Maximum Power Extraction from Wind Turbines Using a Fault-Tolerant Fractional-Order Nonsingular Terminal Sliding Mode Controller

Observer-based fractional-order dynamic terminal sliding mode control of active shock absorbing prostheses for lower limb amputees

Recent biomedical engineering developments have empowered prosthetic devices to evolve from purely mechanical devices to more sophisticated controlled devices, allowing amputees to perform advanced locomotion modes such as passing stairs and walking on sloped surfaces. However, the strongly coupled nonlinear system dynamics make it difficult for the lower-limb prosthesis (LLP) to adapt to complex tasks and isolate the vibrations and acceleration from the residual limb soft tissue. In this regard, realizing the potential of active LLPs to increase user mobility and efficiency requires reliable, stable, and intuitive control strategies to provide a comfortable gait quality. In this study, a fractional-order dynamic terminal sliding mode controller (FDTSMC) is proposed to effectively isolate the residual limb soft tissue from the vibrations and acceleration arising from the pylon and foot. The proposed sliding surfaces guarantee the fast finite-time system states' convergence, and the chattering is remarkably alleviated. Furthermore, since from the practical viewpoint, the actuators are non-ideal and are affected by dead-zone and hysteresis that degrade the LLP's performance, an observer is augmented with the control system to estimate the lumped uncertainties and compensate for the effects of model nonlinear dynamics and disturbances. The closed-loop system stability is ensured in terms of Lyapunov concept. Comparative performance investigations in ideal and non-ideal situations are carried out, and the proposed control scheme's favorable gait shock absorption performance over observer-based conventional SMC and dynamic SMC approaches is revealed.

Solving the problem of power ripples for a multi-rotor wind turbine system using fractional-order third-order sliding mode algorithms

Power quality is one of the most prominent challenges hindering the spread and use of direct power control (DPC) in the field of control, especially for induction generator (IG) control. The lower power quality in the case of using the DPC approach is due to the use of hysteresis comparators. This work proposes a new controller to overcome the drawbacks of the DPC approach, such as low robustness and high total harmonic distortion (THD) value of current for IG present in multi-rotor wind turbine (MRWT) based power system. The proposed controller is fractional-order third-order sliding mode control (FOTOSMC), as this controller is used to determine reference values ​​for a voltage. In addition to using the FOTOSMC controller, the pulse width modulation strategy is used to control the operation of the machine inverter. The proposed approach differs from the traditional DPC approach and existing controls. This proposed approach is characterized by high robustness and high performance in improving power quality. The DPC approach based on the FOTOSMC controller was implemented in MATLAB with a comparison to the traditional DPC approach and some related works in terms of response time, jitter, steady-state error, and overshoot. Simulations under different wind conditions are performed to evaluate the designed strategy's performance and robustness against conventional methods, revealing substantial improvements in dynamic response and stability. The results show the superior dynamic performance of the developed algorithm in terms of enhancing the quality of active power (37.99%, 55.04%, and 44.44%) and reactive power (49.17%, 27.27%, and 30.87%) in the two tests compared to the DPC. This control method effectively reduces the THD by 42.35%, 41.25%, and 31.36% compared to the DPC, resulting in a more efficient and reliable wind energy conversion system. This research confirms the effectiveness and efficiency of the proposed approach in renewable energy applications. It promotes the most efficient and sustainable energy solutions, making it a promising solution in other industrial applications.

Validation of energy valley optimization for adaptive fuzzy logic controller of DFIG-based wind turbines

