Sliding Mode Control of a Variable- Speed Wind Energy Conversion System Using a Squirrel Cage Induction Generator

Zribi, Mohamed; Alrifai, Muthana T.; Rayan, Mohamed

doi:10.3390/en10050604

Cited by 49 publications

(22 citation statements)

References 28 publications

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“…First, we consider a classical PID controller. As already mentioned in Chapter 1, an induction generator (integrated into a wind energy conversion system) is a nonlinear process, described by a seventh order nonlinear model (Park's equations) [20][21][22][23][24]. The control of a nonlinear plants using a linear controller is a difficult task (and a classical PID controller is a linear controller).…”

Section: Appl Sci 2019 9 X For Peer Review 10 Of 21mentioning

confidence: 99%

“…In order to validate the proposed control strategy, an induction generator connected to a power system was considered as a controlled plant. An induction generator is a highly nonlinear plant (described by a mathematical model of the seventh order) and its operating point is frequently changing under the action of internal or external disturbances [20][21][22][23][24]. In this case, an electrical load/unload is considered one of the main disturbances whose effect must be rejected by the system.…”

Section: Introductionmentioning

confidence: 99%

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Minimum-Variance Control System with Variable Control Penalty Factor

et al. 2020

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The present paper proposes (as the main contribution) an additional self-tuning mechanism for an adaptive minimum-variance control system, whose main goal is to extend its functionality for a large value range of unmeasurable perturbations which disturb the controlled process. Through the standard design procedure, a minimum variance controller uses by default an internal self-tuning mechanism based on the process parameter estimates. However, the main parameter which overwhelmingly influences the control performance is the control penalty factor ( ρ ) . This parameter weights the term that describes the control variance in a criterion function whose minimization is the starting point of the control law design. The classical minimum-variance control involves an off-line tuning of this parameter, its value being set as constant throughout the entire operating regime. Based on the measurement of the process output error, the contribution of the proposed strategy consists in a real-time tuning of the control penalty factor, to ensure the stability of the control system, even under conditions of high disturbances. The proposed tuning mechanism adjusts this parameter by implementing a bipositional switching strategy based on a sharp hysteresis loop. Therefore, instead of the standard solution that involves a constant value of the control penalty factor ρ (a priori computed and set), this paper proposes a dual value for this controller parameter. The main objective is to allow the controlled process to operate in a stable fashion even in more strongly disturbed regimes (regimes where the control system becomes unstable and is usually switched off for safety reasons). To validate the proposed strategy, an induction generator integrated into a wind energy conversion system was considered as controlled plant. Operating under the action of strong disturbances (wind gusts, electrical load variations), the extension of safe operating range (thus avoiding the system disengagement) is an important goal of such a control system.

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Section: Appl Sci 2019 9 X For Peer Review 10 Of 21mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Minimum-Variance Control System with Variable Control Penalty Factor

et al. 2020

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“…Moreover, these techniques require more time (one cycle) than time-domain methods to calculate Fourier coefficients, reference currents, and voltage signals for the sampling of variable coefficients. Meanwhile, time-domain techniques use instantaneous p-q theory [160], synchronous d-q reference frame theorem [161], synchronous detection theorem [120,162], constant power factor algorithm [163], PI controller [156], fuzzy controller [164], hysteresis controller [165], and sliding-mode controller [166]. Methods such as dividing frequency control [167] and extension p-q theorem [168] are implemented to generate the reference signals, which implement the p-q technique.…”

