Application of Fractional-Order PI Controllers and Neuro-Fuzzy PWM Technique to Multi-Rotor Wind Turbine Systems

Abstract

In recent years, the methods of controlling electrical machines have been witnessing increasing development to reduce torque and electric current fluctuations in electrical power generation systems from renewable sources such as wind energy. The generation of electric power from wind plants imposes the need for an efficient and more robust method in order to obtain fewer ripples in active and reactive power. In this work, a new fractional-order proportional-integral (FOPI) controller and intelligent PWM (IPWM) technique are proposed to control an existing asynchronous generator (AG) in variable-speed multi-rotor wind turbines (VSMRWTs). This proposed method depends on combining or using two methods, namely nonlinear area and fractional calculus, to obtain a more robust method and to reduce current and torque ripples. In the framework of this study, the electric power generation system consists of a 1.5 MW AG and VSMRWTs. The AG is controlled using a simpler and easily accomplished method called direct vector control, based on FOPI controllers and the IPWM technique (DVC-FOPI-IPWM). The maximum power point tracking (MPPT) method is used to generate the maximum energy from the VSMRWTs. The proposed DVC-FOPI-IPWM technique is modeled in the Matlab/Simulink platform to obtain good quality current and active power. Simulation results show that the proposed strategy reduces the ripples of torque, current, and active power compared to the classical technique. Moreover, the reduction ratio is about 85%, 99%, and 93.33% for the current, active power, and torque, respectively.

Fractional-order neural control of a DFIG supplied by a two-level PWM inverter for dual-rotor wind turbine system

“…This type of turbine has been discussed in several scientific works. [25][26][27][28][29] In Benbouhenni, 32 DRWT offers 23%-30% more power than conventional turbines. Equation (A8) represents the total torque of the DRWT.…”

“…28 In addition, the use of fractional-order control is not related to the mathematical model of the system, which allows the system not to be complicated and thus easy to implement. Also, excellent results are obtained in the event of a system malfunction as investigated in Benbouhenni et al 29 In the latter, a PI controller based on fractional-order control was used to control the Q s and P s of the DFIG-DRWT system, where the proposed control is characterized by simplicity, high robustness, and ease of adjusting the dynamic response. The use of PI based on fractional-order control led to a reduction in the active and reactive power ripples, especially in the case of system parameters changing in large proportions compared to the traditional control, which is a good thing.…”

“…This type of turbine has been discussed in several scientific works. 25–29 In Benbouhenni, 32 DRWT offers 23%–30% more power than conventional turbines. Equation (A8) represents the total torque of the DRWT. where, T 1 and T 2 are the torque of the first and second turbines and T DRWT is the total torque of the turbine. Equation (A9) and (A10) represent each of the torques of the two turbines used to implement DRWT, 27,28 where the value of the torque of each turbine is related to wind speed ( w ), coefficient of power ( C p ), and tip speed ratio (λ). Where, R 1 and R 2 are the blade radius of the first and second turbines, ρ is the air density, λ 1 and λ 2 are the tip speed ration of the first and second turbines Equation (A11) and (A12) represent each of the λ of the two turbines, where the WS is large, the λ is small, which makes the torque value large, and this is a good thing.…”

Intelligent control of the power generation system using DSPACE

“…Research findings indicate that the use of hysteresis comparators in power control methods has drawbacks, including power and current ripples. Several proposed solutions aim to enhance the capabilities of the DPC strategy, such as synergetic control [15], third‐order sliding mode control [16], fuzzy logic [17], fractional‐order control [18], neuro‐fuzzy logic [19], backstepping control [20], sliding mode control [21], genetic algorithm [22], and ANN‐DPC [26].…”

“…DFIG, double-fed induction generator tors in power control methods has drawbacks, including power and current ripples. Several proposed solutions aim to enhance the capabilities of the DPC strategy, such as synergetic control [15], third-order sliding mode control [16], fuzzy logic [17], fractional-order control [18], neurofuzzy logic [19], backstepping control [20], sliding mode control [21], genetic algorithm [22], and ANN-DPC [26]. This study integrates intelligent neural algorithms to enhance the quality of electric current and mitigate ripples in active power, current, and reactive power of the DFIG.…”