Robust DTC Based on Adaptive Fuzzy Control of Double Star Synchronous Machine Drive with Fixed Switching Frequency

Boudana, D.; Nezli, L.; Tlemçani, Abdelhalim; Mahmoudi, M.O.; Tadjine, Mohamed

doi:10.2478/v10187-012-0021-y

Cited by 7 publications

(7 citation statements)

References 17 publications

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“…The application of the ANNs attracts the attention of many scientists from all over the world [20]. This strategy has many advantages, it is a simple control, easy to implement, the possibility of approximating non-linear function, insensitivity to the distortion of the network [21].…”

Section: Nsosmc Controlmentioning

confidence: 99%

Neuro-scond Order Sliding Mode Field Oriented Command for DFIG Based Wind Turbine

Benbouhenni¹

2018

ijSmartGrid

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In this paper, we present a comparative study between second order sliding mode control (SOSMC) with two-level pulse width modulation (2L-PWM) and neuro-second order sliding mode control (NSOSMC) with two-level fuzzy space vector modulation (2L-FSVM) of reactive stator power and active stator power control of the doubly fed induction generator (DFIG) for wind energy conversion systems (WECSs). The validity of the proposed controls techniques is verified by simulation tests of the DFIG-based wind turbine systems (WTSs). The total harmonic distortion (THD) of rotor current, reactive stator power, electromagnetic torque and active stator power is determined and compared in the above strategies. The obtained results showed that the proposed NSOSMC control with 2L-NSVM technique have electromagnetic torque, reactive stator power and active power with low ripples and low rotor current harmonic distortion than SOSMC control scheme with conventional PWM technique..

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Section: Nsosmc Controlmentioning

confidence: 99%

Neuro-scond Order Sliding Mode Field Oriented Command for DFIG Based Wind Turbine

Benbouhenni¹

2018

ijSmartGrid

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“…It was reported that ANN, SVM, and ANFIS have some advantages in predicting soil parameters. For example, ANN has a simple architecture and is easy in training and generalization; it can solve nonlinear problems with high accuracy [30][31][32][33]. Regarding SVMs, it was reported that they have some advantages such as the ability to provide good out-of-sample generalization and to be robust even when the training sample has some bias [34].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Gaussian Process and M5P for Prediction of Soil Permeability Coefficient

Pham

Al‐Ansari

et al. 2021

Scientific Programming

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The permeability coefficient (k) of soil is one of the most important parameters affecting soil characteristics such as shear strength or settlement. Thus, determining soil permeability coefficient is very crucial; however, a field test for determining this parameter is difficult, time-consuming, and expensive. In this study, soft computing methods, namely, M5P and Gaussian process (GP), for estimating the permeability coefficient were constructed and compared. The results of this paper indicate that the two soft computing algorithms functioned well in predicting k. These two methods gave high accuracy of prediction capability. The determination coefficient of M5P (R2 = 0.766) was higher than that (R2 = 0.700) of GP. This implies that the M5P model is more reliable estimation than the GP model in predicting soils’ permeability coefficient (k). This proves that applying these machine learning techniques can provide an alternative for predicting basic soil parameters, including the permeability coefficient of soil.

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“…Wind power represents a renewable and carbon-free energy resource, which can be made available on a large scale by wind energy conversion systems (WECSs). During the last two decades, electricity generation by wind power experienced a vast expansion leading to a global cumulative installed generation capacity [1][2].…”

Section: Introductionmentioning

confidence: 99%

“…The progress achieved in the domain of the power electronics permitted to construct some static converters at variable frequency. For high powers, the use of the synchronous machines associated multilevel inverters especially finds its application in the naval propulsion, electric traction and the renewable energy conversion [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Sliding Mode Control of Wind Energy Conversion System Using Dual Star Synchronous Machine and Three Level Converter

Berkani¹,

Negadi²,

Allaoui³

et al. 2019

IJES

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This paper investigates a dual star synchronous machine powered by two independent threelevel voltage source inverters integrated in a wind turbine system (Figure 1). The dual star synchronous machine is used in areas of high power industrial applications such as naval propulsion, traction systems and renewable energy. This use is motivated by several important advantages compared to classical three-phase machine. In this work, the machine side converters control the dq component current by a conventional method based PI regulator, and then the load side converters control the DC bus voltage using sliding mode approach and the RMS voltage load. The validity of the proposed control technique is verified by Matlab/Simulink. Simulation results presented in this paper confirm the validity and feasibility of the proposed control approach, and can be tested on experimental setup.

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Robust DTC Based on Adaptive Fuzzy Control of Double Star Synchronous Machine Drive with Fixed Switching Frequency

Cited by 7 publications

References 17 publications

Neuro-scond Order Sliding Mode Field Oriented Command for DFIG Based Wind Turbine

Neuro-scond Order Sliding Mode Field Oriented Command for DFIG Based Wind Turbine

A Comparison of Gaussian Process and M5P for Prediction of Soil Permeability Coefficient

Sliding Mode Control of Wind Energy Conversion System Using Dual Star Synchronous Machine and Three Level Converter

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