Fuzzy Logic Controller Case Study

To assure the effective performance of controller over wide range of system operations and to increase the transient stability of the system, a supplementary fuzzy logic controller (FLC) based on the Mamdani's fuzzy inference method is designed for the PI controller input. FLC generates the required small change for voltage to control the magnitude of the injected voltage. The centroid defuzzyfication technique is used is this fuzzy controller.
The below fig describes the FLC structure. In this case, a two–input, one–output FLC is considered. These two inputs will be evaluated using a fuzzy logic-based control algorithm and an output signal is produced. The input signals are voltage error and rate of change of voltage error and the resultant …show more content…
(χ = 27, Kp= 3.2516, b = 2134.7; χ = 16.5, Kp= 2.1180, b = 1365.2.)

Fig.10. System responses of Test 1 (L = 10.0 mH, vdc = 450 V, i∗=0.333 pu) when there is a step change in χ from 25.5 to 16. (χ = 25.5, Kp=3.0894, b = 2024.7; χ = 16, Kp= 2.0642, b = 1328.6.)

B. (Test 2) Adapt System Damping to Solar Irradiation

When solar irradiation increases, the PV array would equip the PV inverter with more voltage.The output current of the PV inverter should be increased to acquire more power from the PV array. To relieve the tracking error for the high-irradiation case, a high value of χ compared to Test 1 should be taken up. The best method to decrease the current deviation is to alter χ to decline the control gains to adapt PV inverter to the changed solar irradiation. The consequence of altering χ to the changed solar irradiation is shown in Figs. 11 and 12, which confirms that χ has prominent influence on the current response.

Fig.11. System responses of Test 2 (L = 0.0 mH, vdc= 550 V, i*= 0.5 pu) when there is a step change in χ from 27.0 to 18.7. (χ = 27, Kp=3.2516, b = 2134.7; χ = 18.7, Kp= 2.3552, b =