AREAS DE INFLUENCIA

With the laboratory setup as in Fig. 5.15 and the parameters of the system as described in Table 5.1, the performance of the virtual machine controller for various parameter choices will be verified in this section. First, the response of the controller for a change in the reference active and reactive power is tested. For this test, a virtual inertia ofHv = 3.38 and a virtual damping

constant ofKDv = 67.9 corresponding to a natural frequency of 12.6 rad/s and a damping ratio

of 0.4 for accurate system parameters are chosen for the active power controller. Similarly, the integrator gain is chosen asKQc = 18.5 corresponding to a bandwidth of αQc = 62.8 rad/s for

the reactive power controller. For this choice of parameters, the dynamic performance of the virtual machine controller is shown in Fig. 5.20. It can be observed from the results that the measured active and reactive powers follow the references based on the design.

Fig. 5.20 Dynamic performance of the virtual machine controller; (a) reference (gray) and actual (black) injected active power; (b) reference (gray) and actual (black) injected reactive power; (c) vir- tual angle deviation; (d) converter voltage magnitude; Gray solid curves represent reference active and reactive power.

With zero reactive power injection, the impact of the control parameters KDv and Hv on the

dynamic performance of the active power controller will be investigated next. For three different choice of the parameters, the dynamic response of the active power controller for a step in the reference active power is shown in Fig. 5.21. As the results indicate, the damping in the active power response of the implemented virtual machine model increases with a higher choice of the parameter KDv. On the other hand, a higher choice of the virtual inertia results in a

slower response. This means that any additional inertia added to the system from the converter is achieved at a cost of reduced speed of response.

A final test is made to investigate the performance of the virtual machine controller versus the classical cascade controller. For this purpose, the active power controller parameters are cho- sen as KDv = 33.95 and Hv = 0.14 corresponding to a damping ratio of ς = 1.0 and natural

Fig. 5.21 Impact of parameters on the active power controller; Injected active power (plot a), virtual speed deviation (plot b) and virtual angle deviation (plot c) withHv= 0.54 and KDv = 33.95

(black dashed),Hv = 0.54 and KDv = 67.9 (black solid) and Hv = 0.14 and KDv = 33.95

(gray dashed); Gray solid curve represents reference active power.

frequency of ωn = 62.8 rad/s for accurate system parameters. The bandwidth of the reactive

power controller is chosen as αQc = 62.8 rad/s for the virtual machine controller. The gray

solid curves in plot (a) and (b) in Fig. 5.22 represent the performance of the virtual machine controller for this choice of parameters. The reactive power controller performs similar to the simulation result in Fig. 5.14. On the other hand, the active power controller is characterized by an overshoot unlike the simulation results in Fig. 5.14. This is due to inaccuracies in the esti- mated parameters of the system resulting in the actual damping ratio to be less than 1. To avoid the overshoot, the damping term is increased toKDv = 67.9 and the test is repeated. The black

solid curves in plot (a) and (b) in Fig. 5.22 represent the performance of the virtual machine controller for the new choice of parameters. In this case, both the active and reactive power controllers perform similar to a first-order system with no overshoot. For a fair comparison, an integral outer loop active and reactive power controller that gives a bandwidth of 62.8 rad/s is selected for the classical cascade controller and its dynamic performance is shown in Fig. 5.22 (plots (c) and (d)). It can be observed that the performance of the classical cascade controller is similar to the simulation results obtained in Fig. 5.14. In the classical cascade controller, the

fast inner current controller helps to mitigate the impact of system parameter error and grid harmonics on the dynamic response of the power controllers.

Fig. 5.22 Comparison of the virtual machine controller versus the classical cascade controller; Injected active power (plot a) and reactive power (plot b) using virtual machine controller withKDv =

33.95 (gray solid) and KDv = 67.9 (black); Injected active power (plot c) and reactive power

(plot d) using classical cascade controller; Dashed curves represent reference power and solid curves represent actual power.

By adjusting the parameters of the virtual machine controller, a similar performance to the clas- sical cascade controller can be achieved without the need for an inner vector-current controller and the use of a PLL (see black curves in Fig. 5.22). However, the use of a current controller and a PLL is necessary during fast transients like fault conditions where converter current needs to be limited. The experimental results in this section verify the validity of the virtual machine controller as described in Sections 5.4 and 5.5.2.

5.7

Conclusions

The overall control approach for the E-STATCOM has been described in this chapter. First, a classical cascade controller that uses an inner vector-current controller has been derived. Modi- fications to the inner current controller to deal with grid-harmonics disturbances has been pro- posed. Next, an approach to control the E-STATCOM like a synchronous machine has been described. The performance of both control methods has been verified and compared through simulation and experimental results. A detail description of the auxiliary control loops using the two approaches for POD and TSE will be discussed in the next chapter.

Control of E-STATCOM for power system

stability enhancement