Implementation and Validation of an Adaptive Fuzzy Logic Controller for MPPT of PMSG-Based Wind Turbines

Abstract

To ensure the full benefits from wind generation systems, an effective maximum power point tracking (MPPT) technique should be implemented. In this article, an adaptive fuzzy logic controller (AFLC) is presented as a control methodology for producing the peak power from a permanent magnet synchronous generator (PMSG)-based wind turbine (WT). Regardless of the importance and significance of modeling and simulation processes, experimental studies occupy the most important aspect and remain a big challenge. The first part of this article is devoted to the simulation and modeling of PMSG along with the control system by MATLAB/Simulink ® package to develop the control system. To justify the simulation results, experimental validation tests are presented under the same status of wind speed profile and run period. The experimental validation is conducted using the DSPACE DS1104 control board and is compared with simulation results. Moreover, for a realistic response, actual wind speed data is utilized in this research depending on measured data from Ras Ghareb wind farm in the Gulf of Suez, Egypt. The obtained results confirm the superiority of AFLC compared with fuzzy logic controller and conventional PI control schemes. In addition, good tracking with high accuracy is obtained regarding experimental and simulations results. Moreover, an evaluation indices are employed for WT performance based on gross system efficiency and integral of the absolute error (IAE). These indices are used to demonstrate the feasibility of the AFLC methodology compared with traditional approaches under the same wind turbine status.

Reinforcement Learning-Based Adaptive Optimal Fuzzy MPPT Control for Variable Speed Wind Turbine

“…It is noted that the pitch angle β of the blades is 218 always constant during the MPPT control process in the event 219 of the wind speed being below the rated value. The value of 220 C p is calculated as the following [24]:…”

“…In the field of WECS, the control schemes based on learning techniques are also widely popular [17], [18], [19], [20], [21], [22], [23], [24], [25]. These intelligent methodologies can be applied for wind speed estimation [17], [18], MPP searching algorithms [19], [20], or MPP tracking methods [21], [22], [23], [24], [25].…”

“…In the field of WECS, the control schemes based on learning techniques are also widely popular [17], [18], [19], [20], [21], [22], [23], [24], [25]. These intelligent methodologies can be applied for wind speed estimation [17], [18], MPP searching algorithms [19], [20], or MPP tracking methods [21], [22], [23], [24], [25]. In the MPP tracking category, the nonlinearities in the dynamic model and disturbances are identified by the fuzzy logic system then they are compensated by the adaptive fuzzy controller [21] or sliding mode controller [22].…”

“…In [23], the current loop is controlled directly by a fuzzy logic controller with input tracking errors to overcome the disadvantages of the PI controller. The fuzzy logic technique is also employed in [24] to calculate the electromagnetic torque for current loop control using the error of mechanical speed and the change of this error as the inputs. This control structure enhances the performance of the WT under oscillation wind speed and guaranteed efficient and reliable grid integration of the wind turbine.…”

“…-Unlike the existing control algorithm for MPPT control of WECS [5], [6], [7], [8], [9], [10], [11], [12] and [21], [22], [23], [24], [25], the proposed controller is output feedback and does not use the system dynamic model as well as system parameters. Also, the requirement for a mechanical torque observer is omitted.…”