This study presents a novel optimization algorithm known as the Energy Valley Optimizer Approach (EVOA) designed to effectively develop six optimal adaptive fuzzy logic controllers (AFLCs) comprising 30 parameters for a grid-tied doubly fed induction generator (DFIG) utilized in wind power plants (WPP). The primary objective of implementing EVOA-based AFLCs is to maximize power extraction from the DFIG in wind energy applications while simultaneously improving dynamic response and minimizing errors during operation. The performance of the EVOA-based AFLCs is thoroughly investigated and benchmarked against alternative optimization techniques, specifically chaotic billiards optimization (C-BO), genetic algorithms (GA), and marine predator algorithm (MPA)-based optimal proportional-integral (PI) controllers. This comparative analysis is crucial in establishing the efficacy of the proposed method. To validate the proposed approach, experimental assessments are conducted using the DSpace DS1104 control board, allowing for real-time application of the control strategies. The results indicate that the EVOA-AFLCs outperform the C-BO-based AFLCs, GA-based AFLCs, and MPA-based optimal PIs in several key performance metrics. Notably, the EVOA-AFLCs exhibit rapid temporal response, a high rate of convergence, reduced peak overshoot, diminished undershoot, and significantly lower steady-state error. The EVOA-AFLC outperforms the C-BO-AFLC and GA-AFLC in terms of efficiency, transient responses, and oscillations. In comparison to the MPA-PI, it improves speed tracking by 86.3%, the GA-AFLC by 56.36%, and the C-BO by 39.3%. Moreover, integral absolute error (IAE) for each controller has been calculated to validate the system wind turbine performance. The EVOA-AFLC outperforms other approaches significantly, achieving a 71.2% reduction in average integral absolute errors compared to the GA-AFLC, 24.4% compared to the C-BO-AFLC, and an impressive 84% compared to the MPA-PI. These findings underscore the potential of the EVOA as a robust and effective optimization tool for enhancing the performance of adaptive fuzzy logic controllers in DFIG-based wind power systems.

Control approaches of power electronic converter interfacing grid-tied PMSG-VSWT system: A comprehensive review

The growing interest in wind power technology is motivating researchers and decision-makers to focus on maximizing wind energy extraction and enhancing the quality of power integrated into the grid. Over the past decades, significant advancements have been made in Wind Energy Conversion Systems (WECS), such as moving to variable speed wind turbines (VSWT), using various generator types, and interfacing with many power electronic converter topologies. Recently, the majority of wind turbine industries have adopted the VSWT, which is based on the permanent magnet synchronous generator (PMSG) and incorporates a fully controlled power electronic converter (FCPEC) topology due to its notable features of full controllability, ultimately enhancing the efficiency and power quality of the WECS. This paper presents a concise overview of the PMSG-VSWT system and comprehensively reviews the most recent control approaches developed for the FCPEC that play a crucial role in the operation and performance of the PMSG-VSWT system. The paper begins with a comprehensive review of the Maximum Power Extraction Algorithms (MPEA) used in the PMSG-VSWT system, as reported in esteemed research articles over recent years. It investigates the fundamental concepts of each MPEA, examining their advantages and disadvantages, providing critical comparisons, highlighting related work, and discussing the advancements achieved in this field. Subsequently, the paper reviews the prevalent control schemes for the Grid-Side Inverter and Machine-Side Rectifier (GSI/MSR) in the FCPEC. It covers common control approaches such as vector control, direct control, sliding mode control, and model productive control, including modern and intelligent techniques. Additionally, the paper details recent improvements and approaches adopted to address challenges in these common schemes, involving optimizing algorithms and adaptive techniques. The paper provides essential insights into trends, improvements, and challenges in the domain and acts as a crucial reference for researchers working with PMSG-VSWT systems.

Fault-tolerant optimal pitch control of wind turbines using dynamic weighted parallel firefly algorithm

With steadily increasing interest in utilizing wind turbine (WT) systems as primary electrical energy generators, fault-tolerance has been considered decisive to enhance their efficiency and reliability. In this work, an optimal fault-tolerant pitch control (FTPC) strategy is addressed to adjust the pitch angle of WT blades in the presence of sensor, actuator, and system faults. The proposed scheme incorporates a fractional-calculus based extended memory (EM) of pitch angles along with a fractional-order proportional-integral-derivative (FOPID) controller to enhance the performance of the WT. A dynamic weighted parallel firefly algorithm (DWPFA) is also proposed to tune the controller parameters. The efficiency of the proposed algorithm is evaluated on the test functions adopted from 2017 IEEE congress on evolutionary computation (CEC2017). The merits of the proposed fault-tolerant approach are tested on a 4.8-MW WT benchmark model and compared to conventional PI and optimal FOPID approaches. Corresponding comparative simulation results validate the effectiveness and fault-tolerant capability of the proposed control paradigm, where it is observed that the proposed control scheme tends to be more consistent in the power generated at a given wind speed.