Designing of an Enhanced Fuzzy Logic Controller of an Interior Permanent Magnet Synchronous Generator under Variable Wind Speed

Abstract

On account of active governmental stimulation operations in many countries, the residential production of electricity from renewable resources has increased considerably. Due to high efficiency and reliability, a recommended solution for residential wind energy conservation systems (WECS) is permanent magnet synchronous generators (PMSG). A higher torque ripple (TR), engendered by the contact of the stator with the rotor’s magnetomotive force harmonics, is one foremost issue in PMSGs. To control the synchronous generator, numerous control schemes have been proposed. However, it still faces a challenge in the diminishment of the TR. An enhanced fuzzy logic controller (EFLC) in interior PMSG (IPSMG) under variable wind speed (WS) has been proposed in this article to address this challenge. Initially, the wind turbine (WT) system was designed, and the IPMSG was proposed. A hysteresis controller (HC) and fuzzy logic controller (FLC) are the two controller types utilized in this model to control TR. This methodology used the EFLC to eliminate errors during the control. By using the proper membership function (MF) for boundary selection in the WDCSO algorithm, an enhancement was executed. Better performance in TR reduction was attained by the proposed model grounded in the analysis.

Improved Performance for PMSM Sensorless Control Based on Robust-Type Controller, ESO-Type Observer, Multiple Neural Networks, and RL-TD3 Agent

“…Therefore, to define a variation in some parameters, e.g., load torque TL, around nominal values, the command ureal() is used. Using the musyn() command, the numerical values for the implementation of the robust controller K(s) are obtained as shown in Relation (16 The implementation of the robust controller follows the steps described in Section 3, as well as a set of instructions specific to robust synthesis, used in the MATLAB Robust Control toolbox environment [25]. Therefore, to define a variation in some parameters, e.g., load torque T L , around nominal values, the command ureal() is used.…”

“…Therefore, to define a variation in some parameters, e.g., load torque T L , around nominal values, the command ureal() is used. Using the musyn() command, the numerical values for the implementation of the robust controller K(s) are obtained as shown in Relation (16).…”

“…The relatively good performance of these types of controllers is well-known, but when significant parametric variations occur, the performance of these types of control systems, based on PI-type controllers, decreases significantly. A number of PMSM control systems have been developed and adapted, including adaptive [9,10], optimal [11,12] and predictive [13,14], but also fuzzy [15,16] and neuro-fuzzy control systems have been invented with good results [17,18]. A backstepping controller for IM is developed in [19].…”

Model Predictive Control for Single-Phase Switched-Capacitor Multilevel Inverters

“…Therefore, it is essential that a proper control technique be developed sooner rather than later to ensure that the new topologies that are being developed will be compatible with the state-of-the-art control techniques and that they can be used well into the future. However, lately, with the development of digital control hardware platforms and fast microprocessors, nonlinear control methods such as Slide Mode Control (SMC) [28], Fuzzy Logic Control (FLC) [29], and Model Predictive Control (MPC) have started to appear in the field of multilevel inverter control [30], [31].…”

Investigation of the Robust Fractional Order Control Approach Associated with the Online Analytic Unity Magnitude Shaper: The Case of Wind Energy Systems

“…However, these methods did not maximize performance while limiting mechanical fatigue of the system. Adaptive algorithms [18], predictive control [19], Fuzzy logic approaches [20], and predictive control using linear matrix inequalities (LMI) [21] have also been applied to maximize the power extracted from the wind. Studies have also compared the performance of linear versus nonlinear control methods [22].…”