Section: Apfs/statcom Control Techniquesmentioning

confidence: 99%

Mitigation of Power Quality Issues Due to High Penetration of Renewable Energy Sources in Electric Grid Systems Using Three-Phase APF/STATCOM Technologies: A Review

et al. 2018

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Abstract:This study summarizes an analytical review on the comparison of three-phase static compensator (STATCOM) and active power filter (APF) inverter topologies and their control schemes using industrial standards and advanced high-power configurations. Transformerless and reduced switch count topologies are the leading technologies in power electronics that aim to reduce system cost and offer the additional benefits of small volumetric size, lightweight and compact structure, and high reliability. A detailed comparison of the topologies, control strategies and implementation structures of grid-connected high-power converters is presented. However, reducing the number of power semiconductor devices, sensors, and control circuits requires complex control strategies. This study focuses on different topological devices, namely, passive filters, shunt and hybrid filters, and STATCOMs, which are typically used for power quality improvement. Additionally, appropriate control schemes, such as sinusoidal pulse width modulation (SPWM) and space vector PWM techniques, are selected. According to recent developments in shunt APF/STATCOM inverters, simulation and experimental results prove the effectiveness of APF/STATCOM systems for harmonic mitigation based on the defined limit in IEEE-519.

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“…The study conducted in [12] dealt with the load angle estimation by using dynamic ANN with the following inputs: active power, reactive power, field current, and terminal voltage at the current and the previous time instant. The performance of the ANN based load angle estimation generally depends on the training process and the quality of the obtained training data, which represents a significant drawback of this type of estimation methods, as such amount of measurements that would include all operating states of the synchronous generator is hardly feasible for practical applications.In recent years, sliding mode observers (SMOs) [13][14][15][16][17] and sliding mode control (SMC) [18][19][20][21][22][23] have gained much research interest in the field of state estimation and control of electrical drives, including permanent magnet synchronous machines (PMSMs) and induction machines (IMs). The main reasons for increasing interest in application of SMOs in the field of electrical drives are their attractive features, such as simple implementation, strong robustness, order reduction, and disturbance rejection [24].…”

mentioning

confidence: 99%

“…In recent years, sliding mode observers (SMOs) [13][14][15][16][17] and sliding mode control (SMC) [18][19][20][21][22][23] have gained much research interest in the field of state estimation and control of electrical drives, including permanent magnet synchronous machines (PMSMs) and induction machines (IMs). The main reasons for increasing interest in application of SMOs in the field of electrical drives are their attractive features, such as simple implementation, strong robustness, order reduction, and disturbance rejection [24].…”

mentioning

confidence: 99%

Sliding Mode Observer-Based Load Angle Estimation for Salient-Pole Wound Rotor Synchronous Generators

Lopac

Bulić

Vrkic³

2019

Energies

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Synchronous generator load angle is a fundamental quantity for power system stability assessment, with possible real-time applications in protection and excitation control systems. Commonly used methods of load angle determination require additional measuring equipment, while existing research on load angle estimation for wound rotor synchronous generator has been limited to the estimator based on the generator’s phasor diagram and estimators based on artificial neural networks. In this paper, a load angle estimator for salient-pole wound rotor synchronous generator, based on a simple sliding mode observer (SMO) which utilizes field current, stator voltages, and stator currents measurements, is proposed. The conventional SMO structure is improved with use of hyperbolic tangent sigmoid functions, implementation of the second order low-pass filters accompanied with corresponding phase delay compensation, and introduction of an adaptive observer gain proportional to the measured field current value. Several case studies conducted on a generator connected to a power system suggest that the proposed estimator provides an adequate accuracy during active and reactive power disturbances during stable generator operation, outperforming the classical phasor diagram-based estimator by reducing mean squared error by up to 14.10%, mean absolute error by up to 41.55%, and maximum absolute error by up to 8.81%.

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Sliding Mode Control of a Variable- Speed Wind Energy Conversion System Using a Squirrel Cage Induction Generator

Cited by 49 publications

References 28 publications

Minimum-Variance Control System with Variable Control Penalty Factor

Minimum-Variance Control System with Variable Control Penalty Factor

Mitigation of Power Quality Issues Due to High Penetration of Renewable Energy Sources in Electric Grid Systems Using Three-Phase APF/STATCOM Technologies: A Review

Sliding Mode Observer-Based Load Angle Estimation for Salient-Pole Wound Rotor Synchronous Generators

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