An Improved Mechanical Sensorless Maximum Power Point Tracking Method for Permanent-Magnet Synchronous Generator-Based Small Wind Turbines Systems

Balbino, Anderson José; Nora, Bruno da Silva; Lazzarin, Telles Brunelli

doi:10.1109/tie.2021.3084176

Cited by 24 publications

(14 citation statements)

References 34 publications

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“…The previous research works that present MPPT algorithms for wind generation systems using PMSG have used techniques that require wind speed measurement. However, other methods estimate wind speed through electrical parameters on condition of the knowledge of the mechanical properties of wind turbines [33][34][35].…”

Section: Figure 1 Configuration Of a Wecs Based On Pmsgmentioning

confidence: 99%

Multiple Linear Regression Approach for Sensorless MPPT of PMSG Wind Power Generation Systems

Abo‐Khalil

Sobhy

Sayed

2022

Preprint

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In this paper, a novel approach for extracting the maximum power from a wind energy conversion system (WECS) eqquiped with a permanent magnet synchronous generator (PMSG) is presented. Wind power generation systems have gained prominence as an alternative to conventional power plants worldwide, being the renewable energy source with the best growth prospects. However, the intermittent nature of wind power reduces energy reliability and hinders the development of technologies in this area. Therefore, a sensorless controller for a WECS with a PMSG is developed to enhance the conversion efficiency of the wind enegy system. The proposed control algorithm predicts the wind speed from the wind turbine power-speed characteristics. It uses the predicted speed to determine the optimum reference rotating speed to extract the maximum power point (MPPT) based on the optimum tip-speed ratio. The proposed control approach is based on a multiple linear regression (MLR) algorithms which is used to predict the wind speed value by calculating the regression coefficients from the predetermined training samples of the turbine power, rotating speed, and wind speed. The MPPT operating mode is maintained as long as the available power is less than the rated power of the converter. Otherwise, the MPPT algorithm is disabled, and a mechanical power control loop guarantees nominal power. A complete theoretical analysis and the experimental results considering the prototype with the nominal power of the 3 kW wind turbine used are presented. The experimental results has proven the effectiveness and robustness of the proposed control approach.

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Section: Figure 1 Configuration Of a Wecs Based On Pmsgmentioning

confidence: 99%

Multiple Linear Regression Approach for Sensorless MPPT of PMSG Wind Power Generation Systems

Abo‐Khalil

Sobhy

Sayed

2022

Preprint

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“…Pre and post multiplying (16) with N T and N; (17) with diag{N T , N T , I} and diag{N, N, I}, which yield ( 25) and ( 26). This completes the proof.…”

Section: Control Design With Known Actuator Faultmentioning

confidence: 99%

“…under the zero initial condition for any nonzero d(t) ∈  2 [0, +∞). Besides that when d(t) = 0, it follows from (17), it is easily obtain the asymptotic stability condition for error dynamics of PMSG-based WECS without the load torque. Based on the Problem 1, the proof is completed.…”

Section: Switched Fault-tolerant Tracking Controlmentioning

confidence: 99%

“…Furthermore, the utility of controlling PMSG‐based WECS is to stabilize the system and receive maximum output power at various wind levels. In this situation, the maximum power point tracking (MPPT) control scheme has provoked widespread interest from the researchers 15‐17 . Accordingly, tracking the maximum power point of a PMSG‐based WECS is significant since its locus varies over a wide range based on the variable‐speed wind.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy‐dependent switched fault‐tolerant tracking control for permanent magnet synchronous generator‐based wind energy conversion system

Subramanian

Joo

2022

Adaptive Control & Signal

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This article investigates the switched fault-tolerant tracking control techniques for a permanent magnet synchronous generator (PMSG)-based variable-speed wind energy conversion system (WECS) subject to actuator faults. To do this, a Takagi-Sugeno fuzzy model is exploited to delineate the nonlinear WECS composed of a PMSG and wind turbine. By using the switching technique and Lyapunov functional including membership function information, fault-tolerant tracking control is proposed to effectively track the optimal trajectory of PMSG-based WECS states with known and unknown actuator faults. Also, the stabilization conditions are derived in the form of linear matrix inequality to determine the control gains, which assures the maximum power output and enhance the system performance. Meanwhile, the H ∞ performance index is presented with membership function, for attenuating the load torque disturbance of WECS. Finally, the superiority and benefit of presented method are validated by numerical simulations.

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“…Moreover, in the literature, several algorithms for tracking the maximum power in wind systems have been presented, including using a radial-basis function via the neural network control strategy [20], grasshopper-optimization algorithm [21], powercapture optimization [22], perturb-and-observe-based higher-order sliding-mode controller [23], Archimedes optimization algorithm [24], mechanical-senseless method [25], neuro-adaptive generalized global sliding-mode controller [26], artificial intelligence-based adaptive perturb-and-observe controller [27], and MPPT based on integrated generatorrectifier systems [28].…”

Section: Introductionmentioning

confidence: 99%

African Vulture Optimization Algorithm-Based PI Controllers for Performance Enhancement of Hybrid Renewable-Energy Systems

et al. 2022

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An effective maximum power point tracking (MPPT) technique plays a crucial role in improving the efficiency and performance of grid-connected renewable energy sources (RESs). This paper uses the African Vulture Optimization Algorithm (AVOA), a metaheuristic technique inspired by nature, to tune the proportional–integral (PI)-based MPPT controllers for hybrid RESs of solar photovoltaic (PV) and wind systems, as well as the PI controllers in a storage system that are used to smooth the output fluctuations of those RESs in a hybrid system. The performance of the AVOA is compared with that of the widely used the particle swarm optimization (PSO) technique, which is commonly acknowledged as the foundation of swarm intelligence. As a result, this technique is introduced in this study to draw a comparison. It is observed that the proposed algorithm outperformed the PSO algorithm in terms of the tracking speed, robustness, and best convergence to the minimum value. A MATLAB/Simulink model was built, and optimization and simulation for the proposed system were carried out to verify the introduced algorithms. In conclusion, the optimization and simulation results showed that the AVOA is a promising method for solving a variety of engineering problems.

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An Improved Mechanical Sensorless Maximum Power Point Tracking Method for Permanent-Magnet Synchronous Generator-Based Small Wind Turbines Systems

Cited by 24 publications

References 34 publications

Multiple Linear Regression Approach for Sensorless MPPT of PMSG Wind Power Generation Systems

Multiple Linear Regression Approach for Sensorless MPPT of PMSG Wind Power Generation Systems

Fuzzy‐dependent switched fault‐tolerant tracking control for permanent magnet synchronous generator‐based wind energy conversion system

African Vulture Optimization Algorithm-Based PI Controllers for Performance Enhancement of Hybrid Renewable-Energy Systems

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