the experimental results of the grid recovery identification scheme under the communication failure by the battery agen<sup>t</sup> in Figure 21a and by the WPGS agen<sup>t</sup> in Figure 21b, respectively. In Figure 21a, the DCV is regulated with DCVM-CHA by the battery agen<sup>t</sup> before the grid recovery. Once the grid is recovered, the battery agen<sup>t</sup> can detect the grid recovery by using the proposed grid recovery identification scheme and switches the operation into IDLE mode. Instead, the DCV regulation is taken over by the grid agen<sup>t</sup> with DCVM-INV. In Figure 21b, if the grid is recovered when the WPGS agen<sup>t</sup> controls the DCV, the WPGS agen<sup>t</sup> identifies the grid recovery, and switches the operation into the MPPT mode. Similarly, the DCV regulation is taken over by the grid agen<sup>t</sup> from this instant. These results also accord well with the simulation results in Figures 15 and 16.

**Figure 21.** Experimental results for the grid recovery identification under communication failure. (**a**) By battery agent; (**b**) By WPGS agent.

It is confirmed that the grid recovery can be effectively detected by using the proposed scheme even under communication failure in the presence of the disturbance such as load power variation. As a result, the DCV can be well regulated to the nominal value without any conflict in the DCV control by two voltage control sources.

In case of the grid fault, the DCV can be restored to the nominal value in 0.1~0.2 s in the simulation results of Figure 12–14, and in 0.2~0.5 s in the experimental results of Figure 20, depending on the communication speed, the converter control dynamics, and DC link capacitance. In both the simulation and experiment, the maximum variation of the DCV during restoration process is obtained as Δ*VDC*/*VDC* = 40 400·100% = 10% as shown in Figures 13 and 20b.

In case of the grid recovery, the grid recovery can be detected within 0.6 s even under the communication failure as shown in both the simulation and experiment of Figure 15, Figure 16, and Figure 21. The maximum variation of the DCV during detection process is Δ *VDC*/*VDC* = 10 400 ·100% = 2.5%.
